CRAN Package Check Results for Package psycho

Last updated on 2018-04-29 01:47:16 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.1.4 11.76 218.47 230.23 ERROR
r-devel-linux-x86_64-debian-gcc 0.1.4 8.39 187.81 196.20 ERROR
r-devel-linux-x86_64-fedora-clang 0.1.4 285.48 ERROR
r-devel-linux-x86_64-fedora-gcc 0.1.4 276.47 ERROR
r-devel-windows-ix86+x86_64 0.1.4 27.00 297.00 324.00 ERROR
r-patched-linux-x86_64 0.1.4 11.25 210.94 222.19 ERROR
r-patched-solaris-x86 0.1.4 398.30 ERROR
r-release-linux-x86_64 0.1.4 12.05 212.20 224.25 ERROR
r-release-windows-ix86+x86_64 0.1.4 27.00 297.00 324.00 ERROR
r-release-osx-x86_64 0.1.4 ERROR
r-oldrel-windows-ix86+x86_64 0.1.4 18.00 228.00 246.00 ERROR
r-oldrel-osx-x86_64 0.1.4 OK

Check Details

Version: 0.1.4
Check: dependencies in R code
Result: NOTE
    Missing or unexported object: ‘lmerTest::summary’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64

Version: 0.1.4
Check: examples
Result: ERROR
    Running examples in ‘psycho-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: analyze.glmerMod
    > ### Title: Analyze glmerMod objects.
    > ### Aliases: analyze.glmerMod
    >
    > ### ** Examples
    >
    > library(psycho)
    > require(lme4)
    Loading required package: lme4
    Loading required package: Matrix
    > fit <- lme4::glmer(vs ~ mpg + (1|cyl), data=mtcars, family="binomial")
    >
    > results <- analyze(fit)
    The result is correct only if all data used by the model has not changed since model was fitted.
    The result is correct only if all data used by the model has not changed since model was fitted.
    Error: 'summary' is not an exported object from 'namespace:lmerTest'
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 0.1.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [58s/79s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     ── 1. Error: If it works. (@test-analyze.glmerMod.R#9) ────────────────────────
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     ── 2. Error: If it works. (@test-analyze.merMod.R#9) ──────────────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
     ── 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ──────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 0.000214 seconds
     1000 transitions using 10 leapfrog steps per transition would take 2.14 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.69001 seconds (Warm-up)
     0.606782 seconds (Sampling)
     1.29679 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.747372 seconds (Warm-up)
     0.53832 seconds (Sampling)
     1.28569 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.719152 seconds (Warm-up)
     0.510024 seconds (Sampling)
     1.22918 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 4.2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.727539 seconds (Warm-up)
     0.535079 seconds (Sampling)
     1.26262 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 0.000114 seconds
     1000 transitions using 10 leapfrog steps per transition would take 1.14 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 4.01696 seconds (Warm-up)
     4.32594 seconds (Sampling)
     8.3429 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 5.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.57 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 4.35034 seconds (Warm-up)
     3.93989 seconds (Sampling)
     8.29023 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 7.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.73 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 3.98162 seconds (Warm-up)
     3.8097 seconds (Sampling)
     7.79133 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 5.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.56 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 4.39776 seconds (Warm-up)
     3.0362 seconds (Sampling)
     7.43396 seconds (Total)
    
     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
    
     Gradient evaluation took 5.2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.145026 seconds (Warm-up)
     0.158448 seconds (Sampling)
     0.303474 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
     Gradient evaluation took 3.2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.32 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.146017 seconds (Warm-up)
     0.171495 seconds (Sampling)
     0.317512 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.138153 seconds (Warm-up)
     0.139424 seconds (Sampling)
     0.277577 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 2.8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.134632 seconds (Warm-up)
     0.130891 seconds (Sampling)
     0.265523 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 5.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.57 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.118728 seconds (Warm-up)
     0.117526 seconds (Sampling)
     0.236254 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 2.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.117583 seconds (Warm-up)
     0.106257 seconds (Sampling)
     0.22384 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 2.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.125067 seconds (Warm-up)
     0.125156 seconds (Sampling)
     0.250223 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 2.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.14301 seconds (Warm-up)
     0.123831 seconds (Sampling)
     0.266841 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 8.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.327643 seconds (Warm-up)
     0.305436 seconds (Sampling)
     0.633079 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 4.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.318107 seconds (Warm-up)
     0.321424 seconds (Sampling)
     0.639531 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 3.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.327015 seconds (Warm-up)
     0.300459 seconds (Sampling)
     0.627474 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 4.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.295669 seconds (Warm-up)
     0.305562 seconds (Sampling)
     0.601231 seconds (Total)
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06<b0>).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 57 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.1.4
Check: re-building of vignette outputs
Result: WARN
    Error in re-building vignettes:
     ...
    ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
    ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
    ✔ tibble 1.4.2 ✔ dplyr 0.7.4
    ✔ tidyr 0.8.0 ✔ stringr 1.3.0
    ✔ readr 1.1.1 ✔ forcats 0.3.0
    ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Loading required package: Rcpp
    rstanarm (Version 2.17.4, packaged: 2018-04-13 01:51:52 UTC)
    - Do not expect the default priors to remain the same in future rstanarm versions.
    Thus, R scripts should specify priors explicitly, even if they are just the defaults.
    - For execution on a local, multicore CPU with excess RAM we recommend calling
    options(mc.cores = parallel::detectCores())
    - Plotting theme set to bayesplot::theme_default().
    Scale for 'fill' is already present. Adding another scale for 'fill',
    which will replace the existing scale.
    Loading required package: Matrix
    
    Attaching package: 'Matrix'
    
    The following object is masked from 'package:tidyr':
    
     expand
    
    Quitting from lines 398-402 (overview.Rmd)
    Error: processing vignette ‘overview.Rmd’ failed with diagnostics:
    object of type ‘closure’ is not subsettable
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 0.1.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [39s/55s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     ── 1. Error: If it works. (@test-analyze.glmerMod.R#9) ────────────────────────
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     ── 2. Error: If it works. (@test-analyze.merMod.R#9) ──────────────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
     ── 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ──────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 3.8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.38 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.415205 seconds (Warm-up)
     0.36948 seconds (Sampling)
     0.784685 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 2.2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.439898 seconds (Warm-up)
     0.32475 seconds (Sampling)
     0.764648 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.451916 seconds (Warm-up)
     0.314049 seconds (Sampling)
     0.765965 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 2.2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.444024 seconds (Warm-up)
     0.327099 seconds (Sampling)
     0.771123 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 7.9e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.79 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 2.4965 seconds (Warm-up)
     2.65427 seconds (Sampling)
     5.15077 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 3.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 2.61568 seconds (Warm-up)
     2.50356 seconds (Sampling)
     5.11924 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 4.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 2.56474 seconds (Warm-up)
     2.43994 seconds (Sampling)
     5.00468 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 6.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.64 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 2.70922 seconds (Warm-up)
     1.82077 seconds (Sampling)
     4.52999 seconds (Total)
    
     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
    
     Gradient evaluation took 4.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.086802 seconds (Warm-up)
     0.0923 seconds (Sampling)
     0.179102 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
     Gradient evaluation took 2.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.086445 seconds (Warm-up)
     0.104582 seconds (Sampling)
     0.191027 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 2.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.084476 seconds (Warm-up)
     0.085093 seconds (Sampling)
     0.169569 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 2.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.08126 seconds (Warm-up)
     0.077706 seconds (Sampling)
     0.158966 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 4.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.06623 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 2.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.067332 seconds (Warm-up)
     0.060334 seconds (Sampling)
     0.127666 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 1.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.064826 seconds (Warm-up)
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     0.12752 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.079024 seconds (Warm-up)
     0.068151 seconds (Sampling)
     0.147175 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 5.8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.58 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.196305 seconds (Warm-up)
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     0.398778 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 3.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.206346 seconds (Warm-up)
     0.209626 seconds (Sampling)
     0.415972 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 3.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.206263 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 5.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.57 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.190704 seconds (Warm-up)
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     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06<b0>).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 57 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.1.4
Check: examples
Result: ERROR
    Running examples in ‘psycho-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: analyze.glmerMod
    > ### Title: Analyze glmerMod objects.
    > ### Aliases: analyze.glmerMod
    >
    > ### ** Examples
    >
    > library(psycho)
    > require(lme4)
    Loading required package: lme4
    Loading required package: Matrix
    > fit <- lme4::glmer(vs ~ mpg + (1|cyl), data=mtcars, family="binomial")
    >
    > results <- analyze(fit)
    The result is correct only if all data used by the model has not changed since model was fitted.
    The result is correct only if all data used by the model has not changed since model was fitted.
    Error: 'summary' is not an exported object from 'namespace:lmerTest'
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64

Version: 0.1.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [73s/85s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     ── 1. Error: If it works. (@test-analyze.glmerMod.R#9) ────────────────────────
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     ── 2. Error: If it works. (@test-analyze.merMod.R#9) ──────────────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
     ── 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ──────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 0.000285 seconds
     1000 transitions using 10 leapfrog steps per transition would take 2.85 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.876947 seconds (Warm-up)
     0.802117 seconds (Sampling)
     1.67906 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.924802 seconds (Warm-up)
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     1.60557 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 5.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.54 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.951925 seconds (Warm-up)
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     1.62223 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 4.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.974115 seconds (Warm-up)
     0.721872 seconds (Sampling)
     1.69599 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 0.000172 seconds
     1000 transitions using 10 leapfrog steps per transition would take 1.72 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 5.23171 seconds (Warm-up)
     5.60099 seconds (Sampling)
     10.8327 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 0.000125 seconds
     1000 transitions using 10 leapfrog steps per transition would take 1.25 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 5.63815 seconds (Warm-up)
     4.81202 seconds (Sampling)
     10.4502 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 7.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.76 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 5.20246 seconds (Warm-up)
     5.09156 seconds (Sampling)
     10.294 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 9.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.93 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 5.92425 seconds (Warm-up)
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     9.92005 seconds (Total)
    
     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
    
     Gradient evaluation took 8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.8 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.199739 seconds (Warm-up)
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     0.413268 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
     Gradient evaluation took 4.8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.189145 seconds (Warm-up)
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     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 5.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.55 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.179524 seconds (Warm-up)
     0.163234 seconds (Sampling)
     0.342758 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 3.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.177323 seconds (Warm-up)
     0.172245 seconds (Sampling)
     0.349568 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 6.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.66 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.137202 seconds (Warm-up)
     0.130435 seconds (Sampling)
     0.267637 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 3.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.140909 seconds (Warm-up)
     0.123465 seconds (Sampling)
     0.264374 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 2.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.117741 seconds (Warm-up)
     0.125978 seconds (Sampling)
     0.243719 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 7.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.74 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.165594 seconds (Warm-up)
     0.143793 seconds (Sampling)
     0.309387 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 0.0001 seconds
     1000 transitions using 10 leapfrog steps per transition would take 1 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.395384 seconds (Warm-up)
     0.399208 seconds (Sampling)
     0.794592 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 5.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.42646 seconds (Warm-up)
     0.410978 seconds (Sampling)
     0.837438 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 4.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.398542 seconds (Warm-up)
     0.344937 seconds (Sampling)
     0.743479 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 5.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.333663 seconds (Warm-up)
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     0.700862 seconds (Total)
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06<b0>).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 56 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [67s/77s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     ── 1. Error: If it works. (@test-analyze.glmerMod.R#9) ────────────────────────
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     ── 2. Error: If it works. (@test-analyze.merMod.R#9) ──────────────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
     ── 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ──────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 9e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.837887 seconds (Warm-up)
     0.759061 seconds (Sampling)
     1.59695 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 4.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.87508 seconds (Warm-up)
     0.668918 seconds (Sampling)
     1.544 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 5.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.55 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.861534 seconds (Warm-up)
     0.593656 seconds (Sampling)
     1.45519 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 5.2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.857146 seconds (Warm-up)
     0.635522 seconds (Sampling)
     1.49267 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 0.00017 seconds
     1000 transitions using 10 leapfrog steps per transition would take 1.7 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 4.93103 seconds (Warm-up)
     5.09246 seconds (Sampling)
     10.0235 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 6.8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 5.0656 seconds (Warm-up)
     4.73776 seconds (Sampling)
     9.80337 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 6.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.67 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 4.73958 seconds (Warm-up)
     4.69862 seconds (Sampling)
     9.43821 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 5.36042 seconds (Warm-up)
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     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
    
     Gradient evaluation took 6.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.66 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.186349 seconds (Warm-up)
     0.183884 seconds (Sampling)
     0.370233 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
     Gradient evaluation took 4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.167595 seconds (Warm-up)
     0.197617 seconds (Sampling)
     0.365212 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 3.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.156279 seconds (Warm-up)
     0.174136 seconds (Sampling)
     0.330415 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 3.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.167638 seconds (Warm-up)
     0.164993 seconds (Sampling)
     0.332631 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 6.2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.62 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.1327 seconds (Warm-up)
     0.12877 seconds (Sampling)
     0.26147 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 2.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.136006 seconds (Warm-up)
     0.130504 seconds (Sampling)
     0.26651 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 3.8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.38 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.130114 seconds (Warm-up)
     0.134641 seconds (Sampling)
     0.264755 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.15113 seconds (Warm-up)
     0.128563 seconds (Sampling)
     0.279693 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 8.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.358486 seconds (Warm-up)
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     0.690581 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 4.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.34082 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 4.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.346676 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 4.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.325412 seconds (Warm-up)
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     0.680618 seconds (Total)
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06<b0>).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 56 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.1.4
Check: tests
Result: ERROR
     Running 'testthat.R' [63s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     -- 1. Error: If it works. (@test-analyze.glmerMod.R#9) ------------------------
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     -- 2. Error: If it works. (@test-analyze.merMod.R#9) --------------------------
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
     -- 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ------------------
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.823 seconds (Warm-up)
     0.608 seconds (Sampling)
     1.431 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.936 seconds (Warm-up)
     0.783 seconds (Sampling)
     1.719 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.955 seconds (Warm-up)
     0.764 seconds (Sampling)
     1.719 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.889 seconds (Warm-up)
     0.655 seconds (Sampling)
     1.544 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 3.635 seconds (Warm-up)
     3.198 seconds (Sampling)
     6.833 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 3.666 seconds (Warm-up)
     4.243 seconds (Sampling)
     7.909 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 0 seconds
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     Elapsed Time: 4.119 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
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     Elapsed Time: 3.931 seconds (Warm-up)
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     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
    
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     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
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     Elapsed Time: 0.187 seconds (Warm-up)
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     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
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     Elapsed Time: 0.172 seconds (Warm-up)
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     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
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     Elapsed Time: 0.172 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
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     Elapsed Time: 0.11 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 0.001 seconds
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     Elapsed Time: 0.135 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
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     Elapsed Time: 0.125 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
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     Elapsed Time: 0.297 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
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     Elapsed Time: 0.28 seconds (Warm-up)
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     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06°).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     == testthat results ===========================================================
     OK: 56 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64

Version: 0.1.4
Check: re-building of vignette outputs
Result: WARN
    Error in re-building vignettes:
     ...
    -- Attaching packages ---------------------------------- tidyverse 1.2.1 --
    v ggplot2 2.2.1 v purrr 0.2.4
    v tibble 1.4.2 v dplyr 0.7.4
    v tidyr 0.8.0 v stringr 1.3.0
    v readr 1.1.1 v forcats 0.3.0
    -- Conflicts ------------------------------------- tidyverse_conflicts() --
    x dplyr::filter() masks stats::filter()
    x dplyr::lag() masks stats::lag()
    Loading required package: Rcpp
    rstanarm (Version 2.17.4, packaged: 2018-04-13 01:51:52 UTC)
    - Do not expect the default priors to remain the same in future rstanarm versions.
    Thus, R scripts should specify priors explicitly, even if they are just the defaults.
    - For execution on a local, multicore CPU with excess RAM we recommend calling
    options(mc.cores = parallel::detectCores())
    - Plotting theme set to bayesplot::theme_default().
    Scale for 'fill' is already present. Adding another scale for 'fill',
    which will replace the existing scale.
    Loading required package: Matrix
    
    Attaching package: 'Matrix'
    
    The following object is masked from 'package:tidyr':
    
     expand
    
    Quitting from lines 398-402 (overview.Rmd)
    Error: processing vignette 'overview.Rmd' failed with diagnostics:
    object of type 'closure' is not subsettable
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 0.1.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [54s/69s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     ── 1. Error: If it works. (@test-analyze.glmerMod.R#9) ────────────────────────
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     ── 2. Error: If it works. (@test-analyze.merMod.R#9) ──────────────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
     ── 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ──────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 5.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.55 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.613439 seconds (Warm-up)
     0.552172 seconds (Sampling)
     1.16561 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 3.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.670258 seconds (Warm-up)
     0.48436 seconds (Sampling)
     1.15462 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 2.9e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.669972 seconds (Warm-up)
     0.470101 seconds (Sampling)
     1.14007 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.659815 seconds (Warm-up)
     0.501626 seconds (Sampling)
     1.16144 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 8.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 3.75978 seconds (Warm-up)
     3.98137 seconds (Sampling)
     7.74115 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 4.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 4.03682 seconds (Warm-up)
     3.62788 seconds (Sampling)
     7.66471 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 4.9e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 3.7854 seconds (Warm-up)
     3.65148 seconds (Sampling)
     7.43688 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 6.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.61 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 4.10329 seconds (Warm-up)
     2.75328 seconds (Sampling)
     6.85657 seconds (Total)
    
     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
    
     Gradient evaluation took 4.9e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.133514 seconds (Warm-up)
     0.138482 seconds (Sampling)
     0.271996 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
     Gradient evaluation took 3.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.12411 seconds (Warm-up)
     0.147794 seconds (Sampling)
     0.271904 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 2.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.130803 seconds (Warm-up)
     0.124318 seconds (Sampling)
     0.255121 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 2.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.124562 seconds (Warm-up)
     0.125686 seconds (Sampling)
     0.250248 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 5.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.111659 seconds (Warm-up)
     0.110389 seconds (Sampling)
     0.222048 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.110549 seconds (Warm-up)
     0.103451 seconds (Sampling)
     0.214 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.105115 seconds (Warm-up)
     0.115493 seconds (Sampling)
     0.220608 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.127711 seconds (Warm-up)
     0.104733 seconds (Sampling)
     0.232444 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 6.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.291848 seconds (Warm-up)
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     0.584953 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 3.9e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.301862 seconds (Warm-up)
     0.296192 seconds (Sampling)
     0.598054 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 4.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.315765 seconds (Warm-up)
     0.282753 seconds (Sampling)
     0.598518 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 3.3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.279411 seconds (Warm-up)
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     0.555474 seconds (Total)
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06<b0>).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 57 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-linux-x86_64

Version: 0.1.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [85s/85s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     ── 1. Error: If it works. (@test-analyze.glmerMod.R#9) ────────────────────────
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     ── 2. Error: If it works. (@test-analyze.merMod.R#9) ──────────────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
     ── 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ──────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 1.32 seconds (Warm-up)
     1.08 seconds (Sampling)
     2.4 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
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     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
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     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06<b0>).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 56 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-solaris-x86

Version: 0.1.4
Check: re-building of vignette outputs
Result: WARN
    Error in re-building vignettes:
     ...
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
    ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
    ✔ tibble 1.4.2 ✔ dplyr 0.7.4
    ✔ tidyr 0.8.0 ✔ stringr 1.3.0
    ✔ readr 1.1.1 ✔ forcats 0.3.0
    ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Loading required package: Rcpp
    rstanarm (Version 2.17.4, packaged: 2018-04-13 01:51:52 UTC)
    - Do not expect the default priors to remain the same in future rstanarm versions.
    Thus, R scripts should specify priors explicitly, even if they are just the defaults.
    - For execution on a local, multicore CPU with excess RAM we recommend calling
    options(mc.cores = parallel::detectCores())
    - Plotting theme set to bayesplot::theme_default().
    Scale for 'fill' is already present. Adding another scale for 'fill',
    which will replace the existing scale.
    Loading required package: Matrix
    
    Attaching package: 'Matrix'
    
    The following object is masked from 'package:tidyr':
    
     expand
    
    Quitting from lines 398-402 (overview.Rmd)
    Error: processing vignette 'overview.Rmd' failed with diagnostics:
    object of type 'closure' is not subsettable
    Execution halted
Flavor: r-patched-solaris-x86

Version: 0.1.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [53s/70s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     ── 1. Error: If it works. (@test-analyze.glmerMod.R#9) ────────────────────────
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     ── 2. Error: If it works. (@test-analyze.merMod.R#9) ──────────────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
     ── 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ──────────────────
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"),
     checkConv = list(check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL),
     check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(
     action = "warning", tol = 1e-06)), optCtrl = list()), class = c("lmerControl",
     "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: withCallingHandlers(tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir),
     error = function(e) {
     e$call <- cl.i
     stop(e)
     }), warning = function(w) {
     w$call <- cl.i
     w
     })
     16: tryCatch(if (missE) ...elt(i) else eval(cl.i, envir = envir), error = function(e) {
     e$call <- cl.i
     stop(e)
     })
     17: tryCatchList(expr, classes, parentenv, handlers)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: value[[3L]](cond)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 5.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.56 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.628104 seconds (Warm-up)
     0.555282 seconds (Sampling)
     1.18339 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
     Gradient evaluation took 2.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.674264 seconds (Warm-up)
     0.475336 seconds (Sampling)
     1.1496 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 2.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.677955 seconds (Warm-up)
     0.47118 seconds (Sampling)
     1.14914 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 3.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.664673 seconds (Warm-up)
     0.48188 seconds (Sampling)
     1.14655 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 7.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.77 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 3.74011 seconds (Warm-up)
     3.97581 seconds (Sampling)
     7.71593 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 4.2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 3.89025 seconds (Warm-up)
     3.29727 seconds (Sampling)
     7.18752 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 4.1e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 3.46515 seconds (Warm-up)
     3.56309 seconds (Sampling)
     7.02824 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 4.01905 seconds (Warm-up)
     2.74949 seconds (Sampling)
     6.76854 seconds (Total)
    
     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
    
     Gradient evaluation took 3.9e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.142582 seconds (Warm-up)
     0.140903 seconds (Sampling)
     0.283485 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
     Gradient evaluation took 2.6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.135744 seconds (Warm-up)
     0.163231 seconds (Sampling)
     0.298975 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
     Gradient evaluation took 3.7e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.135561 seconds (Warm-up)
     0.132967 seconds (Sampling)
     0.268528 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
     Gradient evaluation took 2.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.120335 seconds (Warm-up)
     0.111816 seconds (Sampling)
     0.232151 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 3.8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.38 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.101212 seconds (Warm-up)
     0.095193 seconds (Sampling)
     0.196405 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.101701 seconds (Warm-up)
     0.094913 seconds (Sampling)
     0.196614 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 1.8e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.096733 seconds (Warm-up)
     0.099103 seconds (Sampling)
     0.195836 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 2e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.117484 seconds (Warm-up)
     0.098758 seconds (Sampling)
     0.216242 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
     Gradient evaluation took 6e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.270042 seconds (Warm-up)
     0.272941 seconds (Sampling)
     0.542983 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 3e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.283671 seconds (Warm-up)
     0.300357 seconds (Sampling)
     0.584028 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
     Gradient evaluation took 4.4e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.294847 seconds (Warm-up)
     0.279498 seconds (Sampling)
     0.574345 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 3.5e-05 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.276652 seconds (Warm-up)
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     0.549499 seconds (Total)
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06<b0>).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 57 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-release-linux-x86_64

Version: 0.1.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [57s/89s]
    Running the tests in ‘tests/testthat.R’ failed.
    Last 13 lines of output:
     0.576833 seconds (Total)
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06<b0>).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 56 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-release-osx-x86_64

Version: 0.1.4
Check: re-building of vignette outputs
Result: WARN
    Error in re-building vignettes:
     ...
    ✔ readr 1.1.1 ✔ forcats 0.3.0
    ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Loading required package: Rcpp
    rstanarm (Version 2.17.4, packaged: 2018-04-13 01:51:52 UTC)
    - Do not expect the default priors to remain the same in future rstanarm versions.
    Thus, R scripts should specify priors explicitly, even if they are just the defaults.
    - For execution on a local, multicore CPU with excess RAM we recommend calling
    options(mc.cores = parallel::detectCores())
    - Plotting theme set to bayesplot::theme_default().
    Scale for 'fill' is already present. Adding another scale for 'fill',
    which will replace the existing scale.
    Loading required package: Matrix
    
    Attaching package: 'Matrix'
    
    The following object is masked from 'package:tidyr':
    
     expand
    
    Quitting from lines 398-402 (overview.Rmd)
    Error: processing vignette 'overview.Rmd' failed with diagnostics:
    object of type 'closure' is not subsettable
    Execution halted
Flavor: r-release-osx-x86_64

Version: 0.1.4
Check: tests
Result: ERROR
     Running 'testthat.R' [85s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(psycho)
     >
     > test_check("psycho")
     -- 1. Error: If it works. (@test-analyze.glmerMod.R#9) ------------------------
     'summary' is not an exported object from 'namespace:lmerTest'
     1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9
     2: analyze.glmerMod(fit)
     3: data.frame(lmerTest::summary(fit)$coefficients)
     4: lmerTest::summary
     5: getExportedValue(pkg, name)
     6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     -- 2. Error: If it works. (@test-analyze.merMod.R#9) --------------------------
     object of type 'closure' is not subsettable
     1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9
     2: analyze.merMod(x)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = structure(list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"), .Names = c("check.nobs.vs.rankZ",
     "check.nobs.vs.nlev", "check.nlev.gtreq.5", "check.nlev.gtr.1", "check.nobs.vs.nRE",
     "check.rankX", "check.scaleX", "check.formula.LHS")), checkConv = structure(list(
     check.conv.grad = structure(list(action = "warning", tol = 0.002, relTol = NULL), .Names = c("action",
     "tol", "relTol")), check.conv.singular = structure(list(action = "ignore",
     tol = 1e-04), .Names = c("action", "tol")), check.conv.hess = structure(list(
     action = "warning", tol = 1e-06), .Names = c("action", "tol"))), .Names = c("check.conv.grad",
     "check.conv.singular", "check.conv.hess")), optCtrl = list()), .Names = c("optimizer",
     "restart_edge", "boundary.tol", "calc.derivs", "use.last.params", "checkControl",
     "checkConv", "optCtrl"), class = c("lmerControl", "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: as.formula(formula, env = denv)
     16: formula(object, env = baseenv())
     17: formula.character(object, env = baseenv())
     18: formula(eval(parse(text = x, keep.source = FALSE)[[1L]]))
     19: formula.default(eval(parse(text = x, keep.source = FALSE)[[1L]]))
     20: notnull(x$formula)
    
     -- 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ------------------
     object of type 'closure' is not subsettable
     1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9
     2: analyze.merMod(fit)
     3: analyze(fit)
     4: analyze.merMod(fit)
     5: lmerTest::lmer(formula, data)
     6: eval.parent(mc)
     7: eval(expr, p)
     8: eval(expr, p)
     9: lme4::lmer(formula = formula, data = data)
     10: eval(mc, parent.frame(1L))
     11: eval(mc, parent.frame(1L))
     12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa",
     restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,
     checkControl = structure(list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",
     check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop",
     check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"), .Names = c("check.nobs.vs.rankZ",
     "check.nobs.vs.nlev", "check.nlev.gtreq.5", "check.nlev.gtr.1", "check.nobs.vs.nRE",
     "check.rankX", "check.scaleX", "check.formula.LHS")), checkConv = structure(list(
     check.conv.grad = structure(list(action = "warning", tol = 0.002, relTol = NULL), .Names = c("action",
     "tol", "relTol")), check.conv.singular = structure(list(action = "ignore",
     tol = 1e-04), .Names = c("action", "tol")), check.conv.hess = structure(list(
     action = "warning", tol = 1e-06), .Names = c("action", "tol"))), .Names = c("check.conv.grad",
     "check.conv.singular", "check.conv.hess")), optCtrl = list()), .Names = c("optimizer",
     "restart_edge", "boundary.tol", "calc.derivs", "use.last.params", "checkControl",
     "checkConv", "optCtrl"), class = c("lmerControl", "merControl")))
     13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop")
     14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3)
     15: as.formula(formula, env = denv)
     16: formula(object, env = baseenv())
     17: formula.character(object, env = baseenv())
     18: formula(eval(parse(text = x, keep.source = FALSE)[[1L]]))
     19: formula.default(eval(parse(text = x, keep.source = FALSE)[[1L]]))
     20: notnull(x$formula)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
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     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.842 seconds (Warm-up)
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     1.513 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
     Rejecting initial value:
     Log probability evaluates to log(0), i.e. negative infinity.
     Stan can't start sampling from this initial value.
    
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     Elapsed Time: 0.951 seconds (Warm-up)
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     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
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     Elapsed Time: 0.939 seconds (Warm-up)
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     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
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     Elapsed Time: 1.014 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
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     Elapsed Time: 3.929 seconds (Warm-up)
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     7.226 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
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     Elapsed Time: 3.828 seconds (Warm-up)
     4.621 seconds (Sampling)
     8.449 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
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     Elapsed Time: 4.14 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
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     Elapsed Time: 3.853 seconds (Warm-up)
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     7.246 seconds (Total)
    
     The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*).
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
    
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     Elapsed Time: 0.203 seconds (Warm-up)
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     0.405 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
    
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     Elapsed Time: 0.188 seconds (Warm-up)
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     0.406 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
    
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     Elapsed Time: 0.187 seconds (Warm-up)
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     0.359 seconds (Total)
    
    
     SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
    
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     Elapsed Time: 0.171 seconds (Warm-up)
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     0.343 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
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     Elapsed Time: 0.125 seconds (Warm-up)
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     0.234 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
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     Elapsed Time: 0.14 seconds (Warm-up)
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
     Gradient evaluation took 0 seconds
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     Adjust your expectations accordingly!
    
    
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     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
    
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     Adjust your expectations accordingly!
    
    
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     Elapsed Time: 0.265 seconds (Warm-up)
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     0.546 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
    
     Gradient evaluation took 0 seconds
     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
     Iteration: 1 / 2000 [ 0%] (Warmup)
     Iteration: 200 / 2000 [ 10%] (Warmup)
     Iteration: 400 / 2000 [ 20%] (Warmup)
     Iteration: 600 / 2000 [ 30%] (Warmup)
     Iteration: 800 / 2000 [ 40%] (Warmup)
     Iteration: 1000 / 2000 [ 50%] (Warmup)
     Iteration: 1001 / 2000 [ 50%] (Sampling)
     Iteration: 1200 / 2000 [ 60%] (Sampling)
     Iteration: 1400 / 2000 [ 70%] (Sampling)
     Iteration: 1600 / 2000 [ 80%] (Sampling)
     Iteration: 1800 / 2000 [ 90%] (Sampling)
     Iteration: 2000 / 2000 [100%] (Sampling)
    
     Elapsed Time: 0.234 seconds (Warm-up)
     0.265 seconds (Sampling)
     0.499 seconds (Total)
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06°).
    
     The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*).
    
     1
     == testthat results ===========================================================
     OK: 56 SKIPPED: 0 FAILED: 3
     1. Error: If it works. (@test-analyze.glmerMod.R#9)
     2. Error: If it works. (@test-analyze.merMod.R#9)
     3. Error: If it works. (@test-analyze.merModLmerTest.R#9)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-oldrel-windows-ix86+x86_64