`get_confidence_interval()`

now uses column names (‘lower_ci’ and ‘upper_ci’) in output that are consistent with other infer functionality (#317).

`get_confidence_interval()`

can now produce bias-corrected confidence intervals by setting`type = "bias-corrected"`

. Thanks to @davidbaniadam for the initial implementation (#237, #318)!

- Fix CRAN check failures related to long double errors.

- Warn the user when a p-value of 0 is reported (#257, #273)
- Added new vignettes:
`chi_squared`

and`anova`

(#268) - Updates to documentation and existing vignettes (#268)
- Add alias for
`hypothesize()`

(`hypothesise()`

) (#271) - Subtraction order no longer required for difference-based tests–a warning will be raised in the case that the user doesn’t supply an
`order`

argument (#275, #281) - Add new messages for common errors (#277)
- Increase coverage of theoretical methods in documentation (#278, #280)
- Drop missing values and reduce size of
`gss`

dataset used in examples (#282) - Add
`stat = "ratio of props"`

and`stat = "odds ratio"`

to`calculate`

(#285) - Add
`prop_test()`

, a tidy interface to`prop.test()`

(#284, #287) - Updates to
`visualize()`

for compatibility with`ggplot2`

v3.3.0 (#289) - Fix error when bootstrapping with small samples and raise warnings/errors when appropriate (#239, #244, #291)
- Fix unit test failures resulting from breaking changes in
`dplyr`

v1.0.0 - Fix error in
`generate()`

when response variable is named`x`

(#299) - Add
`two-sided`

and`two sided`

as aliases for`two_sided`

for the`direction`

argument in`get_p_value()`

and`shade_p_value()`

(#302) - Fix
`t_test()`

and`t_stat()`

ignoring the`order`

argument (#310)

- Updates to documentation and other tweaks

`shade_confidence_interval()`

now plots vertical lines starting from zero (previously - from the bottom of a plot) (#234).`shade_p_value()`

now uses “area under the curve” approach to shading (#229).

- Updated
`chisq_test()`

to take arguments in a response/explanatory format, perform goodness of fit tests, and default to the approximation approach (#241). - Updated
`chisq_stat()`

to do goodness of fit (#241). - Make interface to
`hypothesize()`

clearer by adding the options for the point null parameters to the function signature (#242). - Manage
`infer`

class more systematically (#219). - Use
`vdiffr`

for plot testing (#221).

- Added Evgeni Chasnovski as author for his incredible work on refactoring the package and providing excellent support.

- Changed method of computing two-sided p-value to a more conventional one. It also makes
`get_pvalue()`

and`visualize()`

more aligned (#205).

- Deprecated
`p_value()`

(use`get_p_value()`

instead) (#180). - Deprecated
`conf_int()`

(use`get_confidence_interval()`

instead) (#180). - Deprecated (via warnings) plotting p-value and confidence interval in
`visualize()`

(use new functions`shade_p_value()`

and`shade_confidence_interval()`

instead) (#178).

`shade_p_value()`

- {ggplot2}-like layer function to add information about p-value region to`visualize()`

output. Has alias`shade_pvalue()`

.`shade_confidence_interval()`

- {ggplot2}-like layer function to add information about confidence interval region to`visualize()`

output. Has alias`shade_ci()`

.

- Account for
`NULL`

value in left hand side of formula in`specify()`

(#156) and`type`

in`generate()`

(#157). - Update documentation code to follow tidyverse style guide (#159).
- Remove help page for internal
`set_params()`

(#165). - Fully use {tibble} (#166).
- Fix
`calculate()`

to not depend on order of`p`

for`type = "simulate"`

(#122). - Reduce code duplication (#173).
- Make transparency in
`visualize()`

to not depend on method and data volume. - Make
`visualize()`

work for “One sample t” theoretical type with`method = "both"`

. - Add
`stat = "sum"`

and`stat = "count"`

options to`calculate()`

(#50).

- Stop using package {assertive} in favor of custom type checks (#149)
- Fixed
`t_stat()`

to use`...`

so`var.equal`

works - With the help of @echasnovski, fixed
`var.equal = TRUE`

for`specify() %>% calculate(stat = "t")`

- Use custom functions for error, warning, message, and
`paste()`

handling (#155)

- Added
`conf_int`

logical argument and`conf_level`

argument to`t_test()`

- Switched
`shade_color`

argument in`visualize()`

to be`pvalue_fill`

instead since fill color for confidence intervals is also added now - Shading for Confidence Intervals in
`visualize()`

- Green is default color for CI and red for p-values
`direction = "between"`

to get the green shading- Currently working only for simulation-based methods

- Implemented
`conf_int()`

function for computing confidence interval provided a simulation-based method with a`stat`

variable`get_ci()`

and`get_confidence_interval()`

are aliases for`conf_int()`

- Converted longer confidence interval calculation code in vignettes to use
`get_ci()`

instead

- Implemented
`p_value()`

function for computing p-value provided a simulation-based method with a`stat`

variable`get_pvalue()`

is an alias for`p_value()`

- Converted longer p-value calculation code in vignettes to use
`get_pvalue()`

instead

- Implemented Chi-square Goodness of Fit observed stat depending on
`params`

being set in`hypothesize`

with`specify() %>% calculate()`

shortcut - Removed “standardized” slope \(t\) since its formula is different than “standardized” correlation and there is no way currently to give one over the other
- Implemented correlation with bootstrap CI and permutation hypothesis test
- Filled the
`type`

argument automatically in`generate()`

based on`specify()`

and`hypothesize()`

- Added message if
`type`

is given differently than expected

- Added message if
- Implemented
`specify() %>% calculate()`

for getting observed statistics.`visualize()`

works with either a 1x1 data frame or a vector for its`obs_stat`

argument- Got
`stat = "t"`

working

- Refactored
`calculate()`

into smaller functions to reduce complexity - Produced error if
`mu`

is given in`hypothesize()`

but`stat = "median"`

is provided in`calculate()`

and other similar mis-specifications - Tweaked
`chisq_stat()`

and`t_stat()`

to match with`specify() %>% calculate()`

framework- Both work in the one sample and two sample cases by providing
`formula`

- Added
`order`

argument to`t_stat()`

- Both work in the one sample and two sample cases by providing
- Added implementation of one sample
`t_test()`

by passing in the`mu`

argument to`t.test`

from`hypothesize()`

- Tweaked
`pkgdown`

page to include ToDo’s using {dplyr} example

- Switched to
`!!`

instead of`UQ()`

since`UQ()`

is deprecated in {rlang} 0.2.0 - Added many new files:
`CONDUCT.md`

,`CONTRIBUTING.md`

, and`TO-DO.md`

- Updated README file with more development information
- Added wrapper functions
`t_test()`

and`chisq_test()`

that use a formula interface and provide an intuitive wrapper to`t.test()`

and`chisq.test()`

- Created
`stat = "z"`

and`stat = "t"`

options - Added many new arguments to
`visualize()`

to prescribe colors to shade and use for observed statistics and theoretical density curves - Added check so that a bar graph created with
`visualize()`

if number of unique values for generated statistics is small - Added shading for
`method = "theoretical"`

- Implemented shading for simulation methods w/o a traditional distribution
- Use percentiles to determine two-tailed shading

- Changed
`method = "randomization"`

to`method = "simulation"`

- Added warning when theoretical distribution is used that assumptions should be checked

- Added theoretical distributions to
`visualize()`

alone and as overlay with current implementations being- Two sample t
- ANOVA F
- One proportion z
- Two proportion z
- Chi-square test of independence
- Chi-square Goodness of Fit test
- Standardized slope (t)

- Added additional tests
- Added
`order`

argument in`calculate()`

- Fixed bugs post-CRAN release
- Automated travis build of pkgdown to gh-pages branch

- Altered the way that successes are indicated in an infer pipeline. They now live in
`specify()`

. - Updated documentation with examples
- Created
`pkgdown`

site materials- Deployed to https://infer.netlify.com

- Implemented the “intro stats” examples for randomization methods