CRAN Package Check Results for Package sentometrics

Last updated on 2018-10-19 01:46:51 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.5.1 49.08 170.05 219.13 NOTE
r-devel-linux-x86_64-debian-gcc 0.5.1 47.33 124.70 172.03 NOTE
r-devel-linux-x86_64-fedora-clang 0.5.1 273.77 NOTE
r-devel-linux-x86_64-fedora-gcc 0.5.1 260.26 NOTE
r-devel-windows-ix86+x86_64 0.5.1 143.00 262.00 405.00 NOTE
r-patched-linux-x86_64 0.5.1 53.82 141.93 195.75 NOTE
r-patched-solaris-x86 0.5.1 326.50 NOTE
r-release-linux-x86_64 0.5.1 54.48 141.33 195.81 NOTE
r-release-windows-ix86+x86_64 0.5.1 146.00 330.00 476.00 NOTE
r-release-osx-x86_64 0.5.1 NOTE
r-oldrel-windows-ix86+x86_64 0.5.1 114.00 360.00 474.00 ERROR
r-oldrel-osx-x86_64 0.5.1 ERROR

Additional issues

clang-UBSAN gcc-UBSAN

Check Details

Version: 0.5.1
Check: for GNU extensions in Makefiles
Result: NOTE
    GNU make is a SystemRequirements.
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, r-oldrel-osx-x86_64

Version: 0.5.1
Check: installed package size
Result: NOTE
     installed size is 5.3Mb
     sub-directories of 1Mb or more:
     data 2.3Mb
     libs 2.6Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-osx-x86_64

Version: 0.5.1
Check: data for non-ASCII characters
Result: NOTE
     Note: found 4436 marked UTF-8 strings
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 0.5.1
Check: running tests for arch ‘i386’
Result: ERROR
     Running 'testthat.R' [75s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     >
     > library("testthat")
     > library("sentometrics")
     Loading required package: data.table
     >
     > test_check("sentometrics")
     iteration: 1 from 6
     alphas run: 0.2, 0.7
     iteration: 2 from 6
     alphas run: 0.2, 0.7
     iteration: 3 from 6
     alphas run: 0.2, 0.7
     iteration: 4 from 6
     alphas run: 0.2, 0.7
     iteration: 5 from 6
     alphas run: 0.2, 0.7
     iteration: 6 from 6
     alphas run: 0.2, 0.7
     -- 1. Error: (unknown) (@test_methods_sentomeasures.R#33) ---------------------
     length of 'center' must equal the number of columns of 'x'
     1: scale(sentMeas, center = as.vector(sentMeas$stats["mean", ]), scale = as.vector(sentMeas$stats["sd",
     ])) at testthat/test_methods_sentomeasures.R:33
     2: scale.sentomeasures(sentMeas, center = as.vector(sentMeas$stats["mean", ]), scale = as.vector(sentMeas$stats["sd",
     ]))
     3: scale(measures, center, scale)
     4: scale.default(measures, center, scale)
     5: stop("length of 'center' must equal the number of columns of 'x'")
    
     alphas run: 0.2, 0.7
     alphas run: 0.2, 0.7
     alphas run: 0.2, 0.7
     Training model... Done.
     Training model... Done.
     Training model... Done.
     alphas run: 0.2, 0.7
     iteration: 1 from 16
     alphas run: 0, 0.4, 1
     iteration: 2 from 16
     alphas run: 0, 0.4, 1
     iteration: 3 from 16
     alphas run: 0, 0.4, 1
     iteration: 4 from 16
     alphas run: 0, 0.4, 1
     iteration: 5 from 16
     alphas run: 0, 0.4, 1
     iteration: 6 from 16
     alphas run: 0, 0.4, 1
     iteration: 7 from 16
     alphas run: 0, 0.4, 1
     iteration: 8 from 16
     alphas run: 0, 0.4, 1
     iteration: 9 from 16
     alphas run: 0, 0.4, 1
     iteration: 10 from 16
     alphas run: 0, 0.4, 1
     iteration: 11 from 16
     alphas run: 0, 0.4, 1
     iteration: 12 from 16
     alphas run: 0, 0.4, 1
     iteration: 13 from 16
     alphas run: 0, 0.4, 1
     iteration: 14 from 16
     alphas run: 0, 0.4, 1
     iteration: 15 from 16
     alphas run: 0, 0.4, 1
     iteration: 16 from 16
     alphas run: 0, 0.4, 1
     iteration: 1 from 16
     alphas run: 0, 0.4, 1
     iteration: 2 from 16
     alphas run: 0, 0.4, 1
     iteration: 3 from 16
     alphas run: 0, 0.4, 1
     iteration: 4 from 16
     alphas run: 0, 0.4, 1
     iteration: 5 from 16
     alphas run: 0, 0.4, 1
     iteration: 6 from 16
     alphas run: 0, 0.4, 1
     iteration: 7 from 16
     alphas run: 0, 0.4, 1
     iteration: 8 from 16
     alphas run: 0, 0.4, 1
     iteration: 9 from 16
     alphas run: 0, 0.4, 1
     iteration: 10 from 16
     alphas run: 0, 0.4, 1
     iteration: 11 from 16
     alphas run: 0, 0.4, 1
     iteration: 12 from 16
     alphas run: 0, 0.4, 1
     iteration: 13 from 16
     alphas run: 0, 0.4, 1
     iteration: 14 from 16
     alphas run: 0, 0.4, 1
     iteration: 15 from 16
     alphas run: 0, 0.4, 1
     iteration: 16 from 16
     alphas run: 0, 0.4, 1
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Number of observations: 226
     Optimal elastic net alpha parameter: 0.7
     Optimal elastic net lambda parameter: 0.11
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) 96.374913
     GI_en--economy--almon1 1445.453900
     GI_en--noneconomy--almon1 -2747.069670
     LM_en--wsj--almon1 -3768.057855
     LM_en--wapo--almon1 5157.542215
     LM_en--economy--almon1 1510.211743
     GI_en--wsj--almon1_inv -242.569023
     GI_en--wapo--almon1_inv 1407.865522
     LM_en--wsj--almon1_inv 170.561023
     LM_en--wapo--almon1_inv -1793.703655
     LM_en--economy--almon1_inv 1692.843627
     GI_en--economy--almon2 214.657191
     GI_en--noneconomy--almon2 -801.010997
     LM_en--economy--almon2 1508.768614
     LM_en--economy--almon2_inv 409.196574
     GI_en--wsj--almon3 -103.845098
     GI_en--noneconomy--almon3 -503.717642
     LM_en--wsj--almon3 -2018.717050
     LM_en--wapo--almon3 423.943608
     LM_en--economy--almon3 -4288.401756
     GI_en--wsj--almon3_inv 629.186140
     GI_en--economy--almon3_inv -3049.207135
     GI_en--noneconomy--almon3_inv 3207.076752
     LM_en--wsj--almon3_inv 4705.304398
     LM_en--wapo--almon3_inv -4942.936279
     x1 -2.975119
     x2 1.495385
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: binomial
     Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
     Number of observations: 233
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 100
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) -0.65335663
     x1 0.27680048
     x2 0.02006744
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: multinomial
     Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
     Number of observations: 229
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 0.15
    
     Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
     - - - - - - - - - - - - - - - - - - - -
    
     below- 7
     below 10
     above 7
     above+ 5
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Sample size: 216
     Total number of iterations/predictions: 16
     Optimal average elastic net alpha parameter: 1
     Optimal average elastic net lambda parameter: 49.91
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 33.33 %
     Root mean squared prediction error: 70
     Mean absolute deviation: 55.36
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Sample size: 216
     Total number of iterations/predictions: 16
     Optimal average elastic net alpha parameter: 0
     Optimal average elastic net lambda parameter: 4692.05
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 53.33 %
     Root mean squared prediction error: 49.7
     Mean absolute deviation: 36.93
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Number of observations: 226
     Optimal elastic net alpha parameter: 0.7
     Optimal elastic net lambda parameter: 0.11
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) 96.374913
     GI_en--economy--almon1 1445.453900
     GI_en--noneconomy--almon1 -2747.069670
     LM_en--wsj--almon1 -3768.057855
     LM_en--wapo--almon1 5157.542215
     LM_en--economy--almon1 1510.211743
     GI_en--wsj--almon1_inv -242.569023
     GI_en--wapo--almon1_inv 1407.865522
     LM_en--wsj--almon1_inv 170.561023
     LM_en--wapo--almon1_inv -1793.703655
     LM_en--economy--almon1_inv 1692.843627
     GI_en--economy--almon2 214.657191
     GI_en--noneconomy--almon2 -801.010997
     LM_en--economy--almon2 1508.768614
     LM_en--economy--almon2_inv 409.196574
     GI_en--wsj--almon3 -103.845098
     GI_en--noneconomy--almon3 -503.717642
     LM_en--wsj--almon3 -2018.717050
     LM_en--wapo--almon3 423.943608
     LM_en--economy--almon3 -4288.401756
     GI_en--wsj--almon3_inv 629.186140
     GI_en--economy--almon3_inv -3049.207135
     GI_en--noneconomy--almon3_inv 3207.076752
     LM_en--wsj--almon3_inv 4705.304398
     LM_en--wapo--almon3_inv -4942.936279
     x1 -2.975119
     x2 1.495385
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: binomial
     Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
     Number of observations: 233
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 100
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) -0.65335663
     x1 0.27680048
     x2 0.02006744
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: multinomial
     Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
     Number of observations: 229
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 0.15
    
     Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
     - - - - - - - - - - - - - - - - - - - -
    
     below- 7
     below 10
     above 7
     above+ 5
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Sample size: 216
     Total number of iterations/predictions: 16
     Optimal average elastic net alpha parameter: 1
     Optimal average elastic net lambda parameter: 49.91
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 33.33 %
     Root mean squared prediction error: 70
     Mean absolute deviation: 55.36
     A sentomodel object.A sentomodel object.A sentomodel object.A sentomodeliter object.== testthat results ===========================================================
     OK: 135 SKIPPED: 0 FAILED: 1
     1. Error: (unknown) (@test_methods_sentomeasures.R#33)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-oldrel-windows-ix86+x86_64

Version: 0.5.1
Check: running tests for arch ‘x64’
Result: ERROR
     Running 'testthat.R' [85s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     >
     > library("testthat")
     > library("sentometrics")
     Loading required package: data.table
     >
     > test_check("sentometrics")
     iteration: 1 from 6
     alphas run: 0.2, 0.7
     iteration: 2 from 6
     alphas run: 0.2, 0.7
     iteration: 3 from 6
     alphas run: 0.2, 0.7
     iteration: 4 from 6
     alphas run: 0.2, 0.7
     iteration: 5 from 6
     alphas run: 0.2, 0.7
     iteration: 6 from 6
     alphas run: 0.2, 0.7
     -- 1. Error: (unknown) (@test_methods_sentomeasures.R#33) ---------------------
     length of 'center' must equal the number of columns of 'x'
     1: scale(sentMeas, center = as.vector(sentMeas$stats["mean", ]), scale = as.vector(sentMeas$stats["sd",
     ])) at testthat/test_methods_sentomeasures.R:33
     2: scale.sentomeasures(sentMeas, center = as.vector(sentMeas$stats["mean", ]), scale = as.vector(sentMeas$stats["sd",
     ]))
     3: scale(measures, center, scale)
     4: scale.default(measures, center, scale)
     5: stop("length of 'center' must equal the number of columns of 'x'")
    
     alphas run: 0.2, 0.7
     alphas run: 0.2, 0.7
     alphas run: 0.2, 0.7
     Training model... Done.
     Training model... Done.
     Training model... Done.
     alphas run: 0.2, 0.7
     iteration: 1 from 16
     alphas run: 0, 0.4, 1
     iteration: 2 from 16
     alphas run: 0, 0.4, 1
     iteration: 3 from 16
     alphas run: 0, 0.4, 1
     iteration: 4 from 16
     alphas run: 0, 0.4, 1
     iteration: 5 from 16
     alphas run: 0, 0.4, 1
     iteration: 6 from 16
     alphas run: 0, 0.4, 1
     iteration: 7 from 16
     alphas run: 0, 0.4, 1
     iteration: 8 from 16
     alphas run: 0, 0.4, 1
     iteration: 9 from 16
     alphas run: 0, 0.4, 1
     iteration: 10 from 16
     alphas run: 0, 0.4, 1
     iteration: 11 from 16
     alphas run: 0, 0.4, 1
     iteration: 12 from 16
     alphas run: 0, 0.4, 1
     iteration: 13 from 16
     alphas run: 0, 0.4, 1
     iteration: 14 from 16
     alphas run: 0, 0.4, 1
     iteration: 15 from 16
     alphas run: 0, 0.4, 1
     iteration: 16 from 16
     alphas run: 0, 0.4, 1
     iteration: 1 from 16
     alphas run: 0, 0.4, 1
     iteration: 2 from 16
     alphas run: 0, 0.4, 1
     iteration: 3 from 16
     alphas run: 0, 0.4, 1
     iteration: 4 from 16
     alphas run: 0, 0.4, 1
     iteration: 5 from 16
     alphas run: 0, 0.4, 1
     iteration: 6 from 16
     alphas run: 0, 0.4, 1
     iteration: 7 from 16
     alphas run: 0, 0.4, 1
     iteration: 8 from 16
     alphas run: 0, 0.4, 1
     iteration: 9 from 16
     alphas run: 0, 0.4, 1
     iteration: 10 from 16
     alphas run: 0, 0.4, 1
     iteration: 11 from 16
     alphas run: 0, 0.4, 1
     iteration: 12 from 16
     alphas run: 0, 0.4, 1
     iteration: 13 from 16
     alphas run: 0, 0.4, 1
     iteration: 14 from 16
     alphas run: 0, 0.4, 1
     iteration: 15 from 16
     alphas run: 0, 0.4, 1
     iteration: 16 from 16
     alphas run: 0, 0.4, 1
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Number of observations: 226
     Optimal elastic net alpha parameter: 0.7
     Optimal elastic net lambda parameter: 0.11
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) 96.374913
     GI_en--economy--almon1 1445.453895
     GI_en--noneconomy--almon1 -2747.069668
     LM_en--wsj--almon1 -3768.057854
     LM_en--wapo--almon1 5157.542206
     LM_en--economy--almon1 1510.211737
     GI_en--wsj--almon1_inv -242.569022
     GI_en--wapo--almon1_inv 1407.865521
     LM_en--wsj--almon1_inv 170.561023
     LM_en--wapo--almon1_inv -1793.703655
     LM_en--economy--almon1_inv 1692.843622
     GI_en--economy--almon2 214.657189
     GI_en--noneconomy--almon2 -801.010995
     LM_en--economy--almon2 1508.768623
     LM_en--economy--almon2_inv 409.196576
     GI_en--wsj--almon3 -103.845099
     GI_en--noneconomy--almon3 -503.717641
     LM_en--wsj--almon3 -2018.717049
     LM_en--wapo--almon3 423.943606
     LM_en--economy--almon3 -4288.401757
     GI_en--wsj--almon3_inv 629.186139
     GI_en--economy--almon3_inv -3049.207127
     GI_en--noneconomy--almon3_inv 3207.076749
     LM_en--wsj--almon3_inv 4705.304396
     LM_en--wapo--almon3_inv -4942.936268
     x1 -2.975119
     x2 1.495385
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: binomial
     Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
     Number of observations: 233
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 100
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) -0.65335663
     x1 0.27680048
     x2 0.02006744
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: multinomial
     Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
     Number of observations: 229
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 0.15
    
     Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
     - - - - - - - - - - - - - - - - - - - -
    
     below- 7
     below 10
     above 7
     above+ 5
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Sample size: 216
     Total number of iterations/predictions: 16
     Optimal average elastic net alpha parameter: 1
     Optimal average elastic net lambda parameter: 49.91
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 33.33 %
     Root mean squared prediction error: 70
     Mean absolute deviation: 55.36
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Sample size: 216
     Total number of iterations/predictions: 16
     Optimal average elastic net alpha parameter: 0
     Optimal average elastic net lambda parameter: 4692.05
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 53.33 %
     Root mean squared prediction error: 49.7
     Mean absolute deviation: 36.93
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Number of observations: 226
     Optimal elastic net alpha parameter: 0.7
     Optimal elastic net lambda parameter: 0.11
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) 96.374913
     GI_en--economy--almon1 1445.453895
     GI_en--noneconomy--almon1 -2747.069668
     LM_en--wsj--almon1 -3768.057854
     LM_en--wapo--almon1 5157.542206
     LM_en--economy--almon1 1510.211737
     GI_en--wsj--almon1_inv -242.569022
     GI_en--wapo--almon1_inv 1407.865521
     LM_en--wsj--almon1_inv 170.561023
     LM_en--wapo--almon1_inv -1793.703655
     LM_en--economy--almon1_inv 1692.843622
     GI_en--economy--almon2 214.657189
     GI_en--noneconomy--almon2 -801.010995
     LM_en--economy--almon2 1508.768623
     LM_en--economy--almon2_inv 409.196576
     GI_en--wsj--almon3 -103.845099
     GI_en--noneconomy--almon3 -503.717641
     LM_en--wsj--almon3 -2018.717049
     LM_en--wapo--almon3 423.943606
     LM_en--economy--almon3 -4288.401757
     GI_en--wsj--almon3_inv 629.186139
     GI_en--economy--almon3_inv -3049.207127
     GI_en--noneconomy--almon3_inv 3207.076749
     LM_en--wsj--almon3_inv 4705.304396
     LM_en--wapo--almon3_inv -4942.936268
     x1 -2.975119
     x2 1.495385
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: binomial
     Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
     Number of observations: 233
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 100
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) -0.65335663
     x1 0.27680048
     x2 0.02006744
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: multinomial
     Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
     Number of observations: 229
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 0.15
    
     Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
     - - - - - - - - - - - - - - - - - - - -
    
     below- 7
     below 10
     above 7
     above+ 5
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Sample size: 216
     Total number of iterations/predictions: 16
     Optimal average elastic net alpha parameter: 1
     Optimal average elastic net lambda parameter: 49.91
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 33.33 %
     Root mean squared prediction error: 70
     Mean absolute deviation: 55.36
     A sentomodel object.A sentomodel object.A sentomodel object.A sentomodeliter object.== testthat results ===========================================================
     OK: 135 SKIPPED: 0 FAILED: 1
     1. Error: (unknown) (@test_methods_sentomeasures.R#33)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-oldrel-windows-ix86+x86_64

Version: 0.5.1
Check: tests
Result: ERROR
     Running ‘testthat.R’ [61s/54s]
    Running the tests in ‘tests/testthat.R’ failed.
    Last 13 lines of output:
     Optimal average elastic net alpha parameter: 1
     Optimal average elastic net lambda parameter: 49.91
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 33.33 %
     Root mean squared prediction error: 70
     Mean absolute deviation: 55.36
     A sentomodel object.A sentomodel object.A sentomodel object.A sentomodeliter object.══ testthat results ═══════════════════════════════════════════════════════════
     OK: 135 SKIPPED: 0 FAILED: 1
     1. Error: (unknown) (@test_methods_sentomeasures.R#33)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-oldrel-osx-x86_64