Changelog
Source:NEWS.md
olsrr 0.5.3.9000
New Features
 Force variables in/out in variable selection procedures
 Hierarchical selection
 Variable selection using rsquared and adjusted rsquared
Bug Fixes
 Allow users to specify threshold for detecting outliers (#178)
 If
ols_test_outlier()
does not find any outliers, it returns largest positive residual instead of largest absolute residual (#177)  using
ols_step_all_possible()
with Model created from dynamic function leads to"Error in eval(model$call$data) . . . not found"
(#176) 
ols_step_both_p(): Error in if (pvals[minp] <= pent) {: argument is of length zero
(#175)  Handle extremely significant variables (#173)

ols_correlations()
returns error for models with 2 predictors (#168) 
ols_step_both_aic()
doesn’t return model (#167) 
ols_regress()
returned residual standard error instead of RMSE (@jensdanielmueller, #165)  Extracting model data (#159)
 ols_plot_resid_stud() fails to plot outliers due to yaxis range (#155)
 ols_correlations error (#191)
 Mallow’s Cp behaves inconsistently depending on model specification (#196)
 ols_step_forward_p(…) problem using the funtion ols_step_forward_p (#200)
 Output of the command “ols_step_both_aic” doesn’t contain final model (#201)
olsrr 0.5.3
CRAN release: 20200210
This is a patch release to reduce the number of packages imported and fix other CRAN errors.
New Features
 Bonferroni outlier test (#129)
olsrr 0.5.2
CRAN release: 20181122
This is a minor release to fix bugs from breaking changes in recipes package and other enhancements.
olsrr 0.5.1
CRAN release: 20180504
This is a patch release to fix minor bugs and improve error messages.
olsrr 0.5.0
CRAN release: 20180326
This is a minor release for bug fixes and API changes.
olsrr 0.4.0
CRAN release: 20171205
Enhancements
 use
ols_launch_app()
to launch a shiny app for building models  save beta coefficients for each independent variable in
ols_all_subset()
(#41)
Bug Fixes
 mismatch in sign of partial and semi partial correlations (#44)
 error in diagnostic panel (#45)
 standardized betas in the presence of interaction terms (#46)
A big thanks goes to (Dr. Kimberly Henry) for identifying bugs and other valuable feedback that helped improve the package.
olsrr 0.3.0
CRAN release: 20170831
This is a minor release containing bug fixes.
Bug Fixes
 output from reg_compute rounded up to 3 decimal points (#24)
 added variable plot fails when model includes categorical variables (#25)
 all possible regression fails when model includes categorical predictors (#26)
 output from bartlett test rounded to 3 decimal points (#27)
 best subsets regression fails when model includes categorical predictors (#28)
 output from breusch pagan test rounded to 4 decimal points (#29)
 output from collinearity diagnostics rounded to 3 decimal points (#30)
 cook’s d bar plot threshold rounded to 3 decimal points (#31)
 cook’s d chart threshold rounded to 3 decimal points (#32)
 output from f test rounded to 3 decimal points (#33)
 output from measures of influence rounded to 4 decimal points (#34)
 output from information criteria rounded to 4 decimal points (#35)
 studentized residuals vs leverage plot threshold rounded to 3 decimal points (#36)
 output from score test rounded to 3 decimal points (#37)
 step AIC backward method AIC value rounded to 3 decimal points (#38)
 step AIC backward method AIC value rounded to 3 decimal points (#39)
 step AIC both direction method AIC value rounded to 3 decimal points (#40)
olsrr 0.2.0
CRAN release: 20170605
This is a minor release containing bug fixes and minor improvements.
Bug Fixes
 inline functions in model formula caused errors in stepwise regression (#2)
 added variable plots (
ols_avplots
) returns error when model formula contains inline functions (#3)  all possible regression (
ols_all_subset
) returns an error when the model formula contains inline functions or interaction variables (#4)  best subset regression (
ols_best_subset
) returns an error when the model formula contains inline functions or interaction variables (#5)  studentized residual plot (
ols_srsd_plot
) returns an error when the model formula contains inline functions (#6)  stepwise backward regression (
ols_step_backward
) returns an error when the model formula contains inline functions or interaction variables (#7)  stepwise forward regression (
ols_step_backward
) returns an error when the model formula contains inline functions (#8)  stepAIC backward regression (
ols_stepaic_backward
) returns an error when the model formula contains inline functions (#9)  stepAIC forward regression (
ols_stepaic_forward
) returns an error when the model formula contains inline functions (#10)  stepAIC regression (
ols_stepaic_both
) returns an error when the model formula contains inline functions (#11)  outliers incorrectly plotted in (
ols_cooksd_barplot
) cook’s d bar plot (#12)  regression (
ols_regress
) returns an error when the model formula contains inline functions (#21)  output from step AIC backward regression (
ols_stepaic_backward
) is not properly formatted (#22)  output from step AIC regression (
ols_stepaic_both
) is not properly formatted (#23)
Enhancements
 cook’s d bar plot (
ols_cooksd_barplot
) returns the threshold value used to classify the observations as outliers (#13)  cook’s d chart (
ols_cooksd_chart
) returns the threshold value used to classify the observations as outliers (#14)  DFFITs plot (
ols_dffits_plot
) returns the threshold value used to classify the observations as outliers (#15)  deleted studentized residuals vs fitted values plot (
ols_dsrvsp_plot
) returns the threshold value used to classify the observations as outliers (#16)  studentized residuals vs leverage plot (
ols_rsdlev_plot
) returns the threshold value used to detect outliers/high leverage observations (#17)  standarized residuals chart (
ols_srsd_chart
) returns the threshold value used to classify the observations as outliers (#18)  studentized residuals plot (
ols_srsd_plot
) returns the threshold value used to classify the observations as outliers (#19)