Changes in version 0.7.0 (2026-04-22) This is a minor release for bug fixes and other enhancements. New Features - compare performance of regression models using multiple metrics - ANOVA for regression models - evaluate performance of regression models - comprehensive information from model objects Enhancements - Fixed number of steps in stepwise regression (#214) Bug Fixes - ols_step_best_subset() unable to force more than one variable in selection process (#210) - plot() with ols_step_both_aic() doesn't show anything when AIC values are negative (#212) Changes in version 0.6.1 (2024-11-06) This is a patch release for urgent bug fixes. Bug Fixes - Limit maximum subset order in ols_step_all_possible() (#202) - Check model type (#204) - Mismatch in column names in ols_step_all_possible() (#211) - RMSE is not square root of MSE in ols_regress() (#213) - geom_segment() warning in ols_plot_obs_fit() (#217) - New snapshot added every time tests are run (#218) Changes in version 0.6.0 (2024-02-12) This is a minor release for bug fixes and other enhancements. New Features - hierarchical selection can be enables when using p values as variable selection metric Enhancements - force variables to be included or excluded from the model at all stages of variable selection - Variable selection methods allow use of the following metrics: - p value - akaike information criterion (aic) - schwarz bayesian criterion (sbc) - sawa bayesian criterion (sbic) - r-square - adjusted r-square - Choose threshold for determining influential observations in ols_plot_dffits() 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 (@jens-daniel-mueller, #165) - Extracting model data (#159) - ols_plot_resid_stud() fails to plot outliers due to y-axis 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) Changes in version 0.5.3 (2020-02-10) This is a patch release to reduce the number of packages imported and fix other CRAN errors. New Features - Bonferroni outlier test (#129) Breaking Changes The following functions will now require the variable names to be enclosed within quotes - ols_test_bartlett() - ols_plot_resid_regressor() Changes in version 0.5.2 (2018-11-22) This is a minor release to fix bugs from breaking changes in recipes package and other enhancements. Enhancements - variable selection procedures now return the final model as an object of class lm (#81) - data preparation functions of selected plots are now exported to enable end users to create customized plots and to use plotting library of their choice (#86) Changes in version 0.5.1 (2018-05-04) This is a patch release to fix minor bugs and improve error messages. Enhancements olsrr now throws better error messages keeping in mind beginner and intermediate R users. It is a work in progress and should get better in future releases. Bug Fixes Variable selection procedures based on p values now handle categorical variables in the same way as the procedures based on AIC values. Changes in version 0.5.0 (2018-03-26) This is a minor release for bug fixes and API changes. API Changes We have made some changes to the API to make it more user friendly: - all the variable selection procedures start with ols_step_* - all the test start with ols_test_* - all the plots start with ols_plot_* Bug Fixes - ols_regress returns error in the presence of interaction terms in the formula (#49) - ols_regress returns error in the presence of interaction terms in the formula (#47) - return current version (#48) Changes in version 0.4.0 (2017-12-05) 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. Changes in version 0.3.0 (2017-08-31) 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) Changes in version 0.2.0 (2017-06-05) 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) Documentation There were errors in the description of the values returned by some functions. The documentation has been thoroughly revised and improved in this release. Changes in version 0.1.0 (2017-05-11) First release.