olsrr 0.6.0

This is a minor release for bug fixes and other enhancements.

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
• hierarchical selection can be enabled when using p values as variable selection metric
• 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)