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

- hierarchical selection can be enables when using
`p`

values as variable selection metric

- 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()`

- 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)

This is a patch release to reduce the number of packages imported and fix other CRAN errors.

- Bonferroni outlier test (#129)

The following functions will now require the variable names to be enclosed within quotes

`ols_test_bartlett()`

`ols_plot_resid_regressor()`

This is a minor release to fix bugs from breaking changes in recipes package and other 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)

This is a patch release to fix minor bugs and improve error messages.

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.

Variable selection procedures based on p values now handle categorical variables in the same way as the procedures based on AIC values.

This is a minor release for bug fixes and 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_*`

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)

- use
`ols_launch_app()`

to launch a shiny app for building models - save beta coefficients for each independent variable in
`ols_all_subset()`

(#41)

- 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.

This is a minor release containing 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)

This is a minor release containing bug fixes and minor improvements.

- 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)

- 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)

There were errors in the description of the values returned by some functions. The documentation has been thoroughly revised and improved in this release.

First release.