`NEWS.md`

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

This is a minor release for bug fixes and API changes.

- 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)