# olsrr 0.7.0

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](https://github.com/rsquaredacademy/olsrr/issues/214))

## Bug Fixes

- `ols_step_best_subset()` unable to force more than one variable in selection process ([#210](https://github.com/rsquaredacademy/olsrr/issues/210))
- `plot()` with `ols_step_both_aic()` doesn't show anything when AIC values are negative ([#212](https://github.com/rsquaredacademy/olsrr/issues/212))

# olsrr 0.6.1

This is a patch release for urgent bug fixes.

## Bug Fixes

- Limit maximum subset order in `ols_step_all_possible()` ([#202](https://github.com/rsquaredacademy/olsrr/issues/202))
- Check model type ([#204](https://github.com/rsquaredacademy/olsrr/issues/204))
- Mismatch in column names in `ols_step_all_possible()` ([#211](https://github.com/rsquaredacademy/olsrr/issues/211))
- RMSE is not square root of MSE in `ols_regress()` ([#213](https://github.com/rsquaredacademy/olsrr/issues/213))
- `geom_segment()` warning in `ols_plot_obs_fit()` ([#217](https://github.com/rsquaredacademy/olsrr/issues/217))
- New snapshot added every time tests are run ([#218](https://github.com/rsquaredacademy/olsrr/issues/218))

# olsrr 0.6.0

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](https://github.com/rsquaredacademy/olsrr/issues/178))
- If `ols_test_outlier()` does not find any outliers, it returns largest positive residual instead of largest absolute residual ([#177](https://github.com/rsquaredacademy/olsrr/issues/177))
- using `ols_step_all_possible()` with Model created from dynamic function leads to `"Error in eval(model$call$data) . . . not found"` ([#176](https://github.com/rsquaredacademy/olsrr/issues/176))
- `ols_step_both_p(): Error in if (pvals[minp] <= pent) {: argument is of length zero` ([#175](https://github.com/rsquaredacademy/olsrr/issues/175))
- Handle extremely significant variables ([#173](https://github.com/rsquaredacademy/olsrr/issues/173))
- `ols_correlations()` returns error for models with 2 predictors ([#168](https://github.com/rsquaredacademy/olsrr/issues/168))
- `ols_step_both_aic()` doesn't return model ([#167](https://github.com/rsquaredacademy/olsrr/issues/167))
- `ols_regress()` returned residual standard error instead of RMSE (@jens-daniel-mueller, [#165](https://github.com/rsquaredacademy/olsrr/issues/165))
- Extracting model data ([#159](https://github.com/rsquaredacademy/olsrr/issues/159))
- ols_plot_resid_stud() fails to plot outliers due to y-axis range ([#155](https://github.com/rsquaredacademy/olsrr/issues/155))
- ols_correlations error ([#191](https://github.com/rsquaredacademy/olsrr/issues/191))
- Mallow's Cp behaves inconsistently depending on model specification ([#196](https://github.com/rsquaredacademy/olsrr/issues/196))
- ols_step_forward_p(...) problem using the funtion ols_step_forward_p ([#200](https://github.com/rsquaredacademy/olsrr/issues/200))
- Output of the command "ols_step_both_aic" doesn't contain final model ([#201](https://github.com/rsquaredacademy/olsrr/issues/201))

# olsrr 0.5.3

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

## New Features

- Bonferroni outlier test ([#129](https://github.com/rsquaredacademy/olsrr/issues/129))

## Breaking Changes

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

- `ols_test_bartlett()`
- `ols_plot_resid_regressor()`

# olsrr 0.5.2

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](https://github.com/rsquaredacademy/olsrr/issues/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](https://github.com/rsquaredacademy/olsrr/issues/81))

# olsrr 0.5.1

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.

# olsrr 0.5.0

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](https://github.com/rsquaredacademy/olsrr/issues/49))

- ols_regress returns error in the presence of interaction terms in the formula ([#47](https://github.com/rsquaredacademy/olsrr/issues/47))

- return current version ([#48](https://github.com/rsquaredacademy/olsrr/issues/48))

# olsrr 0.4.0

## 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](https://github.com/rsquaredacademy/olsrr/issues/41))

## Bug Fixes

- mismatch in sign of partial and semi partial correlations ([#44](https://github.com/rsquaredacademy/olsrr/issues/44))
- error in diagnostic panel ([#45](https://github.com/rsquaredacademy/olsrr/issues/45))
- standardized betas in the presence of interaction terms ([#46](https://github.com/rsquaredacademy/olsrr/issues/46))

A big thanks goes to ([Dr. Kimberly Henry](https://kimberlyhenry.github.io/my_website/)) for
identifying bugs and other valuable feedback that helped improve the package.

# olsrr 0.3.0

This is a minor release containing bug fixes.

## Bug Fixes

- output from reg_compute rounded up to 3 decimal points ([#24](https://github.com/rsquaredacademy/olsrr/issues/24))
- added variable plot fails when model includes categorical variables ([#25](https://github.com/rsquaredacademy/olsrr/issues/25))
- all possible regression fails when model includes categorical predictors ([#26](https://github.com/rsquaredacademy/olsrr/issues/26))
- output from bartlett test rounded to 3 decimal points ([#27](https://github.com/rsquaredacademy/olsrr/issues/27))
- best subsets regression fails when model includes categorical predictors ([#28](https://github.com/rsquaredacademy/olsrr/issues/28))
- output from breusch pagan test rounded to 4 decimal points ([#29](https://github.com/rsquaredacademy/olsrr/issues/29))
- output from collinearity diagnostics rounded to 3 decimal points ([#30](https://github.com/rsquaredacademy/olsrr/issues/30))
- cook's d bar plot threshold rounded to 3 decimal points ([#31](https://github.com/rsquaredacademy/olsrr/issues/31))
- cook's d chart threshold rounded to 3 decimal points ([#32](https://github.com/rsquaredacademy/olsrr/issues/32))
- output from f test rounded to 3 decimal points ([#33](https://github.com/rsquaredacademy/olsrr/issues/33))
- output from measures of influence rounded to 4 decimal points ([#34](https://github.com/rsquaredacademy/olsrr/issues/34))
- output from information criteria rounded to 4 decimal points ([#35](https://github.com/rsquaredacademy/olsrr/issues/35))
- studentized residuals vs leverage plot threshold rounded to 3 decimal points ([#36](https://github.com/rsquaredacademy/olsrr/issues/36))
- output from score test rounded to 3 decimal points ([#37](https://github.com/rsquaredacademy/olsrr/issues/37))
- step AIC backward method AIC value rounded to 3 decimal points ([#38](https://github.com/rsquaredacademy/olsrr/issues/38))
- step AIC backward method AIC value rounded to 3 decimal points ([#39](https://github.com/rsquaredacademy/olsrr/issues/39))
- step AIC both direction method AIC value rounded to 3 decimal points ([#40](https://github.com/rsquaredacademy/olsrr/issues/40))

# olsrr 0.2.0

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

## Bug Fixes

- inline functions in model formula caused errors in stepwise regression ([#2](https://github.com/rsquaredacademy/olsrr/issues/2))
- added variable plots (`ols_avplots`) returns error when model formula contains inline functions ([#3](https://github.com/rsquaredacademy/olsrr/issues/3))
- all possible regression (`ols_all_subset`) returns an error when the model formula contains inline functions or interaction variables ([#4](https://github.com/rsquaredacademy/olsrr/issues/4))
- best subset regression (`ols_best_subset`) returns an error when the model formula contains inline functions or interaction variables ([#5](https://github.com/rsquaredacademy/olsrr/issues/5))
- studentized residual plot (`ols_srsd_plot`) returns an error when the model formula contains inline functions ([#6](https://github.com/rsquaredacademy/olsrr/issues/6))
- stepwise backward regression (`ols_step_backward`) returns an error when the model formula contains inline functions or interaction variables ([#7](https://github.com/rsquaredacademy/olsrr/issues/7))
- stepwise forward regression (`ols_step_backward`) returns an error when the model formula contains inline functions ([#8](https://github.com/rsquaredacademy/olsrr/issues/8))
- stepAIC backward regression (`ols_stepaic_backward`) returns an error when the model formula contains inline functions ([#9](https://github.com/rsquaredacademy/olsrr/issues/9))
- stepAIC forward regression (`ols_stepaic_forward`) returns an error when the model formula contains inline functions ([#10](https://github.com/rsquaredacademy/olsrr/issues/10))
- stepAIC regression (`ols_stepaic_both`) returns an error when the model formula contains inline functions ([#11](https://github.com/rsquaredacademy/olsrr/issues/11))
- outliers incorrectly plotted in (`ols_cooksd_barplot`) cook's d bar plot ([#12](https://github.com/rsquaredacademy/olsrr/issues/12))
- regression (`ols_regress`) returns an error when the model formula contains inline functions ([#21](https://github.com/rsquaredacademy/olsrr/issues/21))
- output from step AIC backward regression (`ols_stepaic_backward`) is not properly formatted ([#22](https://github.com/rsquaredacademy/olsrr/issues/22))
- output from step AIC regression (`ols_stepaic_both`) is not properly formatted ([#23](https://github.com/rsquaredacademy/olsrr/issues/23))

## Enhancements

- cook's d bar plot (`ols_cooksd_barplot`) returns the threshold value used to classify the observations as outliers ([#13](https://github.com/rsquaredacademy/olsrr/issues/13))
- cook's d chart (`ols_cooksd_chart`) returns the threshold value used to classify the observations as outliers ([#14](https://github.com/rsquaredacademy/olsrr/issues/14))
- DFFITs plot (`ols_dffits_plot`) returns the threshold value used to classify the observations as outliers ([#15](https://github.com/rsquaredacademy/olsrr/issues/15))
- deleted studentized residuals vs fitted values plot (`ols_dsrvsp_plot`) returns the threshold value used to classify the observations as outliers ([#16](https://github.com/rsquaredacademy/olsrr/issues/16))
- studentized residuals vs leverage plot (`ols_rsdlev_plot`) returns the threshold value used to detect outliers/high leverage observations ([#17](https://github.com/rsquaredacademy/olsrr/issues/17))
- standarized residuals chart (`ols_srsd_chart`) returns the threshold value used to classify the observations as outliers ([#18](https://github.com/rsquaredacademy/olsrr/issues/18))
- studentized residuals plot (`ols_srsd_plot`) returns the threshold value used to classify the observations as outliers ([#19](https://github.com/rsquaredacademy/olsrr/issues/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.

# olsrr 0.1.0

First release.
