- Report an error if the initial model contains all the available variables.

- Skip test on CRAN to avoid failure reported by R-devel with OpenBLAS.

- Remove test that will fail due changes in R-devel.

- Introduce the fs() and nested.fs() functions which adopt a new interface based on formulas
- Change the interface of nested.glm() to align to the new formula interface.
- Change default values for the max.iters (from 15 to 10) and min.llk.diff (from 0 to 2) options.
- Replace the parallel computation backend from the doParallel to the parallel package.
- Restructure the diabetes dataset to be a single data frame.
- Update and expand the example in the README file.
- Use markdown in the package documentation.
- Include tests in the package.

- Silence messages output by newer versions of the pROC package.

- Change maintainer email address.

- Use getfullname() if available also in summary.fs().
- Make nested.glm() accept a formula argument so that models with interaction terms can be specified. This also ensures that such models are fitted correctly in nested.forward.selection() after selection has been performed.
- Add the family field and assign a class to the object created by nested.glm().
- Add nested.performance() to compute the performance of cross-validated models as the area under the curve or the correlation coefficient.

- Document the default selection criterion.
- Correct the check for the verbose option in nested.forward.selection().
- Fix an error occurring in nested.forward.selection() when a categorical variable is selected.

- Make the univariate filter cope with non-matching names in filter.ignore.
- Parallelise the univariate filtering step.
- Add the verbose option to forward.selection().
- Return the coefficients of summary() instead of summary() itself from nested.glm().
- Swap family and folds in nested.glm() for consistency with other functions.
- Add tests for nested.glm().

- Close the parallel clusters at the end of the examples.
- Vectorize the computation of differences in log-likelihoods at iteration 1.
- First version on CRAN.

- Rewrite the examples to satisfy the CRAN upload request.
- Decrease the minimum number of inner folds to 5.

- Use family$dev.resids() to compute log-likelihoods.
- Fix forward.selection() when there’s only one variable to choose from.
- Allow to specify variable names in the choose.from argument and not only indices.
- Allow more freedom in how the outcome variable can be specified for logistic regression.
- Rename parameters x.all, y.all and all.folds to x, y, and folds.
- Merge init.vars and init.model to make formulas a first class input type.
- Rework the diabetes dataset and save it in .rda format.
- Replace the doMC package with doParallel.
- Remove automatic registration of the parallel backend when attaching the package to pass checks on the R-devel win-builder machine.
- Add tests for forward.selection() and nested.forward.selection().

- Sort the indices of the test observations within each fold.
- Reorder some arguments of forward.selection() according to importance.
- Improve the argument checks in forward.selection().
- Let the family argument also be one of the family functions.
- Add tests for argument checks.

- Limit the variable names in the output to the length of the field.
- Clarify that the p-value from forward selection is a false discovery rate.
- Convert documentation to roxygen2 format.

- Check that the indices in the folds don’t exceed the size of the dataset.
- Make the init.model option work in more cases.

- Check for missing values in the predictors and in the outcome variable.
- Return only the right-hand side of the formula in final.model from forward.selection().

- First version of the package.