collinear 2.0.0

Warning: This version includes several breaking changes.

Main Changes

Now works with any combination of categorical and numeric responses and predictors. Previously, only numeric responses were considered valid.
Accepts a character vector with multiple response variables and returns a named list of data frames in such cases.
All functions used as input for the argument f have been rewritten, with extended coverage of cases. These functions have also been consistently renamed following these rules:
- A code indicating the metric: r2 for R-squared, auc for area under the curve (for binomial responses), and v for Cramer's V (for categorical responses).
- A code indicating the model: spearman, pearson, and v for direct association; glm for GLMs; gam for GAMs; rf for Random Forest models; and rpart for Recursive Partition Trees.
- The model family for GLMs or GAMs: gaussian for numeric responses, binomial for binomial responses, and poisson for integer counts.
- The term poly2 for GLMs with second-degree polynomials.
When f = NULL, the function f_auto() determines an appropriate default adapted to the types of the response and predictors.
Now issues a warning if predictors show a suspiciously high association with the response. The sensitivity of this test is controlled by the new argument warn_limit.
Parallelization setup is now managed via future::plan(), and a progress bar is available through progressr::handlers().

Now works with any combination of categorical and numeric responses and predictors. Previously, only numeric responses were valid. Categorical predictors are excluded from VIF analysis but are returned in the output if they pass the pairwise correlation test.
Accepts a character vector with multiple response variables and returns a named list of data frames in such cases.
The preference order is now computed internally if preference_order = NULL (default). Therefore, all relevant arguments of the function preference_order() have been added to collinear() with the prefix "preference_".
Parallelization setup is now managed via future::plan(), with a progress bar provided by progressr::handlers(). This setup is leveraged by preference_order() and cor_select().
Target encoding can be disabled by setting the encoding_method argument to NULL.
VIF filtering can be disabled by setting max_vif to NULL.
Pairwise correlation filtering can be disabled by setting max_cor to NULL.

A new robust forward selection algorithm ensures that the most important predictors are retained after multicollinearity filtering when preference_order is used.
Target encoding, along with the response and encoding_method arguments, has been removed from this function. This change also applies to cor_df().
The function now calls validate_data_cor() to ensure that the data is suitable for pairwise correlation multicollinearity filtering.
Parallelization setup is now managed via future::plan(), with a progress bar provided by progressr::handlers(). This setup is used by cor_numeric_vs_categorical() and cor_categorical_vs_categorical() to speed up pairwise correlation computation.

Fixed a bug that prevented cor_numeric_vs_categorical() and cor_categorical_vs_categorical() from triggering properly.

A new robust forward selection algorithm better preserves predictors with higher preference when preference_order is used.
Target encoding, along with the response and encoding_method arguments, has been removed. As a result, this function now only works with numeric predictors. This change also applies to vif_df().
The new function validate_data_vif() is called to ensure the data is suitable for VIF-based multicollinearity filtering. Attempting a VIF analysis in a data frame with more columns than rows now returns an error.

Completely rewritten for parallelization using future::plan() and a progress bar via progressr::handlers().
The default encoding method is now "loo" (leave-one-out), as it provides more useful results in most cases.
The functions target_encoding_mean(), target_encoding_rank(), and target_encoding_loo() have been simplified to the bare minimum, with all redundant logic moved to target_encoding_lab().
NA cases in the predictor to encode are now grouped under "NA".
The "rnorm" method has been deprecated, and the function target_encoding_rnorm() has been removed from the package.

Added the function cor_clusters() to group predictors using stats::hclust() based on their pairwise correlation matrix.
Streamlined the package documentation using roxygen methods to inherit sections and parameters.
Removed dplyr as a dependency.
Added mgcv, rpart, and ranger to Imports to support all f_xxx() functions from the start.
All warnings in data validation functions have been converted to messages. These messages now indicate the function that generated them, aiding in debugging and ensuring that messages and warnings are printed in the correct order.