Changes in version 0.5.0 (2026-03-19)                  

  - Addresses [#19]. When family = "multinomial" is specified,
    previously bolasso did not correctly support variable selection,
    plotting, etc. This version addresses this. In general, when family
    = "multinomial", all outputs become a list where each list element
    corresponds to a unique outcome in the multinomial response vector.
    However, the content of each list element will be the same as it
    originally was (e.g. a tibble of coefficients, or a vector of
    variable names, etc.). In the case of plotting, it will create a
    facetted plot with one facet per unique outcome. The only
    limitations are that family = "mgaussian" is not currently
    supported, and when predicting with family = "multinomial", only
    type = "class" is currently supported. These may be added at a later
    date.

                 Changes in version 0.4.0 (2025-10-14)                  

  - Allows the user to extract the bootstrap indices with
    bootstrap_samples().
    
    library(bolasso)
    
    model <- bolasso(mpg ~ hp + wt, data = mtcars, n.boot = 10)
    bootstrap_samples(model)

  - Fixes #12

  - Fixes #13

                 Changes in version 0.3.0 (2024-12-08)                  

New Features

  - Fast Estimation Mode:
    
      - bolasso() gains a fast argument which optimizes computation by
        using a single cross-validated regression on the entire dataset
        to determine the optimal regularization parameter (lambda). This
        approach bypasses the need for cross-validation within each
        bootstrap replicate, drastically reducing computation time,
        especially beneficial for large datasets or when using a high
        number of bootstrap replicates.
        # Fast mode reduces computation time by using a single cross-validated lambda
        model_fast <- bolasso(
        diabetes ~ .,
        data = train,
        n.boot = 1000,
        progress = FALSE,
        family = "binomial",
        fast = TRUE
        )

  - Enhanced Variable Selection Methods:
    
      - selected_vars() is now a shorthand for selected_variables().
      - selected_variables()/selected_vars() supports two variable
        selection algorithms via the method argument: the Variable
        Inclusion Probability (VIP) method and the Quantile (QNT)
        method. The VIP method selects variables that appear in a high
        percentage of bootstrap models, while the QNT method selects
        variables based on bootstrap confidence intervals. Set method =
        "vip" or method = "qnt", respectively.
        # Select variables using the VIP method with a 95% threshold
        selected_vars_vip <- selected_variables(model, threshold = 0.95, method = "vip")
        
        # Select variables using the QNT method
        selected_vars_qnt <- selected_variables(model, threshold = 0.95, method = "qnt")

  - Tidy Method for Bolasso Objects:
    
      - tidy() extracts a tidy tibble summarizing bootstrap-level
        coefficients for each covariate. This method provides a clean
        and organized way to inspect model coefficients.
        # Extract a tidy tibble of coefficients
        tidy_coefs <- tidy(model, select = "lambda.min")

  - Variable Selection Visualization:
    
      - plot_selection_thresholds() provides a visual representation of
        the selection thresholds for each variable. This visualization
        helps users understand the stability and robustness of variable
        selection across different thresholds and methods.
        # Visualize selection thresholds for variables
        plot_selection_thresholds(model, select = "lambda.min")

Improvements

  - Plotting Coefficient Distributions:
      - plot_selected_variables() visualizes the coefficient
        distributions for only the selected variables. This function
        provides a focused view of the most relevant variables in the
        model.
        
        # Plot coefficient distributions for selected variables
        plot_selected_variables(
        model,
        threshold = 0.95,
        method = "vip",
        select = "lambda.min"
        )

      - plot visualizes the coefficient distributions for all model
        covariates.
        
        # Plot coefficient distributions for selected variables
        plot(model, select = "lambda.min")

                 Changes in version 0.2.0 (2022-05-09)                  

  - Added a NEWS.md file to track changes to the package.
  - bolasso() argument form has been renamed to formula to reflect
    common naming conventions in R statistical modeling packages.
  - predict() and coef() methods are now implemented using
    future.apply::future_lapply allowing for computing predictions and
    extracting coefficients in parallel. This may result in slightly
    worse performance (due to memory overhead) when the model/prediction
    data is small but will be significantly faster when e.g. generating
    predictions on a very large data-set.
  - Solved an issue with bolasso() argument formula. The user-supplied
    value of formula is handled via deparse() which has a default
    width.cutoff value of 60. This was causing issues with formulas by
    splitting them into multi-element character vectors. It has now been
    set to the maximum value of 500L which will correctly parse all
    lengths of formulas.
  - predict() now forces evaluation of the formula argument in the
    bolasso() call. This resolves an issue where, if a user passes a
    formula via a variable, predict() would pass the variable name to
    the underlying prediction function as opposed to the actual formula.