Changes in version 2.0.0 (2026-04-10)                  

This is a major maintenance and interface-cleanup release prepared for
CRAN resubmission. The release focuses on code correctness,
documentation, naming consistency, and more predictable behavior for
core fitting, prediction, and visualization workflows.

Major changes

  - Refactored the package around a modular source layout while
    preserving the core ntree = 1 tree-based boosting path.
  - Standardized the public interface to lower-case, dotted naming
    conventions.
      - Families are now specified as "continuous", "binary", "nominal",
        and "ordinal".
      - Returned object components now use the same naming convention.
  - Added boostmtree.control() as the primary user-facing control
    interface for resampling, reproducibility, and other fitting
    options.
  - Clarified first-pass scope: the current refactor supports
    control$ntree = 1.

Fitting and model-object updates

  - Reworked preprocessing so that subject-level covariates,
    identifiers, times, and responses remain aligned throughout fitting.
  - Improved handling of non-continuous responses, including binary,
    nominal, and ordinal encoding.
  - Improved support for univariate fits when tm and id are omitted.
  - Restored the original tree-fitting logic more faithfully in key
    places while keeping the refactored package structure.
  - Simplified and regularized the structure of returned objects.
      - Internal code keeps the boosted-subproblem (q) representation.
      - Public objects flatten single-response results consistently for
        continuous and binary fits.
  - Removed legacy object components and hidden option paths that were
    no longer needed in the first-pass redesign.

Resampling, OOB, and cross-validation

  - Cleaned up the interaction between bootstrap sampling, OOB
    availability, cross-validation, and variable importance.
  - seed now controls reproducibility only.
  - Removed the silent legacy rewrite of bootstrap = "none" into a full
    in-bag user bootstrap.
  - When cv.flag = TRUE, the fit now enforces an OOB-producing
    resampling rule and records OOB availability in the fitted object.
  - Added explicit fitted-object fields documenting OOB behavior,
    including oob.available and oob.subject.count.

Prediction, printing, and plotting

  - Updated predict.boostmtree() to match the refactored object
    structure and naming conventions.
  - Improved prediction handling for:
      - held-out longitudinal test data,
      - new subjects evaluated on the fitted training time grid,
      - user-supplied common time grids via partial prediction.
  - Updated print.boostmtree() so grow and predict objects are
    summarized correctly and consistently.
  - Updated plot.boostmtree() so plotting now goes to the active
    graphics device by default.
      - PDF output is still available when explicitly requested.
      - Plotting data can also be returned for user-directed graphics
        workflows.

Variable importance and effect plots

  - Redesigned variable-importance output around a classed
    vimp.boostmtree object.
  - Replaced the old vimpPlot() workflow with plot() methods for
    variable-importance objects.
  - Added clearer checks for grow-object variable importance when OOB
    information is unavailable.
  - Reworked partial and marginal effect plotting around canonical
    function names:
      - partial.plot() / partial.plot.boostmtree()
      - marginal.plot() / marginal.plot.boostmtree()
  - Updated effect-plot functions to use the active graphics device by
    default instead of creating PDF files automatically.
  - Added response-label selection for partial and marginal plots in
    multi-level response settings.

Documentation

  - Rewrote the main boostmtree help page in a more user-facing style.
  - Expanded examples to cover:
      - continuous longitudinal fits,
      - binary fits,
      - univariate fits,
      - held-out prediction,
      - AF-data illustration,
      - variable importance,
      - partial and marginal plots.
  - Improved documentation for print, plot, predict, variable
    importance, and effect-plot methods.
  - Clarified the interpretation of phi, rho, lambda, mod.grad, and
    prediction outputs such as mu and muhat.
  - Updated package references to include later methodological and
    application papers:
      - Pande A., Ishwaran H., Blackstone E.H., Rajeswaran J., and
        Gillinov M. (2022). Application of gradient boosting in
        evaluating surgical ablation for atrial fibrillation. SN
        Computer Science, 3:466.
      - Pande A., Ishwaran H., and Blackstone E.H. (2022). Boosting for
        multivariate longitudinal responses. SN Computer Science, 3:186.

Backward-incompatible changes

  - The package now uses canonical lower-case, dotted names throughout
    the public interface.
  - Returned object components were renamed to match the new naming
    convention.
  - Legacy mixed-case and underscore-style argument names are no longer
    supported in the first-pass refactor.
  - Legacy plotting helpers such as vimpPlot(), partialPlot(), and
    marginalPlot() were replaced by classed objects and plot() methods
    or lower-case dotted interfaces.
  - Automatic PDF creation is no longer the default plotting behavior.

Internal cleanup and bug fixes

  - Removed conflicting and duplicated legacy helper code from
    utilities.R.
  - Fixed multiple issues uncovered during example-driven testing,
    including bugs affecting:
      - univariate fits,
      - longitudinal prediction,
      - predict-object printing,
      - grow/predict consistency for mu, muhat, and related fields,
      - partial-plot indexing,
      - compatibility between stored gamma representations used during
        prediction.
  - Removed legacy components no longer needed for the first-pass
    package design, including forest.tol.

                 Changes in version 1.0.0 (2016-03-17)                  

  - Initial CRAN submission