boostmtree

• 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.