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.