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ppforest2 0.1.2

CRAN

• Fixed a -Wdeprecated-declarations warning reported by CRAN’s macOS/M1mac additional check (Apple clang 21, macOS 26 SDK). The newer libc++ deprecates std::char_traits<unsigned char>, which the vendored nlohmann/json instantiates through its binary output/stream adapters (std::basic_string<std::uint8_t> / std::basic_ostream<std::uint8_t>). ppforest2 does not use nlohmann’s binary formats, so the vendored json.hpp is now bracketed with a _Pragma guard that suppresses the deprecation. _Pragma (unlike #pragma) is not flagged by R CMD check’s pragma check. The guard is applied by make r-vendor-deps (scripts/vendor-guard-json.sh).

ppforest2 0.1.1

Packaging and build

• R: The R package now compiles the C++ core directly through Makevars instead of CMake, with no network access or downloaded dependencies at install time. Eigen is provided by RcppEigen; nlohmann/json and pcg headers are vendored under inst/include. This makes the package installable on CRAN’s offline build machines. (fmt and csv-parser, used only by the CLI, are no longer part of the R build.)
• R: Self-registering strategies are compiled directly into the shared object, removing the previous whole-archive linking workaround.
• R: Compiler flags for the direct build mirror the standalone C++ build — EIGEN_NO_AUTOMATIC_RESIZING on all platforms and EIGEN_DONT_VECTORIZE on Windows.
• Core: Replaced C++20 designated initializers and fixed member-initialization order in stats/GroupPartition and stats/Simulation so the code compiles warning-free under a strict C++17 GCC (-Wall -Wextra -pedantic).
• Core: A compile-time EIGEN_VERSION_AT_LEAST(3, 4, 0) guard fails the build with a clear message if an incompatible Eigen is supplied (e.g. via RcppEigen).
• make r-vendor-deps re-vendors the committed json/pcg headers after a version bump in core/Dependencies.cmake.

Documentation

• R: DESCRIPTION uses Authors@R and cites the projection-pursuit tree and forest references with DOIs.
• R: Examples for the parsnip and plot methods use \donttest with requireNamespace() guards instead of \dontrun, so they run under --run-donttest when the suggested packages are available.

CRAN

• Added cran-comments.md. The package passes R CMD check --as-cran with no errors or warnings; remaining notes (new submission, cosmetic pragmas in the vendored nlohmann/json headers) are documented for the reviewer.

ppforest2 0.1.0

New features

• Core: Projection-pursuit oblique decision trees and random forests for classification, using LDA/PDA optimization.
• Core: Random uniform variable selection per split for forest diversity.
• Core: Three variable importance measures: permuted (VI1), projections (VI2), and weighted projections (VI3).
• Core: Out-of-bag error and confusion matrix for forests, with bootstrap sample indices persisted for recomputation. oob_error is NA_real_ (R) / “not available” (CLI) when no observation has any out-of-bag tree.
• Core: Degenerate split detection when projection-pursuit cannot find a useful projection.
• Core: OpenMP multi-threaded forest training.
• Core: JSON serialization for trained models. Optional metrics fields use a uniform null-or-value representation so downstream tooling can distinguish “computed but empty” from other shapes without special-casing.
• Core: Cross-platform reproducibility — identical results for the same seed on Linux, macOS, and Windows, enforced by golden-file tests in CI.
• R: pptr() and pprf() with formula and matrix interfaces. Returned models carry an S3 class vector identifying both model type and mode (e.g. c("pprf_classification", "pprf", "ppmodel")).
• R: predict() returns group labels (type = "class") or vote proportions (type = "prob") for classification.
• R: summary() displays training and OOB confusion matrices.
• R: Lazy OOB accessors — oob_error(), oob_predictions(), oob_samples(), bag_samples(), permuted_importance(), weighted_importance() — compute from the training data stored on the model on first access and memoize in an environment cache, so training is fast and repeated access is free. oob_predictions() returns a factor with NA for rows with no OOB tree.
• R: Permuted variable importance may be negative; this is meaningful signal (“within noise”) rather than a sentinel, so callers should rely on the ranking rather than clipping at zero. Weighted projection importance is non-negative by construction.
• R: Degenerate split warnings when projection-pursuit cannot separate groups.
• R: save_json() and load_json() for model persistence.
• R: tidymodels/parsnip integration: pp_tree() and pp_rand_forest() model specifications.
• R: ggplot2 visualizations — tree diagrams, variable importance plots, projection histograms, and decision boundary plots.
• R: Bundled classification datasets: crab, crabs, fishcatch, glass, image, leukemia, lymphoma, NCI60, olive, parkinson, and wine. (Use datasets::iris from base R for iris examples.)
• CLI: train fits a tree or forest from CSV and saves as JSON.
• CLI: predict applies a saved model to new data.
• CLI: evaluate runs train/test evaluation with smart convergence.
• CLI: summarize displays model configuration, data summary, and metrics from a saved model JSON. --data recomputes metrics from training data.
• CLI: benchmark runs multi-scenario performance benchmarks with baseline comparison.
• CLI: serve exposes a saved model over HTTP — GET / returns the model summary as JSON (or an HTML dashboard for browsers showing configuration, training metrics, and variable importance), GET /health is a liveness probe, and POST /predict accepts a feature CSV and returns predictions (JSON for API clients, an HTML predictions page for browsers, with a Download CSV button and confusion matrix when the request CSV includes a response column). Results are cached in-memory with shareable ?id=… URLs; the dashboard binds to 127.0.0.1:8080 by default.

Experimental features

Regression support is included but untested in production workloads. API surface and defaults may change in future releases.

• Core: Regression training via a ByCutpoint grouping strategy that quantile-slices the continuous response, a MeanResponse leaf, and MinSize / MinVariance / CompositeStop (stop::any) stop rules.
• Core: Regression metrics — MSE, MAE, and R² — computed for training and out-of-bag predictions. Forest OOB error is reported as MSE.
• R: Regression auto-detected when y is numeric (not a factor). predict() returns a numeric vector (type = "response").
• R: Regression strategy wrappers: grouping_by_cutpoint(), leaf_mean_response(), stop_min_size(), stop_min_variance(), stop_any().
• R: summary() displays MSE / MAE / R² for regression models. oob_predictions() returns a numeric vector with NA_real_ for rows with no OOB tree.
• R: save_json() / load_json() preserve regression mode; parsnip pp_tree() / pp_rand_forest() accept mode = "regression".
• CLI: --mode classification|regression selects the training mode; regression reads the last CSV column as the continuous response. predict returns numeric predictions and MSE/MAE/R²; evaluate reports MSE for regression.
• R: Bundled regression dataset california_housing (20,433 × 9, predict median_house_value). For smaller regression examples use datasets::mtcars from base R.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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