boostPM

boostPM fits probability distributions with unsupervised tree boosting. The method represents a density as an ensemble of tree-based probability measures, allows analytic density evaluation, and provides a sampler from the fitted distribution.

This repository is an in-development, CRAN-oriented package implementation. The immutable research implementation used for the published work is retained under original/, and documented regression cases are compared against it.

Installation

The package is not on CRAN yet. Install a development checkout with:

install.packages("/path/to/boostPM-cran", repos = NULL, type = "source")

Once the repository is public, a GitHub installation route can be used with remotes::install_github("nawaya040/boostPM-cran").

A small example

library(boostPM)

set.seed(1)
data <- cbind(
  rbeta(80, shape1 = 2, shape2 = 5),
  rbeta(80, shape1 = 5, shape2 = 2)
)

support <- cbind(c(0, 0), c(1, 1))

set.seed(2)
fit <- fit_boostpm(
  data,
  Omega = support,
  max_marginal_trees = 2,
  max_dependence_trees = 2,
  n_bins = 4,
  max_split_depth = 1,
  min_node_observations = 2,
  c0 = 0.1,
  gamma = 0.5,
  add_noise = FALSE
)

log_density <- predict(fit, data, type = "log_density")
exp(log_density)

set.seed(3)
simulated <- simulate(fit, nsim = 10)

The fit is a boostPM_fit object whose components include serialized trees, residual coordinates, per-tree and held-out diagnostics, support, and variable importance.

Inspect a fitted model

fit_boostpm() returns silently. Use the S3 methods to inspect the fit and its diagnostics.

print(fit)
summary(fit)
plot(fit, type = "variable_importance")

The plot method also supports type = "tree_node_counts" and type = "tree_depths".

Input and reproducibility notes

• data must be a finite numeric matrix with observations in rows.
• Constant columns are rejected.
• A supplied Omega has one lower and upper bound per variable. Training data must be strictly inside those bounds.
• predict(fit, newdata, type = "log_density") returns log density. Evaluation outside Omega returns -Inf.
• Fits and simulation use R’s random-number generator. Call set.seed() before each stochastic operation when reproducibility is needed.

The introductory vignette is available after installation:

vignette("boostPM-introduction", package = "boostPM")

Citation

Please cite the method paper:

Awaya, N. and Ma, L. (2024). Unsupervised Tree Boosting for Learning Probability Distributions. Journal of Machine Learning Research, 25, 1-52.

Use citation("boostPM") for the installed-package citation record.

Development and validation

Routine checks run on Windows, macOS, and Ubuntu through GitHub Actions. Original-versus-package regression and longer statistical validation runners live under validation/. Their results and performance records are documented under docs/ and benchmarks/.