Simulated training data | Scenario 1: independent beta variables | Scenario 2: sinusoidal ridge | Fit the ensembles | Inspect fitted objects | Evaluate the fitted densities | Generate and compare samples | Practical notes | References
Package: boostPM 0.1.0
boostPM: Unsupervised Tree Boosting for Learning Probability Distributions
Implements the unsupervised tree boosting method for learning probability distributions introduced by Awaya and Ma (2024). Provides model fitting, density evaluation, simulation, and diagnostic methods.
Authors:
boostPM_0.1.0.tar.gz
boostPM_0.1.0.zip(r-4.7-x86_64)boostPM_0.1.0.zip(r-4.7-arm64)boostPM_0.1.0.zip(r-4.6-x86_64)boostPM_0.1.0.zip(r-4.6-arm64)boostPM_0.1.0.zip(r-4.5-x86_64)
boostPM_0.1.0.tgz(r-4.6-x86_64)boostPM_0.1.0.tgz(r-4.6-arm64)boostPM_0.1.0.tgz(r-4.5-x86_64)boostPM_0.1.0.tgz(r-4.5-arm64)
boostPM_0.1.0.tar.gz(r-4.7-arm64)boostPM_0.1.0.tar.gz(r-4.7-x86_64)boostPM_0.1.0.tar.gz(r-4.6-arm64)boostPM_0.1.0.tar.gz(r-4.6-x86_64)
boostPM_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
boostPM/json (API)
| # Install 'boostPM' in R: |
| install.packages('boostPM', repos = c('https://nawaya040.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/nawaya040/boostpm-cran/issues
Last updated from:8b015901cc. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 116 | ||
| linux-devel-x86_64 | OK | 139 | ||
| source / vignettes | OK | 219 | ||
| linux-release-arm64 | OK | 112 | ||
| linux-release-x86_64 | OK | 130 | ||
| macos-release-arm64 | OK | 158 | ||
| macos-release-x86_64 | OK | 289 | ||
| macos-oldrel-arm64 | OK | 237 | ||
| macos-oldrel-x86_64 | OK | 445 | ||
| windows-devel-arm64 | OK | 410 | ||
| windows-devel-x86_64 | OK | 157 | ||
| windows-release-arm64 | OK | 421 | ||
| windows-release-x86_64 | OK | 148 | ||
| windows-oldrel-x86_64 | OK | 89 | ||
| wasm-release | OK | 109 |
Exports:fit_boostpm
Dependencies:RcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| boostPM: Unsupervised Tree Boosting for Learning Probability Distributions | boostPM-package boostPM |
| Fit a boostPM Distribution | fit_boostpm |
| Plot Diagnostics for a Fitted boostPM Distribution | plot.boostPM_fit |
| Evaluate a Fitted boostPM Density | predict.boostPM_fit |
| Print a Fitted boostPM Distribution | print.boostPM_fit |
| Print a boostPM Fit Summary | print.summary.boostPM_fit |
| Simulate from a Fitted boostPM Distribution | simulate.boostPM_fit |
| Summarize a Fitted boostPM Distribution | summary.boostPM_fit |