Package: PDEnaiveBayes 0.2.9

PDEnaiveBayes: Plausible Naive Bayes Classifier Using PDE

A nonparametric, multicore-capable plausible naive Bayes classifier based on the Pareto density estimation (PDE) featuring a plausible approach to a pitfall in the Bayesian theorem covering low evidence cases. Stier, Q., Hoffmann, J., and Thrun, M.C.: "Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naive Bayes" (2026), Machine Learning and Knowledge Extraction (MAKE), <doi:10.3390/make8010013>.

Authors:Michael Thrun [aut, cph, cre], Quirin Stier [aut, rev], Tim Robin Neldner [ctr, ctb]

PDEnaiveBayes_0.2.9.tar.gz
PDEnaiveBayes_0.2.9.zip(r-4.7)PDEnaiveBayes_0.2.9.zip(r-4.6)PDEnaiveBayes_0.2.9.zip(r-4.5)
PDEnaiveBayes_0.2.9.tgz(r-4.6-x86_64)PDEnaiveBayes_0.2.9.tgz(r-4.6-arm64)PDEnaiveBayes_0.2.9.tgz(r-4.5-x86_64)PDEnaiveBayes_0.2.9.tgz(r-4.5-arm64)
PDEnaiveBayes_0.2.9.tar.gz(r-4.7-arm64)PDEnaiveBayes_0.2.9.tar.gz(r-4.7-x86_64)PDEnaiveBayes_0.2.9.tar.gz(r-4.6-arm64)PDEnaiveBayes_0.2.9.tar.gz(r-4.6-x86_64)
PDEnaiveBayes_0.2.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
PDEnaiveBayes/json (API)

# Install 'PDEnaiveBayes' in R:

install.packages('PDEnaiveBayes', repos = c('https://mthrun.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mthrun/pdebayes/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

Hepta - Hepta introduced in [Ultsch, 2003]

On CRAN:

cpp

4.30 score 1 scripts 471 downloads 11 exports 73 dependencies

Last updated from:2d34f6b83a. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	168
linux-devel-x86_64	OK	174
source / vignettes	OK	317
linux-release-arm64	OK	165
linux-release-x86_64	OK	176
macos-release-arm64	OK	122
macos-release-x86_64	OK	393
macos-oldrel-arm64	OK	107
macos-oldrel-x86_64	OK	293
windows-devel	OK	129
windows-release	OK	143
windows-oldrel	OK	126
wasm-release	OK	134

Exports:ApplyBayesTheorem4Likelihoods getPriors PlotBayesianDecision2D PlotLikelihoodFuns PlotLikelihoods PlotNaiveBayes PlotPosteriors Predict_naiveBayes predict.PDEbayes Train_naiveBayes Train_naiveBayes_multicore

Dependencies:ABCanalysis askpass base64enc bslib cachem cli cpp11 crosstalk curl data.table DatabionicSwarm deldir digest dplyr evaluate farver fastmap fontawesome fs GeneralizedUmatrix generics ggplot2 glue gtable highr htmltools htmlwidgets httr isoband jquerylib jsonlite knitr labeling later lazyeval lifecycle magrittr memoise memshare mime openssl otel pillar pkgconfig plotly plotrix pracma promises purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppParallel rlang rmarkdown S7 sass scales stringi stringr sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml

An Introduction to PDE naive Bayes (PDENB)

Michael C. Thrun

Rendered fromPDEnaiveBayes.Rmdusingknitr::rmarkdownon Jun 03 2026.

Last update: 2026-01-09
Started: 2026-01-09

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Plausible Naive Bayes Classifier Using PDE	PDEnaiveBayes-package
ApplyBayesTheorem4Likelihoods	ApplyBayesTheorem4Likelihoods
defineOrEstimateDistribution	defineOrEstimateDistribution
fitParameters	fitParameters
GetLikelihoods	GetLikelihoods
getPriors	getPriors
Hepta introduced in [Ultsch, 2003]	Hepta
PlotBayesianDecision2D	PlotBayesianDecision2D
PlotLikelihoodFuns	PlotLikelihoodFuns
PlotLikelihoods	PlotLikelihoods
PlotNaiveBayes	PlotNaiveBayes
PlotPosteriors	PlotPosteriors
Predict_naiveBayes	Predict_naiveBayes
predict.PDEbayes	predict.PDEbayes
Train_naiveBayes	Train_naiveBayes
Train_naiveBayes_multicore	Train_naiveBayes_multicore