PEkit: Partition Exchangeability Toolkit

Bayesian supervised predictive classifiers, hypothesis testing, and parametric estimation under Partition Exchangeability are implemented. The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities.

Imports: stats (≥ 4.1.0)
Suggests: testthat (≥ 3.0.0)
Published: 2021-11-22
Author: Ville Kinnula [aut], Jing Tang ORCID iD [ctb], Ali Amiryousefi ORCID iD [aut, cre]
Maintainer: Ali Amiryousefi <ali.amiryousefi at>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: PEkit results


Reference manual: PEkit.pdf


Package source: PEkit_1.0.0.1000.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
macOS binaries: r-release (arm64): PEkit_1.0.0.1000.tgz, r-release (x86_64): PEkit_1.0.0.1000.tgz, r-oldrel: not available


Please use the canonical form to link to this page.