fipp: Induced Priors in Bayesian Mixture Models

Computes implicitly induced quantities from prior/hyperparameter specifications of three Mixtures of Finite Mixtures models: Dirichlet Process Mixtures (DPMs; Escobar and West (1995) <doi:10.1080/01621459.1995.10476550>), Static Mixtures of Finite Mixtures (Static MFMs; Miller and Harrison (2018) <doi:10.1080/01621459.2016.1255636>), and Dynamic Mixtures of Finite Mixtures (Dynamic MFMs; Frühwirth-Schnatter, Malsiner-Walli and Grün (2020) <arXiv:2005.09918>). For methodological details, please refer to Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020) <arXiv:2012.12337>) as well as the package vignette.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: Rcpp, stats, matrixStats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2021-02-11
Author: Jan Greve [aut, cre], Bettina Grün ORCID iD [ctb], Gertraud Malsiner-Walli ORCID iD [ctb], Sylvia Frühwirth-Schnatter ORCID iD [ctb]
Maintainer: Jan Greve <jan.greve at>
License: GPL-2
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: fipp results


Reference manual: fipp.pdf
Vignettes: fipp Crash Course
Package source: fipp_1.0.0.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release: fipp_1.0.0.tgz, r-oldrel: fipp_1.0.0.tgz


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