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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)
<doi:10.48550/arXiv.2005.09918>). For methodological details, please refer to
Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020)
<doi:10.48550/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 
[ctb],
Gertraud Malsiner-Walli
[ctb],
Sylvia Frühwirth-Schnatter
[ctb] |
| Maintainer: |
Jan Greve <jan.greve at wu.ac.at> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
fipp results |
Documentation:
Downloads:
Linking:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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