Package: PLNmodels 1.2.2

PLNmodels: Poisson Lognormal Models
The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.
Authors:
PLNmodels_1.2.2.tar.gz
PLNmodels_1.2.2.tar.gz(r-4.5-noble)PLNmodels_1.2.2.tar.gz(r-4.4-noble)
PLNmodels_1.2.2.tgz(r-4.4-emscripten)PLNmodels_1.2.2.tgz(r-4.3-emscripten)
PLNmodels.pdf |PLNmodels.html✨
PLNmodels/json (API)
NEWS
# Install 'PLNmodels' in R: |
install.packages('PLNmodels', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/pln-team/plnmodels/issues11 issues
Pkgdown site:https://pln-team.github.io
- barents - Barents fish data set
- mollusk - Mollusk data set
- oaks - Oaks amplicon data set
- scRNA - Single cell RNA-seq data
- trichoptera - Trichoptera data set
Conda:r-plnmodels-1.2.2(2025-03-25)
Last updated 13 days agofrom:a5d7121bfe. Checks:3 OK. Indexed: no.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 21 2025 |
R-4.5-linux-x86_64 | OK | Mar 21 2025 |
R-4.4-linux-x86_64 | OK | Mar 21 2025 |
Exports:%>%coefficient_pathcompute_offsetcompute_PLN_starting_pointextract_probsgetBestModelgetModelPLNPLN_paramPLNLDAPLNLDA_paramPLNmixturePLNmixture_paramPLNnetworkPLNnetwork_paramPLNPCAPLNPCA_parampredict_condprepare_datarPLNstability_selectionstandard_errorZIPLNZIPLN_paramZIPLNnetworkZIPLNnetwork_param
Dependencies:bitbit64callrclicodetoolscolorspacecorocorrplotcpp11descdigestdplyrfansifarverfuturefuture.applygenericsggplot2glassoFastglobalsgluegridExtragtableigraphisobandjsonlitelabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmenloptrparallellypillarpkgconfigprocessxpspsclpurrrR6RColorBrewerRcppRcppArmadillorlangsafetensorsscalesstringistringrtibbletidyrtidyselecttorchutf8vctrsviridisLitewithr
Analyzing multivariate count data with the Poisson log-normal model
Rendered fromPLN.Rmd
usingknitr::rmarkdown
on Mar 21 2025.Last update: 2023-08-24
Started: 2019-05-17
Clustering of multivariate count data with PLN-mixture
Rendered fromPLNmixture.Rmd
usingknitr::rmarkdown
on Mar 21 2025.Last update: 2023-01-06
Started: 2021-03-16
Description of the Trichoptera data set
Rendered fromTrichoptera.Rmd
usingknitr::rmarkdown
on Mar 21 2025.Last update: 2023-01-06
Started: 2019-05-17
Dimension reduction of multivariate count data with PLN-PCA
Rendered fromPLNPCA.Rmd
usingknitr::rmarkdown
on Mar 21 2025.Last update: 2023-01-06
Started: 2019-05-17
Data importation in PLNmodels
Rendered fromImport_data.Rmd
usingknitr::rmarkdown
on Mar 21 2025.Last update: 2024-01-09
Started: 2019-05-17
Sparse structure estimation for multivariate count data with PLN-network
Rendered fromPLNnetwork.Rmd
usingknitr::rmarkdown
on Mar 21 2025.Last update: 2023-01-06
Started: 2019-05-17
Supervized classification of multivariate count table with the Poisson discriminant Analysis
Rendered fromPLNLDA.Rmd
usingknitr::rmarkdown
on Mar 21 2025.Last update: 2023-01-06
Started: 2019-05-17
Citation
To cite PLNmodels (either PLNPCA or PLNnetwork) in publications, please use:
J. Chiquet, M. Mariadassou and S. Robin: The Poisson-lognormal model as a versatile framework for the joint analysis of species abundances, Frontiers in Ecology and Evolution, 2021.
J. Chiquet, M. Mariadassou and S. Robin: Variational inference for sparse network reconstruction from count data, Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.
J. Chiquet, M. Mariadassou and S. Robin: Variational inference for probabilistic Poisson PCA, the Annals of Applied Statistics, 2018.
Corresponding BibTeX entries:
@Article{PLNmodels, author = {Julien Chiquet and Mahendra Mariadassou and Stéphane Robin}, title = {The Poisson-lognormal model as a versatile framework for the joint analysis of species abundances}, journal = {Frontiers in Ecology and Evolution}, year = {2021}, doi = {10.3389/fevo.2021.588292}, url = {https://www.frontiersin.org/articles/10.3389/fevo.2021.588292}, }
@InProceedings{PLNnetwork, author = {Julien Chiquet and Mahendra Mariadassou and Stéphane Robin}, title = {Variational inference for sparse network reconstruction from count data}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, year = {2019}, volume = {97}, series = {Proceedings of Machine Learning Research}, url = {http://proceedings.mlr.press/v97/chiquet19a.html}, }
@Article{PLNPCA, author = {Julien Chiquet and Mahendra Mariadassou and Stéphane Robin}, title = {Variational inference for probabilistic Poisson PCA}, journal = {The Annals of Applied Statistics}, year = {2018}, volume = {12}, pages = {2674--2698}, url = {https://projecteuclid.org/journals/annals-of-applied-statistics/volume-12/issue-4/Variational-inference-for-probabilistic-Poisson-PCA/10.1214/18-AOAS1177.full}, }