From 976e7f1b111e39a2c0728f1b3c602284ce662582 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 23 Aug 2022 15:02:50 +0200 Subject: [PATCH] updating citation --- DESCRIPTION | 2 +- NEWS.md | 4 +++- README.Rmd | 2 +- README.md | 54 +++++++++++++++++------------------------------------ 4 files changed, 22 insertions(+), 40 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index a3f115b8..3322a4af 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Authors@R: c( person("François", "Gindraud", role = "aut", email = "francois.gindraud@gmail.com") ) Description: The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, - 2020 ) can be used for + 2021 ) 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 diff --git a/NEWS.md b/NEWS.md index 63eef837..d9ff9897 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,7 @@ -# PLNmodels dev +# PLNmodels 0.11.7 +* fix expression of ELBO in VEstep, related to #91 +* typos and regeneration of documentation( HTML5) * added an S3 method predict_cond to perform conditional predictions * fix #89 bug by forcing an intercept in `PLNLDA()` and changing `extract_model()` to conform with `model.frame()` diff --git a/README.Rmd b/README.Rmd index f918ac87..5438b89a 100644 --- a/README.Rmd +++ b/README.Rmd @@ -116,7 +116,7 @@ myPLNmixture <- PLNmixture(Abundance ~ 1, data = trichoptera) Please cite our work using the following references: -- J. Chiquet, M. Mariadassou and S. Robin: The Poisson-lognormal model as a versatile framework for the joint analysis of species abundances, bioRxiv preprint, 2020.. [link](https://doi.org/10.1101/2020.10.07.329383) +- 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. [link](https://www.frontiersin.org/articles/10.3389/fevo.2021.588292/full) - 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. [link](http://proceedings.mlr.press/v97/chiquet19a.html) diff --git a/README.md b/README.md index 84b83b0c..2e6c0b3a 100644 --- a/README.md +++ b/README.md @@ -7,19 +7,20 @@ status](https://github.com/pln-team/PLNmodels/workflows/R-CMD-check/badge.svg)](https://github.com/pln-team/PLNmodels/actions) [![Coverage status](https://codecov.io/gh/pln-team/PLNmodels/branch/master/graph/badge.svg)](https://codecov.io/github/pln-team/PLNmodels?branch=master) -[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/PLNmodels)](https://cran.r-project.org/package=PLNmodels) +[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/PLNmodels)](https://cran.r-project.org/package=PLNmodels) [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html) [![](https://img.shields.io/github/last-commit/pln-team/PLNmodels.svg)](https://github.com/pln-team/PLNmodels/commits/master) +[![R-CMD-check](https://github.com/PLN-team/PLNmodels/workflows/R-CMD-check/badge.svg)](https://github.com/PLN-team/PLNmodels/actions) -> The Poisson lognormal model and variants can be used for a variety -> of multivariate problems when count data are at play (including PCA, -> LDA and network inference for count data). This package implements +> The Poisson lognormal model and variants can be used for a variety of +> multivariate problems when count data are at play (including PCA, LDA +> and network inference for count data). This package implements > efficient algorithms to fit such models accompanied with a set of -> functions for visualization and diagnostic. See -> [this deck of slides](https://pln-team.github.io/slideshow/slides) -> for a comprehensive introduction. +> functions for visualization and diagnostic. See [this deck of +> slides](https://pln-team.github.io/slideshow/slides) for a +> comprehensive introduction. ## Installation @@ -30,58 +31,37 @@ version is available on [Github](https://github.com/pln-team/PLNmodels). ### R Package installation - - - - - - - - - - - - - - - #### Installing PLNmodels - - For the last stable version, use the CRAN version - - +- For the last stable version, use the CRAN version ``` r install.packages("PLNmodels") ``` - - For the development version, use the github install - - +- For the development version, use the github install ``` r remotes::install_github("pln-team/PLNmodels") ``` - - For a specific tagged release, use - - +- For a specific tagged release, use ``` r remotes::install_github("pln-team/PLNmodels@tag_number") @@ -138,16 +118,16 @@ myPLNmixture <- PLNmixture(Abundance ~ 1, data = trichoptera) Please cite our work using the following references: - - J. Chiquet, M. Mariadassou and S. Robin: The Poisson-lognormal model +- J. Chiquet, M. Mariadassou and S. Robin: The Poisson-lognormal model as a versatile framework for the joint analysis of species - abundances, bioRxiv preprint, 2020.. - [link](https://doi.org/10.1101/2020.10.07.329383) + abundances, Frontiers in Ecology and Evolution, 2021. + [link](https://www.frontiersin.org/articles/10.3389/fevo.2021.588292/full) - - J. Chiquet, M. Mariadassou and S. Robin: Variational inference for +- 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. [link](http://proceedings.mlr.press/v97/chiquet19a.html) - - J. Chiquet, M. Mariadassou and S. Robin: Variational inference for +- J. Chiquet, M. Mariadassou and S. Robin: Variational inference for probabilistic Poisson PCA, the Annals of Applied Statistics, 12: - 2674–2698, 2018. [link](https://dx.doi.org/10.1214/18-AOAS1177) + 2674–2698, 2018. [link](http://dx.doi.org/10.1214/18%2DAOAS1177)