plnmodels

A collection of Poisson lognormal models for multivariate count data analysis

https://github.com/pln-team/plnmodels

Science Score: 59.0%

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Links to: frontiersin.org
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Low similarity (16.0%) to scientific vocabulary

Keywords

count-data multivariate-analysis network-inference pca poisson-lognormal-model r-package

Keywords from Contributors

bucket-sort clustering-comparison-measures

Last synced: 6 months ago · JSON representation

Repository

A collection of Poisson lognormal models for multivariate count data analysis

Basic Info

Host: GitHub
Owner: PLN-team
License: gpl-3.0
Language: R
Default Branch: master
Homepage: https://pln-team.github.io/PLNmodels
Size: 111 MB

Statistics

Stars: 57
Watchers: 5
Forks: 18
Open Issues: 9
Releases: 21

Topics

count-data multivariate-analysis network-inference pca poisson-lognormal-model r-package

Created almost 9 years ago · Last pushed 7 months ago

Metadata Files

Readme Changelog License Authors

README.Rmd

---
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  fig.path = "man/figures/"
)
```

# PLNmodels: Poisson lognormal models 


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> 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.

## Installation

**PLNmodels** is available on [CRAN](https://cran.r-project.org/package=PLNmodels). The development 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

```{r package CRAN, eval = FALSE}
install.packages("PLNmodels")
```

- For the development version, use the github install

```{r package github, eval = FALSE}
remotes::install_github("pln-team/PLNmodels")
```

- For a specific tagged release, use

```{r package tag, eval = FALSE}
remotes::install_github("pln-team/PLNmodels@tag_number")
```

## Usage and main fitting functions

The package comes with an ecological data set to present the functionality 

```{r load PLNmodels, eval = FALSE}
library(PLNmodels)
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
```

The main fitting functions work with the usual `R formula` notations, with mutivariate responses on the left hand side. You probably want to start by one of them. Check the corresponding vignette and documentation page. There is a dedicated vignettes for each model in the package (See  https://pln-team.github.io/PLNmodels/articles/).

### Unpenalized Poisson lognormal model (aka PLN)

```{r PLN, eval = FALSE}
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
```

### Rank Constrained Poisson lognormal for Poisson Principal Component Analysis (aka PLNPCA)

```{r PLNPCA, eval = FALSE}
myPCA <- PLNPCA(Abundance ~ 1, data = trichoptera, ranks = 1:8)
```

### Poisson lognormal discriminant analysis (aka PLNLDA)

```{r PLNLDA, eval = FALSE}
myLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera)
```

### Sparse Poisson lognormal model for sparse covariance inference for counts (aka PLNnetwork)

```{r PLNnetwork, eval = FALSE}
myPLNnetwork <- PLNnetwork(Abundance ~ 1, data = trichoptera)
```

### Mixture of Poisson lognormal models for model-based clustering of counts  (aka PLNmixture)

```{r PLNmixture, eval = FALSE}
myPLNmixture <- PLNmixture(Abundance ~ 1, data = trichoptera)
```

## References

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, 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)

- J. Chiquet, M. Mariadassou and S. Robin: Variational inference for probabilistic Poisson PCA, the Annals of Applied Statistics, 12: 2674–2698, 2018. [link](http://dx.doi.org/10.1214/18%2DAOAS1177)

Owner

Name: PLN team
Login: PLN-team
Kind: organization
Location: Paris area, France

Repositories: 2
Profile: https://github.com/PLN-team

Regroup and store repositories around our work on the Poisson lognormal model (Code, packages, new development, material for formation)

GitHub Events

Total

Create event: 7
Release event: 1
Issues event: 10
Watch event: 2
Delete event: 6
Issue comment event: 13
Push event: 45
Pull request review event: 2
Pull request event: 12
Fork event: 1

Last Year

Create event: 7
Release event: 1
Issues event: 10
Watch event: 2
Delete event: 6
Issue comment event: 13
Push event: 45
Pull request review event: 2
Pull request event: 12
Fork event: 1

Committers

Last synced: over 2 years ago

All Time

Total Commits: 1,510
Total Committers: 10
Avg Commits per committer: 151.0
Development Distribution Score (DDS): 0.539

Past Year

Commits: 201
Committers: 4
Avg Commits per committer: 50.25
Development Distribution Score (DDS): 0.249

Top Committers

Name	Email	Commits
Julien Chiquet	j**t@g**m	696
mahendra-mariadassou	m**u@g**m	346
Julien Chiquet	j**t@i**r	214
Julien Chiquet	j**t@i**r	204
François Gindraud	f**d@g**m	32
julieaubert	j**t@a**r	9
Francois Gindraud	f**d@u**r	3
giopogg	4****g	3
Julien Chiquet	j****t	2
bastien-mva	b**e@g**m	1

Committer Domains (Top 20 + Academic)

univ-lyon1.fr: 1 agroparistech.fr: 1 inra.fr: 1 inrae.fr: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 110
Total pull requests: 28
Average time to close issues: 3 months
Average time to close pull requests: 8 days
Total issue authors: 33
Total pull request authors: 6
Average comments per issue: 2.12
Average comments per pull request: 0.57
Merged pull requests: 27
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 4
Pull requests: 6
Average time to close issues: 8 days
Average time to close pull requests: 18 days
Issue authors: 4
Pull request authors: 2
Average comments per issue: 0.75
Average comments per pull request: 0.0
Merged pull requests: 6
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

jchiquet (41)
mahendra-mariadassou (19)
scj-robin (8)
cpauvert (7)
BenoitLondon (6)
brgew (2)
wrshoemaker (2)
akhst7 (1)
fplaza (1)
ivangalvan (1)
a-velt (1)
Ananyapam7 (1)
GMBog (1)
ctrapnell (1)
dcalderon (1)

Pull Request Authors

jchiquet (26)
mahendra-mariadassou (10)
Bastien-mva (4)
julieaubert (1)
brgew (1)
giopogg (1)

Top Labels

Issue Labels

enhancement (22) bug (9) correctness (7) feature (6) good practices (4) wontfix (4) PLNmixture (4) performance (3) documentation (2) question (1) Mac OS (1)

Pull Request Labels

enhancement (3) feature (3) bug (1)

Packages

Total packages: 2
Total downloads:
- cran 523 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 1
(may contain duplicates)
Total versions: 24
Total maintainers: 1

cran.r-project.org: PLNmodels

Poisson Lognormal Models

Homepage: https://pln-team.github.io/PLNmodels/
Documentation: http://cran.r-project.org/web/packages/PLNmodels/PLNmodels.pdf
License: GPL (≥ 3)
Latest release: 1.2.2
published 11 months ago

Versions: 19
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 523 Last month

Rankings

Forks count: 4.2%

Stargazers count: 6.8%

Average: 17.5%

Downloads: 23.7%

Dependent repos count: 24.7%

Dependent packages count: 28.4%

Maintainers (1)

julien.chiquet@inrae.fr

Last synced: 6 months ago

conda-forge.org: r-plnmodels

Homepage: https://pln-team.github.io/PLNmodels/
License: GPL-3.0-or-later
Latest release: 0.11.7
published over 3 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 34.0%

Average: 42.6%

Dependent packages count: 51.2%

Last synced: 6 months ago

Dependencies

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.github/workflows/test-coverage.yaml actions

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DESCRIPTION cran

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