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QGglmm

Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.

https://github.com/devillemereuil/qgglmm

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 7 committers (14.3%) from academic institutions
  • ○
    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.0%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.

Basic Info
  • Host: GitHub
  • Owner: devillemereuil
  • Language: TeX
  • Default Branch: master
  • Homepage:
  • Size: 3.53 MB
Statistics
  • Stars: 16
  • Watchers: 4
  • Forks: 3
  • Open Issues: 2
  • Releases: 9
Created over 11 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog

README.md

QGglmm

CRAN_Status_Badge

NEWS

Due to its removal from CRAN, QGglmm dropped R2Cuba as a dependency to solve multivariate integrals. It is now using the package cubature. By taking advantage of the "vectorised" version of the algorithm, the multivariate computations of QGglmm (QGmvparams, QGvcov, QGmvmean, QGmvpsi, QGmvicc, QGmvpred) are considerably faster. Most functions are 10x-50x faster, but especially QGmvicc is 100x-500x faster. A comparison between the old and new version of the example of the man page of QGmvicc showed a decreased in computation from 25 minutes to... 4 seconds!

What is this package?

QGglmm computes various quantitative genetics parameters on the observed data scale from latent parameters estimated using a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the phenotypic mean, phenotypic variance and additive genetic variance on the observed data scale.

More information can be found in this article and on CRAN.

How to install this package

Using CRAN

  • Simply use install.packages("QGglmm") as for any package.

From this GitHub

  • Install the packages on which QGglmm depends: cubature and mvtnorm. install.packages(c("cubature","mvtnorm"))
  • Go to the release page and download the latest release.
  • In a terminal, go to the folder where the release was downloaded and enter the following line:
    R CMD INSTALL QGglmm-xx.tar.gz where xx is the version number.
  • Alternatively, you can use the graphical tools of R-GUI or RStudio to manually install the package after download. For RStudio, this can be done using "Install Packages..." in the Tools menu, choosing "Install from: Package Archive File".

Submit feedback

If you encounter any bug or usability issue, or if you have some suggestions or feature request, please use the issue tracker. Thank you!

Owner

  • Login: devillemereuil
  • Kind: user

GitHub Events

Total
  • Create event: 1
  • Issues event: 2
  • Release event: 1
  • Watch event: 2
  • Delete event: 1
  • Issue comment event: 5
  • Push event: 6
  • Pull request event: 2
Last Year
  • Create event: 1
  • Issues event: 2
  • Release event: 1
  • Watch event: 2
  • Delete event: 1
  • Issue comment event: 5
  • Push event: 6
  • Pull request event: 2

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 141
  • Total Committers: 7
  • Avg Commits per committer: 20.143
  • Development Distribution Score (DDS): 0.362
Past Year
  • Commits: 2
  • Committers: 2
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.5
Top Committers
Name Email Commits
devillemereuil b****y@h****r 90
Pierre de Villemereuil p****l@m****g 29
devillemereuil d****l 13
Pierre de Villemereuil p****e@d****r 4
Pierre de Villemereuil f****s@F****e 3
Tom Ellis e****s 1
Pierre de Villemereuil p****l@e****u 1
Committer Domains (Top 20 + Academic)
ephe.psl.eu: 1 flyosflip.home: 1 dse601ep.mnhn.fr: 1 mailoo.org: 1 horus.ens.fr: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 2
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 2 minutes
  • Total issue authors: 2
  • Total pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 2 minutes
  • Issue authors: 2
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • gtbil (1)
  • qdread (1)
Pull Request Authors
  • devillemereuil (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 232 last-month
  • Total docker downloads: 21,613
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 9
  • Total maintainers: 1
cran.r-project.org: QGglmm

Estimate Quantitative Genetics Parameters from Generalised Linear Mixed Models

  • Versions: 9
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 232 Last month
  • Docker Downloads: 21,613
Rankings
Forks count: 14.9%
Stargazers count: 15.6%
Average: 28.3%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Downloads: 45.7%
Maintainers (1)
pierre.de-villemereuil@mnhn.fr
Last synced: 10 months ago

Dependencies

DESCRIPTION cran
  • cubature >= 1.4 imports