Package: FAMoS 0.3.1

FAMoS: A Flexible Algorithm for Model Selection

Given a set of parameters describing model dynamics and a corresponding cost function, FAMoS performs a dynamic forward-backward model selection on a specified selection criterion. It also applies a non-local swap search method. Works on any cost function. For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.

Authors:Michael Gabel [aut, cre], Tobias Hohl [aut]

FAMoS_0.3.1.tar.gz
FAMoS_0.3.1.zip(r-4.7-any)FAMoS_0.3.1.zip(r-4.6-any)FAMoS_0.3.1.zip(r-4.5-any)
FAMoS_0.3.1.tgz(r-4.6-any)FAMoS_0.3.1.tgz(r-4.5-any)
FAMoS_0.3.1.tar.gz(r-4.7-any)FAMoS_0.3.1.tar.gz(r-4.6-any)
FAMoS_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
FAMoS/json (API)

# Install 'FAMoS' in R:
install.packages('FAMoS', repos = c('https://gabelhub.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gabelhub/famos/issues

Datasets:

On CRAN:

Conda:

4.72 score 1 stars 26 scripts 271 downloads 2 mentions 15 exports 9 dependencies

Last updated from:3e132906f8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK136
source / vignettesOK177
linux-releaseOK128
macos-releaseOK128
macos-oldrelOK155
windows-develOK69
windows-releaseOK72
windows-oldrelOK66
wasm-releaseOK111

Exports:aicc.weightsbase.optimcombine.and.fitcombine.parfamosfamos.performanceget.most.distantmake.directoriesmodel.apprparscale.famosrandom.init.modelretrieve.resultsreturn.resultssc.orderset.crit.parms

Dependencies:codetoolsdigestfutureglobalslistenvparallellyR.methodsS3R.ooR.utils

FAMoS Evaluation Functions
Plotting the results of the model selection process
Rendered fromFAMoS-EvaluationFunctions.Rmdusingknitr::rmarkdown

Last update: 2020-02-10
Started: 2018-06-18

FAMoS Examples
Model selection on a system of ordinary differential equations | Model selection based on logistic regression models
Rendered fromFAMoS-Examples.Rmdusingknitr::rmarkdown

Last update: 2019-05-24
Started: 2019-05-08