Package: scModels 1.0.4

scModels: Fitting Discrete Distribution Models to Count Data

Provides functions for fitting discrete distribution models to count data. Included are the Poisson, the negative binomial, the Poisson-inverse gaussian and, most importantly, a new implementation of the Poisson-beta distribution (density, distribution and quantile functions, and random number generator) together with a needed new implementation of Kummer's function (also: confluent hypergeometric function of the first kind). Three different implementations of the Gillespie algorithm allow data simulation based on the basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for four variants of each of the three aforementioned distributions are also available. The variants include one population and two population mixtures, both with and without zero-inflation. The package depends on the 'MPFR' libraries (<https://www.mpfr.org/>) which need to be installed separately (see description at <https://github.com/fuchslab/scModels>). This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on bioRxiv.

Authors:Lisa Amrhein [aut, cre], Kumar Harsha [aut], Christiane Fuchs [aut], Pavel Holoborodko [ctb]

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scModels.pdf |scModels.html
scModels/json (API)
NEWS

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

Peer review:

Uses libs:
  • mpfr4– Multiple precision floating-point computation
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 stars 1 packages 3 scripts 235 downloads 32 exports 3 dependencies

Last updated 2 years agofrom:ba14d424c6. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-win-x86_64NOTEOct 26 2024
R-4.5-linux-x86_64NOTEOct 26 2024
R-4.4-win-x86_64NOTEOct 26 2024
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Exports:chf_1F1dpbfit_paramsgmRNA_basicgmRNA_basic_burstgmRNA_burstgmRNA_IGbasic_burstgmRNA_switchnlogL_delnlogL_del2nlogL_nbnlogL_nb2nlogL_pbnlogL_pb2nlogL_pignlogL_pig2nlogL_poisnlogL_pois2nlogL_zidelnlogL_zidel2nlogL_zinbnlogL_zinb2nlogL_zipbnlogL_zipb2nlogL_zipignlogL_zipig2nlogL_zipoisnlogL_zipois2ppbqpbrInvGausrpb

Dependencies:gamlss.distMASSRcpp

Readme and manuals

Help Manual

Help pageTopics
Kummer's (confluent hypergeometric) function in log-scalechf_1F1
Functions to estimate parameters of probability distributions by fitting the distributions using optim()fit_params
Gillespie algorithm for mRNA generating processesgmRNA gmRNA_basic gmRNA_basic_burst gmRNA_burst gmRNA_IGbasic_burst gmRNA_switch
Inverse Gaussian DistributionInverse Gaussian rInvGaus
Negative log Likelihood functions for Poisson, negative binomial, Delaporte, Poisson-inverse Gaussian and Poisson-beta distributionsnlogL nlogL_del nlogL_del2 nlogL_nb nlogL_nb2 nlogL_pb nlogL_pb2 nlogL_pig nlogL_pig2 nlogL_pois nlogL_pois2 nlogL_zidel nlogL_zidel2 nlogL_zinb nlogL_zinb2 nlogL_zipb nlogL_zipb2 nlogL_zipig nlogL_zipig2 nlogL_zipois nlogL_zipois2
Poisson-beta Distributiondpb Poisson-beta ppb qpb rpb