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:
scModels_1.0.4.tar.gz
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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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:ba14d424c6. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 26 2024 |
R-4.5-win-x86_64 | NOTE | Oct 26 2024 |
R-4.5-linux-x86_64 | NOTE | Oct 26 2024 |
R-4.4-win-x86_64 | NOTE | Oct 26 2024 |
R-4.4-mac-x86_64 | NOTE | Oct 26 2024 |
R-4.4-mac-aarch64 | NOTE | Oct 26 2024 |
R-4.3-win-x86_64 | NOTE | Oct 26 2024 |
R-4.3-mac-x86_64 | NOTE | Oct 26 2024 |
R-4.3-mac-aarch64 | NOTE | Oct 26 2024 |
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 page | Topics |
---|---|
Kummer's (confluent hypergeometric) function in log-scale | chf_1F1 |
Functions to estimate parameters of probability distributions by fitting the distributions using optim() | fit_params |
Gillespie algorithm for mRNA generating processes | gmRNA gmRNA_basic gmRNA_basic_burst gmRNA_burst gmRNA_IGbasic_burst gmRNA_switch |
Inverse Gaussian Distribution | Inverse Gaussian rInvGaus |
Negative log Likelihood functions for Poisson, negative binomial, Delaporte, Poisson-inverse Gaussian and Poisson-beta distributions | nlogL 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 Distribution | dpb Poisson-beta ppb qpb rpb |