chf_1F1

Kummer's function (also: confluent hypergeometric function of the first kind)
for numeric (non-complex) values and input parameters in log-scale.

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.

Lisa Amrhein

scModels

Fitting Discrete Distribution Models to Count Data

Kumar Harsha

Christiane Fuchs

Pavel Holoborodko

chf_1F1 function

<dl><dt>x</dt>
<dd>numeric value or vector</dd>
<dt>a, b</dt>
<dd>numeric parameters of the Kummer function</dd></dl>

Arguments

Kummer's (confluent hypergeometric) function in log-scale — chf_1F1

<dl>

<dt>x</dt>
<dd>numeric value or vector</dd>


<dt>a, b</dt>
<dd>numeric parameters of the Kummer function</dd>

</dl>

chf_1F1: Kummer's (confluent hypergeometric) function in log-scale

Description

Usage

Arguments

Details

Examples