invgamma: the inverse gamma distribution in R

invgamma is a popular low dependency R package that implements the probability density function (PDF), cumulative distribution function (CDF), quantile function (QF) and random number generator (RNG) functions for the inverse gamma, inverse chi-squared, and inverse exponential distributions, which are missing from base R . The functions follow the standard R syntax and are efficient, leveraging the corresponding functions for the gamma distribution currently in R through straightforward mathematical relationships between the distributions. It is distributed through the Comprehensive R Archive Network (CRAN, https://cran.r-project.org ) and GitHub ( https://github.com/dkahle/invgamma ), where it is version controlled.

Authors

Publication Details

Journal
PeerJ Computer Science
Published
2025-09-26
DOI
https://doi.org/10.7717/peerj-cs.3205
Primary Topic
Bayesian Methods and Mixture Models
Type
article
Field-Weighted Citation Impact
0.00
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article

invgamma: the inverse gamma distribution in R

David Kahle, James D. Stamey
PeerJ Computer Science
Bayesian Methods and Mixture Models
article

invgamma: the inverse gamma distribution in R

David Kahle, James D. Stamey
article en

Abstract

invgamma is a popular low dependency R package that implements the probability density function (PDF), cumulative distribution function (CDF), quantile function (QF) and random number generator (RNG) functions for the inverse gamma, inverse chi-squared, and inverse exponential distributions, which are missing from base R . The functions follow the standard R syntax and are efficient, leveraging the corresponding functions for the gamma distribution currently in R through straightforward mathematical relationships between the distributions. It is distributed through the Comprehensive R Archive Network (CRAN, https://cran.r-project.org ) and GitHub ( https://github.com/dkahle/invgamma ), where it is version controlled.

PeerJ Computer ScienceVol. 11
Openalex Percentile: Top 86%
Bayesian Methods and Mixture Models
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