Package: UHM 0.3.0

Taban Baghfalaki

UHM: Unified Zero-Inflated Hurdle Regression Models

Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024).

Authors:Taban Baghfalaki [cre, aut], Mojtaba Ganjali [aut], Narayanaswamy Balakrishnan [aut]

UHM_0.3.0.tar.gz
UHM_0.3.0.tar.gz(r-4.5-noble)UHM_0.3.0.tar.gz(r-4.4-noble)
UHM_0.3.0.tgz(r-4.4-emscripten)UHM_0.3.0.tgz(r-4.3-emscripten)
UHM.pdf |UHM.html
UHM/json (API)

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

Bug tracker:https://github.com/tbaghfalaki/uhm/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • dataB - Simulated data from zero-inflated Beta regression model
  • dataC - Simulated data from zero-inflated Gaussian regression model
  • dataD - Simulated data from zero-inflated Poisson regression model
  • dataI - Simulated data from zero-inflated exponential regression model
  • dataP - Simulated data from zero-inflated exponential regression model

On CRAN:

Conda:

jagscpp

1.00 score 176 downloads 3 exports 5 dependencies

Last updated 1 years agofrom:252636fc67. Checks:2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 24 2025
R-4.5-linuxOKFeb 24 2025

Exports:PredictionSummaryZIHRZIHR

Dependencies:codajagsUIlatticenumbersrjags