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.zip(r-4.7-any)UHM_0.3.0.zip(r-4.6-any)
UHM_0.3.0.tar.gz(r-4.7-any)UHM_0.3.0.tar.gz(r-4.6-any)
UHM_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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 218 downloads 3 exports 5 dependencies

Last updated from:252636fc67. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK138
source / vignettesOK176
linux-releaseOK102
windows-develOK85
windows-releaseOK79
wasm-releaseOK98

Exports:PredictionSummaryZIHRZIHR

Dependencies:codajagsUIlatticenumbersrjags