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'))

Peer review:

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

jagscpp

1.00 score 153 downloads 3 exports 5 dependencies

Last updated 9 months agofrom:252636fc67. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-linuxOKDec 04 2024

Exports:PredictionSummaryZIHRZIHR

Dependencies:codajagsUIlatticenumbersrjags