Package: hbbr 1.1.2

hbbr: Hierarchical Bayesian Benefit-Risk Assessment Using Discrete Choice Experiment

Implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <doi:10.1080/19466315.2018.1527248>.

Authors:Saurabh Mukhopadhyay [aut], Saurabh Mukhopadhyay [cre]

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hbbr.pdf |hbbr.html✨
hbbr/json (API)

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

Peer review:

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • hbbrPilotResp - A list consisting of pilot data and associated discrete choice design information for the HBBR model framework.
  • simAugData - A list consisting of simulated data, design, baseline profiles, and true part-worth matrix for the Augmented HBBR model framework.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 139 downloads 2 exports 15 dependencies

Last updated 5 years agofrom:39f252c403. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 21 2024
R-4.5-winOKOct 21 2024
R-4.5-linuxOKOct 21 2024
R-4.4-winOKOct 21 2024
R-4.4-macOKOct 21 2024
R-4.3-winOKOct 21 2024
R-4.3-macOKOct 21 2024

Exports:hbbr.FithbbrAug.Fit

Dependencies:abindbootclicodagluelatticelifecyclemagrittrR2jagsR2WinBUGSrjagsrlangstringistringrvctrs