Package: HETOP 0.2-6

J.R. Lockwood

HETOP: MLE and Bayesian Estimation of Heteroskedastic Ordered Probit (HETOP) Model

Provides functions for maximum likelihood and Bayesian estimation of the Heteroskedastic Ordered Probit (HETOP) model, using methods described in Lockwood, Castellano and Shear (2018) <doi:10.3102/1076998618795124> and Reardon, Shear, Castellano and Ho (2017) <doi:10.3102/1076998616666279>. It also provides a general function to compute the triple-goal estimators of Shen and Louis (1998) <doi:10.1111/1467-9868.00135>.

Authors:J.R. Lockwood

HETOP_0.2-6.tar.gz
HETOP_0.2-6.tar.gz(r-4.5-noble)HETOP_0.2-6.tar.gz(r-4.4-noble)
HETOP_0.2-6.tgz(r-4.4-emscripten)HETOP_0.2-6.tgz(r-4.3-emscripten)
HETOP.pdf |HETOP.html✨
HETOP/json (API)

# Install 'HETOP' in R:
install.packages('HETOP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

jagscpp

1.00 score 176 downloads 5 exports 15 dependencies

Last updated 6 years agofrom:736f6f289b. Checks:1 OK, 1 NOTE. Indexed: no.

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

Exports:fh_hetopgendata_hetopmle_hetoptriple_goalwaic_hetop

Dependencies:abindbootclicodagluelatticelifecyclemagrittrR2jagsR2WinBUGSrjagsrlangstringistringrvctrs