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.zip(r-4.5)HETOP_0.2-6.zip(r-4.4)HETOP_0.2-6.zip(r-4.3)
HETOP_0.2-6.tgz(r-4.5-any)HETOP_0.2-6.tgz(r-4.4-any)HETOP_0.2-6.tgz(r-4.3-any)
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://jrlockwood.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jrlockwood/hetop/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

2.00 score 1 stars 185 downloads 5 exports 15 dependencies

Last updated 3 years agofrom:be2121f92b. Checks:6 OK, 3 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 08 2025
R-4.5-winNOTEMar 08 2025
R-4.5-macNOTEMar 08 2025
R-4.5-linuxNOTEMar 08 2025
R-4.4-winOKMar 08 2025
R-4.4-macOKMar 08 2025
R-4.4-linuxOKMar 08 2025
R-4.3-winOKMar 08 2025
R-4.3-macOKMar 08 2025

Exports:fh_hetopgendata_hetopmle_hetoptriple_goalwaic_hetop

Dependencies:abindbootclicodagluelatticelifecyclemagrittrR2jagsR2WinBUGSrjagsrlangstringistringrvctrs