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:
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-4.4-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
Last updated 3 years agofrom:be2121f92b. Checks:OK: 5 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 08 2024 |
| R-4.5-win | NOTE | Nov 08 2024 |
| R-4.5-linux | NOTE | Nov 08 2024 |
| R-4.4-win | OK | Nov 08 2024 |
| R-4.4-mac | OK | Nov 08 2024 |
| R-4.3-win | OK | Nov 08 2024 |
| R-4.3-mac | OK | Nov 08 2024 |
Exports:fh_hetopgendata_hetopmle_hetoptriple_goalwaic_hetop
Dependencies:abindbootclicodagluelatticelifecyclemagrittrR2jagsR2WinBUGSrjagsrlangstringistringrvctrs
