Package: crov 0.3.0
Javier Espinosa
crov: Constrained Regression Model for an Ordinal Response and Ordinal Predictors
Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2019) <doi:10.1007/s11222-018-9842-2>. The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then one of the available methods will either establish it or drop the monotonicity assumption. Two monotonicity tests are also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor.
Authors:
crov_0.3.0.tar.gz
crov_0.3.0.tar.gz(r-4.5-noble)crov_0.3.0.tar.gz(r-4.4-noble)
crov_0.3.0.tgz(r-4.4-emscripten)crov_0.3.0.tgz(r-4.3-emscripten)
crov.pdf |crov.html✨
crov/json (API)
| # Install 'crov' in R: |
| install.packages('crov', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- crovData - Real data example
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:947d800a0a. Checks:1 OK, 1 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 15 2025 |
| R-4.5-linux | NOTE | Feb 15 2025 |
Exports:confRegCCRconfRegUCRandUCCRmdcpmonoTestBonfmonoTestConfRegplotCMLE
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Parameter Vector in Confidence Region CCR | confRegCCR |
| Parameter Vector in Confidence Regions UCR and/or UCCR | confRegUCRandUCCR |
| Real data example | crovData |
| Monotonicity Direction Classification (MDC) procedure | mdcp |
| Monotonicity test | monoTestBonf |
| Monotonicity test using confidence regions | monoTestConfReg |
| Plot unconstrained and constrained proportional odds logit model | plotCMLE |