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crch: Censored Regression with Conditional Heteroscedasticity

Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects.

Version: 1.2-1
Depends: R (≥ 3.6.0)
Imports: stats, Formula, ordinal, sandwich, scoringRules
Suggests: distributions3 (≥ 0.2.1), glmx, knitr, lmtest, memisc, quarto
Published: 2024-09-16
DOI: 10.32614/CRAN.package.crch
Author: Achim Zeileis ORCID iD [aut, cre], Jakob W. Messner ORCID iD [aut], Reto Stauffer ORCID iD [aut], Ioannis Kosmidis ORCID iD [ctb], Georg J. Mayr ORCID iD [ctb]
Maintainer: Achim Zeileis <Achim.Zeileis at R-project.org>
BugReports: https://topmodels.R-Forge.R-project.org/crch/contact.html
License: GPL-2 | GPL-3
URL: https://topmodels.R-Forge.R-project.org/crch/
NeedsCompilation: yes
Citation: crch citation info
Materials: README NEWS
In views: Distributions, Econometrics
CRAN checks: crch results

Documentation:

Reference manual: crch.pdf
Vignettes: Heteroscedastic Censored and Truncated Regression with crch (source, R code)

Downloads:

Package source: crch_1.2-1.tar.gz
Windows binaries: r-devel: crch_1.2-1.zip, r-release: crch_1.2-1.zip, r-oldrel: crch_1.2-1.zip
macOS binaries: r-release (arm64): crch_1.2-1.tgz, r-oldrel (arm64): crch_1.2-1.tgz, r-release (x86_64): crch_1.2-1.tgz, r-oldrel (x86_64): crch_1.2-1.tgz
Old sources: crch archive

Reverse dependencies:

Reverse imports: exdqlm, MortalityGaps
Reverse suggests: ensemblepp, insight, marginaleffects, NetSimR, scoringRules
Reverse enhances: prediction

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.