Authors@R: c(person(given = "Jakob", family = "Messner", role = c("aut", "cre"), email = "Jakob.Messner@uibk.ac.at"),
Description: 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

Authors@R: c(person(given = "Jakob", family = "Messner", role = c("aut", "cre"), email = "jwmm@elektro.dtu.dk"),

Authors@R: c(person(given = "Jakob", family = "Messner", role = c("aut", "cre"), email = "jwmm@elektro.dtu.dk",
person(given = "Reto", family = "Stauffer", role = "aut", email = "reto.stauffer@uibk.ac.at",
Imports: stats, Formula, ordinal, sandwich, scoringRules
Author: Jakob Messner [aut, cre] (<https://orcid.org/0000-0002-1027-3673>),

X-CRAN-Comment: Orphaned and corrected on 2019-04-22 as maintainer's

Authors@R: c(person(given = "Jakob", family = "Messner", role = c("aut", "cre"), email = "jakob.messner@posteo.net",

Package: crch
Title: Censored Regression with Conditional Heteroscedasticity
Version: 1.1-0
Date: 2022-09-07
Authors@R: c(person(given = "Jakob", family = "Messner", role = c("aut", "cre"), email = "jakob.messner@posteo.net", comment = c(ORCID = "0000-0002-1027-3673")),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org", comment = c(ORCID = "0000-0003-0918-3766")),
person(given = "Reto", family = "Stauffer", role = "aut", email = "reto.stauffer@uibk.ac.at", comment = c(ORCID = "0000-0002-3798-5507")))
Depends: R (>= 3.6.0)
Imports: stats, Formula, ordinal, sandwich, scoringRules
Suggests: distributions3 (>= 0.2.1), glmx, lmtest, memisc
Description: 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.
License: GPL-2 | GPL-3
NeedsCompilation: yes
Packaged: 2022-09-07 19:19:14 UTC; zeileis
Author: Jakob Messner [aut, cre] (<https://orcid.org/0000-0002-1027-3673>),
Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>),
Reto Stauffer [aut] (<https://orcid.org/0000-0002-3798-5507>)
Maintainer: Jakob Messner <jakob.messner@posteo.net>
Repository: CRAN
Date/Publication: 2022-09-07 20:42:54 UTC