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.
|Author||Jakob Messner [aut, cre], Achim Zeileis [aut]|
|Date of publication||2016-10-18 10:37:58|
|Maintainer||Jakob Messner <email@example.com>|
|License||GPL-2 | GPL-3|
clogis: The Censored Logistic Distribution
cnorm: The Censored Normal Distribution
coef.crch: Methods for CRCH Objects
coef.crch.boost: Methods for boosted CRCH Objects
coef.hxlr: Methods for HXLR Objects
crch: Censored Regression with Conditional Heteroscedasticy
crch.boost: Auxiliary functions to fit 'crch' models via boosting.
crch.control: Auxiliary Function for Controlling crch Fitting
ct: The Censored Student-t Distribution
hxlr: Heteroscedastic Extended Logistic Regression
hxlr.control: Auxiliary Function for Controlling HXLR Fitting
plot.crch.boost: Plot coefficient paths of boosted CRCH objects.
predict.crch: Predicted/Fitted Values for CRCH Fits
predict.crch.boost: Predicted/Fitted Values for boosted CRCH Fits
predict.hxlr: Predict/Fitted Values for HXLR Fits
RainIbk: Precipitation Observations and Forecasts for Innsbruck
tlogis: The Truncated Logistic Distribution
tnorm: The Truncated Normal Distribution
tt: The Truncated Student-t Distribution