Title: Estimation and Inference for Multi-Kink Quantile Regression
person("Chuang", "Wan", email = "wanchuang@stu.xmu.edu.cn", role =  c("aut", "cre")),
person("Wei", "Zhong", email = "wzhong@xmu.edu.cn", role = "aut"))
Description: Estimation and inference for multiple kink quantile regression. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, "Wei Zhong, Chuang Wan and Wenyang Zhang (2020). Estimation and inference for multi-kink quantile regression, submitted".
Packaged: 2020-04-29 02:11:02 UTC; wanchuang

Title: Multidimensional latent class Item Response Theory models
Author: Francesco Bartolucci, Silvia Bacci, Michela Gnaldi - University
Description: This package provides a flexible framework for the Item
Response Theory analysis of dichotomous and ordinal polythomous
outcomes under the assumption of multidimensionality and
discreteness of the latent traits. The fitting algorithms allow
for missing responses and for different item parameterizations
and are based on the Expectation-Maximization paradigm.

Individual covariates affecting the class weights may be
Packaged: 2012-11-18 13:59:17 UTC; francescobartolucci

Packaged: 2012-12-11 14:56:57 UTC; francescobartolucci

Title: Multidimensional Latent Class Item Response Theory Models
Author: Francesco Bartolucci, Silvia Bacci, Michela Gnaldi - University of Perugia (IT)
Description: Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
Packaged: 2016-04-13 05:21:49 UTC; francescobartolucci