Multi Horizon Superior Predictive Ability
c(person("Luca", "Barbaglia", email = "luc.barbaglia@gmail.com", role = c("cre", "aut")))
Run the Multi Horizon Superior Predictive Ability test to compare the predictive performance of two distinct models when jointly considering all horizons of a forecast path.

Enables Efficient Joining of Data File on Common Fields using
the Unix Utility Join
Wrapper around the Unix join facility which is more efficient than the built-in R routine merge().
The package enables the joining of multiple files on disk at once.
The files can be compressed and various filters can be deployed before joining.
Compiles only under Unix.

Estimation and Inference for Multi-Kink Quantile Regression
c(
person("Chuang", "Wan", email = "wanchuang@stu.xmu.edu.cn", role =  c("aut", "cre")),
person("Wei", "Zhong", email = "wzhong@xmu.edu.cn", role = "aut"))
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".

Multidimensional latent class Item Response Theory models
Francesco Bartolucci, Silvia Bacci, Michela Gnaldi - University
of Perugia (IT)
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.