Author: Hugo Andres Gutierrez Rojas <hagutierrezro@gmail.com>
Maintainer: Hugo Andres Gutierrez Rojas <hagutierrezro@gmail.com>
Packaged: 2018-07-23 01:21:12 UTC; psirusteam

Packaged: 2019-11-18 18:35:00 UTC; psirusteam

Packaged: 2020-01-16 21:22:13 UTC; psirusteam

Packaged: 2020-04-21 19:54:19 UTC; psirusteam

Title: Tests and Estimation of Covariance Change-Points for Hi-Dim Data
Authors@R: c(person("Ping-Shou", "Zhong", role = c("aut", "cre"),
person("Nurlan", "Abdukadyrov", role = "ctb"),
Description: An implementation of the procedures in Zhong et al. (2019) and Santo and Zhong (2020)
for testing the homogeneity of covariance matrices, and estimating
multiple change-points in high-dimensional (Hi-Dim) longitudinal/functional data with general
temporospatial dependence. The null hypothesis of the homogeneity test is that
all covariance matrices are equal at each time point. If the null hypothesis is rejected,
the procedure further identifies the locations of the change points.
Note: The package uses Open MP.  Mac OS X users may need to update clang compiler so that it supports Open MP.
References: Ping-Shou Zhong, Runze Li, Shawn Santo (2019) <doi:10.1093/biomet/asz011>
Shawn Santo, Ping-Shou Zhong (2020) <arXiv:2005.01895>.

Title: API Wrapper for Taiwan Economic Journal Data Service
Authors@R: c(person("Peter","Young", role=c("aut", "cre"), email="peter_young@tej.com.tw"))
Description: Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.

Title: Temporal Contributions on Trends using Mixed Models
Author: Marcos Fernández-Martínez, Joan Maspons
Description: Method to estimate the effect of the trend in predictor variables on the observed trend
of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et
Packaged: 2019-03-18 09:35:34 UTC; m.fernandez

person("Brantly", "Callaway", email = "brantly.callaway@temple.edu", role = c("aut","cre")), person("Weige", "Huang", email = "weige.huang@temple.edu", role=c("aut")))
Description: Estimates conditional distributions and conditional quantiles.  The versions of the methods in this package are primarily for use in multiple step procedures where the first step is to estimate a conditional distribution.  In particular, there are functions for implementing distribution regression.  Distribution regression provides a way to flexibly model the distribution of some outcome Y conditional on covariates X without imposing parametric assumptions on the conditional distribution but providing more structure than fully nonparametric estimation (See Foresi and Peracchi (1995) <doi:10.2307/2291056> and Chernozhukov, Fernandez-Val, and Melly (2013) <doi:10.3982/ECTA10582>).