API Wrapper for Taiwan Economic Journal Data Service
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>.

Temporal Contributions on Trends using Mixed Models
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
al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.

c(
person("Brantly", "Callaway", email = "brantly.callaway@temple.edu", role = c("aut","cre")), person("Weige", "Huang", email = "weige.huang@temple.edu", role=c("aut")))
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>).
Brantly Callaway [aut, cre],
Weige Huang [aut]

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, quantile regression, and versions of local linear distribution regression; all in a unified framework.  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>).