Title: Measures of Uncertainty for Model Selection
Authors@R: c(person("Yuanyuan", "Li", role = c("aut", "cre"),
person("Jiming", "Jiang", role = "ths"))
Description: Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.
Packaged: 2021-04-22 04:50:15 UTC; yuanyuanli

Title: Colombian Macro-Financial Time Series Generator
Maintainer: Pedro Alejandro Cabra-Acela <jando2797@gmail.com>
Description: This repository aims to contribute to the econometric models' production
with Colombian data, by providing a set of web-scrapping functions
of some of the main macro-financial indicators. All the sources are public and
free, but the advantage of these functions is that they directly download
and harmonize the information in R's environment. No need to import or download
additional files. You only need an internet connection!
Author: Pedro Alejandro Cabra-Acela [aut, cre]

Title: Recursive Partitioning Based Multivariate Adaptive Regression
Models, Classification Trees, Survival Trees
Author: Heping Zhang, Yihao Lu, Haixu Ma, Mingliang Ma, Yidong Zhou, Qi Yu
Description: Implements recursive partitioning based, nonparametric methods for high
dimensional regression and classification. Depending on the aims of data analysis as well as the
structures of the data, macs provides three major functions: multivariate adaptive regression
models, classification trees and survival trees. A list of references for this package is, Zhang, H. (1997) <doi:10.1080/10618600.1997.10474728>, Zhang, H. et al. (1999) <ISBN:978-1-4757-3027-2>, Zhang, H. et al. (2014) <doi:10.1002/gepi.21843>.
Packaged: 2019-10-07 19:49:45 UTC; Frederick Yu