DESCRIPTION

Package: midasml
Type: Package
Title: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
Version: 0.1.0
Authors@R: c(
    person("Jonas", "Striaukas",  role = c("cre","aut"), email =  "jonas.striaukas@gmail.com"),
    person("Andrii", "Babii",  role = c("aut"), 
	email =  "andrii@email.unc.edu"),
    person(c("Eric", "Ghysels"), role = c("aut"), 
	email =  "eghysels@unc.edu"),
    person("Alex", "Kostrov",  role = c("ctb"), 
	comment = "Contributions to analytical gradients for non-linear low-dimensional MIDAS estimation code", email =  "alexander.kostrov@unisg.ch"))
Author: Jonas Striaukas [aut, cre] 
Maintainer: Jonas Striaukas <jonas.striaukas@gmail.com>
Description: The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the `midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.
BugReports: https://github.com/jstriaukas/midasml/issues
License: GPL (>= 2)
Depends: Matrix, R (>= 2.10)
Imports: graphics, stats, mcGlobaloptim, methods, lubridate	
Encoding: UTF-8
RoxygenNote: 7.1.0