Package: midasml
Type: Package
Title: Estimation and Prediction Methods for High-Dimensional Mixed
        Frequency Time Series Data
Version: 0.1.10
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"))
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 (>= 3.5.0)
Imports: doRNG, doParallel, foreach, graphics, randtoolbox, snow,
        methods, lubridate, stats
Encoding: UTF-8
Repository: https://jstriaukas.r-universe.dev
Date/Publication: 2022-09-29 12:08:44 UTC
RemoteUrl: https://github.com/jstriaukas/midasml
RemoteRef: HEAD
RemoteSha: a2e547ce1e883c5340813ea9cb4d2d448720f571
NeedsCompilation: yes
Packaged: 2026-07-04 23:45:11 UTC; root
Author: Jonas Striaukas [cre, aut],
  Andrii Babii [aut],
  Eric Ghysels [aut],
  Alex Kostrov [ctb] (Contributions to analytical gradients for
    non-linear low-dimensional MIDAS estimation code)