Package: midasml
Type: Package
Title: Estimation and Prediction Methods for High-Dimensional Mixed
        Frequency Time Series Data
Version: 0.1.11
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("Jad", "Beyhum",  role = c("aut"), 
	email =  "jad.beyhum@kuleuven.be"),
    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
RoxygenNote: 7.3.2
NeedsCompilation: yes
Packaged: 2025-10-09 05:43:06 UTC; js
Author: Jonas Striaukas [cre, aut],
  Andrii Babii [aut],
  Jad Beyhum [aut],
  Eric Ghysels [aut],
  Alex Kostrov [ctb] (Contributions to analytical gradients for
    non-linear low-dimensional MIDAS estimation code)
Repository: CRAN
Date/Publication: 2025-10-09 06:00:02 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2026-02-25 03:20:19 UTC; windows
Archs: x64
