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 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) . 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: 2026-10-27 08:15:17 UTC; root 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: https://cran.r-universe.dev Date/Publication: 2025-10-09 06:46:37 UTC RemoteUrl: https://github.com/cran/midasml RemoteRef: HEAD RemoteSha: 2bb972e64718024889dc72ff9712e7f451aa6679