Packaged: 2020-07-04 09:34:33 UTC; striaukas

Packaged: 2020-07-05 20:35:30 UTC; striaukas

Description: The 'midasml' estimation and prediction methods for high dimensional time series regression models under mixed data sampling data structures using structured-sparsity penalties and orthogonal polynomials. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. Functions that compute MIDAS data structures were inspired by MIDAS 'Matlab' toolbox (v2.3) written by Eric Ghysels.
Packaged: 2021-03-11 09:38:41 UTC; striaukas

person("Jonas", "Striaukas",  role = c("cre","aut"), email =  "jonas.striaukas@gmail.com"),
person("Andrii", "Babii",  role = c("aut"),
person(c("Eric", "Ghysels"), role = c("aut"),
person("Alex", "Kostrov",  role = c("ctb"),
comment = "Contributions to analytical gradients for non-linear low-dimensional MIDAS estimation code", email =  "alexander.kostrov@unisg.ch"))
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.
Imports: graphics, stats, mcGlobaloptim, methods, lubridate
Packaged: 2021-04-14 09:56:18 UTC; striaukas
Alex Kostrov [ctb] (Contributions to analytical gradients for
non-linear low-dimensional MIDAS estimation code)

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.
Imports: doRNG, doParallel, foreach, graphics, randtoolbox, snow,
Packaged: 2022-04-28 23:16:47 UTC; Utilisateur