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 (2020) <arXiv:2005.14057>. Functions that compute MIDAS data structures were inspired by MIDAS 'Matlab' toolbox (v2.3) written by Eric Ghysels.
| Version: | 0.0.5 |
| Depends: | foreach (≥ 1.4.4) |
| Imports: | Rcpp (≥ 1.0.3), lubridate (≥ 1.7.4), parallel (≥ 3.5.2), doSNOW (≥ 1.0.18), stats (≥ 3.5.2), optimx (≥ 2020-4.2), quantreg (≥ 5.34) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2020-07-05 |
| Author: | Jonas Striaukas [aut, cre] |
| Maintainer: | Jonas Striaukas <jonas.striaukas at gmail.com> |
| BugReports: | https://github.com/jstriaukas/midasml/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | midasml results |
| Reference manual: | midasml.pdf |
| Package source: | midasml_0.0.5.tar.gz |
| Windows binaries: | r-devel: midasml_0.0.5.zip, r-release: midasml_0.0.5.zip, r-oldrel: midasml_0.0.5.zip |
| macOS binaries: | r-release: midasml_0.0.5.tgz, r-oldrel: midasml_0.0.5.tgz |
| Old sources: | midasml archive |
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