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midasml

0.1.11Package record

Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data

0packages depend
9Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

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.

Install

Health

CRAN checks
13OK
Slowest check: 3.3 min · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 0.1.11
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
39 (9 internal)includes a package overview page
Examples that run
78%18 run in a plain check · 5 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples
100%
Documented parameters
99%
Return-value docs
100%
Help pages citing references
0%

Downloads

9K
CRAN downloads in the past year
Rank #5,314 · ~25/day · ~747/mo
Daily download trend is not available in this view yet.
60030 days
1.6K90 days
9K1 year
Compare downloads with other packages →
Also on365 r2u5 autocran

Repository

44Stars
23Forks
3Open issues
1Open PRs
0Releases
118Commits
1Commit identities
sparse-group-lassomachine-learningtime-seriesforecasting-modelsnowcasting-models
118 commits · Last activity 2022-09-29 · 0% stars, 30d · The default branch showed an older version (0.1.10) than CRAN on 2026-10-22
Responsiveness
How these are sampled
PRs merged (all time)
0%
Median days to close, 50 most recently updated closed issues
2
Median days to close, 50 most recently updated closed PRs
4

Stars over time

2025-10-01 · 412026-07-07 · 44
PracticesBeta

1 development-tooling and community-health practice detected across 1 family in the upstream repository

Checks run against github.com/jstriaukas/midasml on 2026-10-22.

Git structural (1)
.gitattributes
How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)

Installing midasml adds 13 packages (5 with compiled code, 3.1 MB of source tarballs). Heaviest: randtoolbox 1.4 MB, lubridate 420 KB, cpp11 298 KB.

Depends (2)
R >= 3.5.0Matrix
Imports (9)
LinkingTo (0)
none
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (5)
Maintainer since 0.0.1 (2020), Author
Author · added in 0.1.0
Author · added in 0.1.11
Contributor · added in 0.1.0
Contributions to analytical gradients for non-linear low-dimensional MIDAS estimation code
Releases (19)

Pick two releases to compare their code metrics.

Show 10 earlier eventsHide 10 earlier events
  • 0.1.3
    record
    2021-04-22 · diff ↗
  • 0.1.2
    record
    2021-04-22 · diff ↗
  • 0.1.0
    record
    2021-04-14 · diff ↗ ·
    removed 6 exports
    panel_sgl, reg_sgl, predict.panel_sgl, predict.reg_sgl, apply_transform, pattern:^[[:alpha:]]+
    added 8 exports
    cv.sglfit, cv.panel.sglfit, ic.sglfit, ic.panel.sglfit, reg.sgl, reg.panel.sgl, sglfit, midas.ardl
  • 0.0.6
    record
    2021-03-13 · diff ↗
  • 0.0.5
    record
    2020-07-05 · diff ↗
  • 0.0.4
    record
    2020-07-05 · diff ↗
  • 0.0.3
    record
    2020-07-03 · diff ↗
  • 0.0.2
    record
    2020-07-01 · diff ↗
  • 0.0.1
    record
    2020-06-25
  • R
    R 4.0.0 released · 2020-04-24

Package metadata

First published
2020-06-25
Total releases
19 / 6 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 3.5.0
Bundled data
644 KB / 5 files
Download size
702 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("midasml")
Striaukas, J., Eric Ghysels, Babii, A., Beyhum, J., & Kostrov, A. (2025). midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data (Version 0.1.11) [Computer software]. https://doi.org/10.32614/CRAN.package.midasml

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for midasml version 0.1.11 [Data set]. HJJB, LLC. Data release v2026-10-24. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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