Package: mixedLSR
Title: Mixed, Low-Rank, and Sparse Multivariate Regression on
        High-Dimensional Data
Version: 0.1.0
Authors@R: c(
    person(given = "Alexander", family = "White", role = c("aut", "cre"), email = "whitealj@iu.edu", comment = c(ORCID = "0000-0002-9117-1475")),
    person(given = "Sha", family = "Cao", role = c("aut"), email = "shacao@iu.edu", comment = c(ORCID = "0000-0002-8645-848X")),
    person(given = "Yi", family = "Zhao", role = c("ctb"), email = "yz125@iu.edu", comment = c(ORCID = "0000-0003-4766-5934")),
    person(given = "Chi", family = "Zhang", role = c("ctb"), email = "czhang87@iu.edu", comment = c(ORCID = "0000-0001-9553-0925")))
Description: Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when 
  the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization 
  with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically 
  performing parameter selection to identify low-rank substructures in the coefficient matrix. 
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.1
Depends: R (>= 4.1.0)
Imports: grpreg, purrr, MASS, stats, ggplot2
Suggests: knitr, rmarkdown, mclust
VignetteBuilder: knitr
BugReports: https://github.com/alexanderjwhite/mixedLSR
URL: https://alexanderjwhite.github.io/mixedLSR/
NeedsCompilation: no
Packaged: 2022-11-04 10:33:31 UTC; whitealj
Author: Alexander White [aut, cre] (<https://orcid.org/0000-0002-9117-1475>),
  Sha Cao [aut] (<https://orcid.org/0000-0002-8645-848X>),
  Yi Zhao [ctb] (<https://orcid.org/0000-0003-4766-5934>),
  Chi Zhang [ctb] (<https://orcid.org/0000-0001-9553-0925>)
Maintainer: Alexander White <whitealj@iu.edu>
Repository: CRAN
Date/Publication: 2022-11-04 20:00:02 UTC
Built: R 4.6.0; ; 2026-04-25 17:30:15 UTC; unix
