Title: Sparse Canonical Correlation Analysis for High-Dimensional Mixed
Description: Semi-parametric approach for sparse canonical correlation analysis
which can handle mixed data types: continuous, binary and truncated continuous.
Bridge functions are provided to connect Kendall's tau to latent correlation
under the Gaussian copula model. The methods are described in
Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007>.
Imports: Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba, chebpol
Author: Grace Yoon [aut, cre] (<https://orcid.org/0000-0003-3263-1352>),

Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and
Yoon, Müller and Gaynanova (2020) <arXiv:2006.13875>.

Imports: Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba

Imports: Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba, latentcor (>=

Author: Margot Selosse, Julien Jacques, Christophe Biernacki
Description: Implementation of the co-clustering method for mixed type data proposed in M. Selosse, J. Jacques, C. Biernacki (2018) <https://hal.archives-ouvertes.fr/hal-01893457>. It consists in clustering simultaneously the rows (observations) and the columns (features) of a heterogeneous data set.
Imports: Rcpp (>= 0.12.11), RcppProgress (>= 0.1), fda, methods

Imports: Rcpp (>= 0.12.11), fda, methods

Title: Mixed, Low-Rank, and Sparse Multivariate Regression on
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.
Imports: grpreg, purrr, MASS, stats, ggplot2
Author: Alexander White [aut, cre] (<https://orcid.org/0000-0002-9117-1475>),