c(
person(given = "Grace",
family = "Yoon",
role = c("aut"),
email = "gyoon6067@gmail.com",
comment = c(ORCID = "0000-0003-3263-1352")),
person(given = "Irina",
family = "Gaynanova",
role = c("aut", "cre"),
email = "irinag@stat.tamu.edu",
comment = c(ORCID = "0000-0002-4116-0268")))

c(
person(given = "Grace",
family = "Yoon",
role = c("aut"),
email = "gyoon6067@gmail.com",
comment = c(ORCID = "0000-0003-3263-1352")),
person(given = "Mingze",
family = "Huang",
role = c("ctb"),
email = "mingzehuang@gmail.com",
comment = c(ORCID = "0000-0003-3919-1564")),
person(given = "Irina",
family = "Gaynanova",
role = c("aut", "cre"),
email = "irinag@stat.tamu.edu",
comment = c(ORCID = "0000-0002-4116-0268")))
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> and
Yoon, Mueller and Gaynanova (2020) <doi:10.1080/10618600.2021.1882468>.
Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba

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> and
Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.
Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba, latentcor (>=
2.0.1)

Margot Selosse, Julien Jacques, Christophe Biernacki
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.
Rcpp (>= 0.12.11), RcppProgress (>= 0.1), fda, methods