Clustering of Multivariate Binary Data with Dimension Reduction
via L1-Regularized Likelihood Maximization
The clustering of binary data with reducing the dimensionality (CLUSBIRD) proposed by Yamamoto and Hayashi (2015) <doi:10.1016/j.patcog.2015.05.026>.

Causal Discovery under a Confounder Blanket
person("David", "Watson", , "david.s.watson11@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-9632-2159"))
Methods for learning causal relationships among a set of
foreground variables X based on signals from a (potentially much
larger) set of background variables Z, which are known non-descendants
of X. The confounder blanket learner (CBL) uses sparse regression
techniques to simultaneously perform many conditional independence
tests, with complementary pairs stability selection to guarantee
finite sample error control. CBL is sound and complete with respect to
a so-called "lazy oracle", and works with both linear and nonlinear
systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.

data.table, foreach, glmnet, lightgbm, doRNG

c(person("Yi", "Pan", email = "ypan1988@gmail.com",
role = c("aut", "cre")),
person("Keita", "Teranishi",
role = c("aut")))
Provides the 'cblas.h' header file as C interface to the underlying
internal 'BLAS' library in R. 'CBLAS' <https://www.netlib.org/blas/cblas.h>
is a collection of wrappers originally written by Keita Teranishi and
provides a C interface to the FORTRAN 'BLAS' library
<https://www.netlib.org/blas/>. Note that as internal 'BLAS' library
provided by R <https://svn.r-project.org/R/trunk/src/include/R_ext/BLAS.h>
is used and only the double precision / double complex 'BLAS' routines are
supported.
Yi Pan [aut, cre],
Keita Teranishi [aut]

Estimates conditional binary quantile models developed by Lu (2019) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.
methods, Formula, Rcpp (>= 0.12.0), rstan (>= 2.18.1)

Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.