Mixed, Low-Rank, and Sparse Multivariate Regression on
High-Dimensional Data
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")))
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.

Tools for Discrete Multivariate Mixed Membership Models
c( person("Y. Samuel", "Wang", email = "ysamwang@uw.edu", role =
c("aut","cre")), person("Elena A.", "Erosheva", email =
"erosheva@uw.edu", role = "aut"))
Fits mixed membership models with discrete
multivariate data (with or without repeated measures) following the general
framework of Erosheva 2004. This package uses a Variational EM approach by
approximating the posterior distribution of latent memberships and
selecting hyperparameters through a pseudo-MLE procedure. Currently
supported data types are Bernoulli, multinomial and rank (Plackett-Luce).
Y. Samuel Wang [aut, cre],
Elena A. Erosheva [aut]

Fits mixed membership models with discrete multivariate data (with
or without repeated measures) following the general framework of Erosheva et al
(2004). This package uses a Variational EM approach by approximating the
posterior distribution of latent memberships and selecting hyperparameters
through a pseudo-MLE procedure. Currently supported data types are
Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al     (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.