c(person("Youngseok","Kim",role="aut",
email="youngseok@uchicago.edu"),
person("Peter","Carbonetto",role=c("aut","cre"),
email="peter.carbonetto@gmail.com"),
person("Mihai","Anitescu",role="aut"),
person("Matthew","Stephens",role="aut"),
person("Jason","Willwerscheid",role="ctb"),
person("Jean","Morrison",role="ctb"))
REBayes, Rmosek, testthat, knitr, rmarkdown
Youngseok Kim [aut],
Peter Carbonetto [aut, cre],
Mihai Anitescu [aut],
Matthew Stephens [aut],
Jason Willwerscheid [ctb],
Jean Morrison [ctb]

Provides optimization algorithms based on sequential
quadratic programming (SQP) for maximum likelihood estimation of the
mixture proportions in a finite mixture model where the component
densities are known. The algorithms are expected to obtain solutions
that are at least as accurate as the state-of-the-art MOSEK
interior-point solver (called by function "KWDual" in the 'REBayes'
package), and they are expected to arrive at solutions more quickly
in large data sets. The algorithms are described in Y. Kim,
P. Carbonetto, M. Stephens & M. Anitescu (2018) <arXiv:1806.01412>.

Provides an optimization method based on sequential
quadratic programming (SQP) for maximum likelihood estimation of the
mixture proportions in a finite mixture model where the component
densities are known. The algorithm is expected to obtain solutions
that are at least as accurate as the state-of-the-art MOSEK
interior-point solver (called by function "KWDual" in the 'REBayes'
package), and they are expected to arrive at solutions more quickly
when the number of samples is large and the number of mixture
components is small. This implements the "mix-SQP" algorithm
(without the low-rank approximation) described in Y. Kim,
P. Carbonetto, M. Stephens & M. Anitescu (2018) <arXiv:1806.01412>.

Provides an optimization method based on sequential
quadratic programming (SQP) for maximum likelihood estimation of
the mixture proportions in a finite mixture model where the
component densities are known. The algorithm is expected to obtain
solutions that are at least as accurate as the state-of-the-art
MOSEK interior-point solver (called by function "KWDual" in the
'REBayes' package), and they are expected to arrive at solutions
more quickly when the number of samples is large and the number of
mixture components is not too large. This implements the "mix-SQP"
algorithm, with some improvements, described in Y. Kim,
P. Carbonetto, M. Stephens & M. Anitescu (2020)
<DOI:10.1080/10618600.2019.1689985>.