Encoding: UTF-8
Type: Package
Package: mixsqp
Version: 0.3-17
Date: 2020-01-28
Title: Sequential Quadratic Programming for Fast Maximum-Likelihood
        Estimation of Mixture Proportions
Authors@R: 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"))
URL: https://github.com/stephenslab/mixsqp
BugReports: https://github.com/stephenslab/mixsqp/issues
Depends: R (>= 3.3.0)
Description: 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>.
License: MIT + file LICENSE
Imports: stats, irlba, Rcpp (>= 0.12.15)
Suggests: REBayes, Rmosek, testthat, knitr, rmarkdown
LinkingTo: Rcpp, RcppArmadillo
LazyData: true
NeedsCompilation: yes
VignetteBuilder: knitr
RoxygenNote: 7.0.2
Packaged: 2020-01-28 17:39:25 UTC; pcarbo
Author: Youngseok Kim [aut],
  Peter Carbonetto [aut, cre],
  Mihai Anitescu [aut],
  Matthew Stephens [aut],
  Jason Willwerscheid [ctb],
  Jean Morrison [ctb]
Maintainer: Peter Carbonetto <peter.carbonetto@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-29 09:50:03 UTC
