Package: mixsqp 0.3-54

Peter Carbonetto

mixsqp: Sequential Quadratic Programming for Fast Maximum-Likelihood Estimation of Mixture Proportions

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>.

Authors:Youngseok Kim [aut], Peter Carbonetto [aut, cre], Mihai Anitescu [aut], Matthew Stephens [aut], Jason Willwerscheid [ctb], Jean Morrison [ctb]

mixsqp_0.3-54.tar.gz
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mixsqp_0.3-54.tgz(r-4.4-emscripten)mixsqp_0.3-54.tgz(r-4.3-emscripten)
mixsqp.pdf |mixsqp.html✨
mixsqp/json (API)

# Install 'mixsqp' in R:
install.packages('mixsqp', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/stephenslab/mixsqp/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • tacks - Beckett & Diaconis tack rolling example.

On CRAN:

Conda:

openblascpp

4.56 score 23 packages 5.2k downloads 4 exports 5 dependencies

Last updated 1 years agofrom:c8e49ae42d. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 15 2025
R-4.5-linux-x86_64OKMar 15 2025
R-4.4-linux-x86_64OKMar 15 2025

Exports:mixobjectivemixsqpmixsqp_control_defaultsimulatemixdata

Dependencies:irlbalatticeMatrixRcppRcppArmadillo

Illustration of mixsqp applied to a small data set, and a large one

Youngseok Kim, Peter Carbonetto and Matthew Stephens

Rendered frommixsqp-intro.Rmdusingknitr::rmarkdownon Mar 15 2025.

Last update: 2023-12-21
Started: 2018-11-14

Citation

To cite the mixsqp package, please use:

Youngseok Kim, Peter Carbonetto, Matthew Stephens and Mihai Anitescu (2020). A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming. Journal of Computational and Graphical Statistics 29(2), 261-273,<doi:10.1080/10618600.2019.1689985>

Corresponding BibTeX entry: 

  @Article{,
    title = {A fast algorithm for maximum likelihood estimation of
      mixture proportions using sequential quadratic programming},
    author = {{Youngseok Kim} and {Peter Carbonetto} and {Matthew
      Stephens} and {Mihai Anitescu}},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {29},
    number = {2},
    pages = {261--273},
    year = {2020},
    url = {https://doi.org/10.1080/10618600.2019.1689985},
  }