Package: mixsqp 0.3-54
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:
mixsqp_0.3-54.tar.gz
mixsqp_0.3-54.tar.gz(r-4.5-noble)mixsqp_0.3-54.tar.gz(r-4.4-noble)
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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/stephenslab/mixsqp/issues
- tacks - Beckett & Diaconis tack rolling example.
Last updated 12 months agofrom:c8e49ae42d. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 15 2024 |
R-4.5-linux-x86_64 | OK | Nov 15 2024 |
Exports:mixobjectivemixsqpmixsqp_control_defaultsimulatemixdata
Dependencies:irlbalatticeMatrixRcppRcppArmadillo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
mixsqp: Sequential Quadratic Programming for Fast Maximum-Likelihood Estimation of Mixture Proportions | mixsqp-package |
Compute objective optimized by mixsqp. | mixobjective |
Maximum-likelihood estimation of mixture proportions using SQP | mixsqp mixsqp_control_default |
Create likelihood matrix from simulated data set | simulatemixdata |
Beckett & Diaconis tack rolling example. | tacks |