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]

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

On CRAN:

4 exports 10 stars 4.05 score 5 dependencies 24 dependents 71 scripts 11.2k downloads

Last updated 9 months agofrom:5146a57b2e. Checks:OK: 9. Indexed: yes.

Exports:mixobjectivemixsqpmixsqp_control_defaultsimulatemixdata

Dependencies:irlbalatticeMatrixRcppRcppArmadillo