Separates DNA mixtures using a statistical model within a
greedy algorithm with a useful tcl/tk GUI.

Fitting finite mixture of scale mixture of skew-normal
distributions .
Marcos Prates <marcos.prates@uconn.edu>, Victor Lachos
<hlachos@ime.unicamp.br> and Celso Cabral
<celsoromulo@gmail.com>
Functions to fit finite mixture of scale mixture of
skew-normal (FM-SMSN) distributions.

Marcos Prates <marcosop@ime.unicamp.br>, Victor Lachos
<hlachos@ime.unicamp.br> and Celso Cabral
<celsoromulo@gmail.com>

Marcos Prates <marcosop@est.ufmg.br>, Victor Lachos
<hlachos@ime.unicamp.br> and Celso Cabral
<celsoromulo@gmail.com>

Fitting Finite Mixture of Scale Mixture of Skew-Normal
Distributions
c(person("Marcos", "Prates", role=c("aut","cre","trl"),
email="marcosop@est.ufmg.br"),
person("Victor", "Lachos", role="aut"),
person("Celso", "Cabral", role="aut"))
Marcos Prates [aut, cre, trl], Victor Lachos [aut], Celso Cabral [aut]

Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.
Marcos Prates [aut, cre, trl],
Victor Lachos [aut],
Celso Cabral [aut]

Sequential Quadratic Programming for Fast Maximum-Likelihood
Estimation of Mixture Proportions
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"))
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 (2012) <arXiv:1806.01412>.
Rmosek, REBayes, testthat, knitr, rmarkdown
Youngseok Kim [aut],
Peter Carbonetto [aut, cre],
Mihai Anitescu [aut],
Matthew Stephens [aut]