Package Admix for Admixture (aka Contamination) Models
c(
person(given = "Xavier",
family = "Milhaud",
role = c("aut", "cre"),
email = "xavier.milhaud.research@gmail.com"),
person(given = "Pierre",
family = "Vandekerkhove",
role = "ctb"),
person(given = "Denys",
family = "Pommeret",
role = "ctb"),
person(given = "Yahia",
family = "Salhi",
role = "ctb")
)
Implements several methods to estimate the unknown quantities related
to two-component admixture models, where the two components can belong to any
distribution (note that in the case of multinomial mixtures, the two components
must belong to the same family). Estimation methods depend on the assumptions
made on the unknown component density (see Bordes and Vandekerkhove (2010) <doi:10.3103/S1066530710010023>;
Patra and Sen (2016) <doi:10.1111/rssb.12148>); Milhaud, Pommeret, Salhi and Vandekerkhove
(2021) <https://hal.archives-ouvertes.fr/hal-03201760>). In practice, one can estimate both the
mixture weight and the unknown component density in a wide variety of frameworks.
On top of that, hypothesis tests can be performed in one and two-samples contexts
to test the unknown component density. Finally, clustering of unknown
mixture components is also feasible in a K-samples setting.
base, fdrtool, graphics, Iso, latex2exp, MASS, methods,
orthopolynom, pracma, Rcpp, stats, utils
rmutil, doParallel, foreach, evd, logitnorm, flexsurv, plyr,
reshape2, gridExtra, lattice, testthat (>= 3.0.0), knitr,
rmarkdown, markdown
Xavier Milhaud [aut, cre],
Pierre Vandekerkhove [ctb],
Denys Pommeret [ctb],
Yahia Salhi [ctb]

Implements several methods to estimate the unknown quantities related
to two-component admixture models, where the two components can belong to any
distribution (note that in the case of multinomial mixtures, the two components
must belong to the same family). Estimation methods depend on the assumptions
made on the unknown component density (see Bordes and Vandekerkhove (2010) <doi:10.3103/S1066530710010023>;
Patra and Sen (2016) <doi:10.1111/rssb.12148>); Milhaud, Pommeret, Salhi and Vandekerkhove
(2021) <doi:10.1016/j.jspi.2021.05.010>). In practice, one can estimate both the
mixture weight and the unknown component density in a wide variety of frameworks.
On top of that, hypothesis tests can be performed in one and two-samples contexts
to test the unknown component density. Finally, clustering of unknown
mixture components is also feasible in a K-samples setting.