admix2.5.2 package

Package Admix for Admixture (aka Contamination) Models

admix_cluster

Cluster K populations following admixture models

admix_estim

Estimate the unknown weight in an admixture model

admix_model

Define the distribution/parameter(s) of the known component

admix_test

Equality test for the unknown components in admixture models

admix-package

admix: Package Admix for Admixture (aka Contamination) Models

decontaminated_cdf

Estimates the decontaminated CDF of the unknown component in an admixt...

decontaminated_density

Probability density function of the unknown component

detect_support_type

Detect the type of support of some random variables

estim_BVdk

Estimation of the admixture parameters by Bordes & Vandekerkhove (2010...

estim_IBM

Estimate weights of unknown components from two admixtures using IBM

estim_PS

Estimates in an admixture using Patra and Sen approach

gaussianity_test

Gaussianity test in an admixture model

get_cluster_members

Extractor for members of clusters

get_cluster_sizes

Extractor for cluster sizes

get_discrepancy_matrix

Extractor for discrepancies b/w unknown components

get_discrepancy_rank

Extractor for pairwise discrepancy rankings

get_known_component

Extractor for known component(s) in admixture model(s)

get_mixing_weights

Extractor for estimated mixing weights

get_mixture_data

Extractor for simulated data from two-component mixture

get_statistic_components

Extractor for components involved in test statistic

get_tabulated_dist

Extractor for tabulated distribution in the k-sample test

IBM_k_samples_test

Equality test of K unknown component distributions

is_equal_knownComp

Equality of known components in two admixture models

orthobasis_test

Equality test of two unknown component distributions using polynomial ...

plot.admix_model

Plot method for objects of class admix_model

plot.decontaminated_density

Plot method for object of class decontaminated_density

plot.twoComp_mixt

Plot the empirical mixture pdf

print.admix_cluster

Print method for object of class 'admix_cluster'

print.admix_estim

Print method for object of class admix_estim

print.admix_model

Print method for objects of class admix_model

print.decontaminated_density

Print method for object of class decontaminated_density

print.estim_BVdk

Print method for objects 'estim_BVdk'

print.estim_IBM

Print method for objects of class 'estim_IBM'

print.estim_PS

Print method for objects of class 'estim_PS'

print.twoComp_mixt

Print method for objects twoComp_mixt

reject_nullHyp

Extractor for the test decision

summary.admix_cluster

Summary method for object of class 'admix_cluster'

summary.admix_estim

Summary method for object of class admix_estim

summary.admix_model

Summary method for objects of class admix_model

summary.decontaminated_density

Summary method for object of class decontaminated_density

summary.estim_BVdk

Summary method for objects 'estim_BVdk'

summary.estim_IBM

Summary method for objects 'estim_IBM'

summary.estim_PS

Summary method for objects 'estim_PS'

summary.twoComp_mixt

Summary method for objects twoComp_mixt

twoComp_mixt

Simulation of a two-component mixture model

which_rank

Extractor for the selected rank in the test statistic

Implements techniques 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>, and Milhaud, Pommeret, Salhi, Vandekerkhove (2024) <doi:10.3150/23-BEJ1593>. 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-sample contexts to test the unknown component density (see Milhaud, Pommeret, Salhi and Vandekerkhove (2022) <doi:10.1016/j.jspi.2021.05.010>, and Milhaud, Pommeret, Salhi, Vandekerkhove (2024) <doi:10.3150/23-BEJ1593>). Finally, clustering of unknown mixture components is also feasible in a K-sample setting (see Milhaud, Pommeret, Salhi, Vandekerkhove (2024) <https://jmlr.org/papers/v25/23-0914.html>).

  • Maintainer: Xavier Milhaud
  • License: GPL (>= 3)
  • Last published: 2026-01-08