## All functions

BVdk_ML_varCov_estimators()

Maximum Likelihood estimation of the variance of the unknown density variance estimator in an admixture model

BVdk_contrast()

Contrast as defined in Bordes & Vandekerkhove (2010)

BVdk_contrast_gradient()

Gradient of the contrast as defined in Bordes & Vandekerkhove (2010)

BVdk_estimParam()

Estimation of the parameters in a two-component admixture model with symmetric unknown density

BVdk_varCov_estimators()

Estimation of the variance of the estimators in admixture models with symmetric unknown density

IBM_decontaminated_unknownComp()

Provide the decontaminated density of the unknown component in an admixture model

IBM_empirical_contrast()

Empirical computation of the contrast in the Inversion - Best Matching (IBM) method

IBM_estimProp()

Estimate the weights related to the proportions of the unknown components of the two admixture models

IBM_estimVarCov_gaussVect()

Nonparametric estimation of the variance-covariance matrix of the gaussian vector in IBM approach

IBM_gap()

Difference between the unknown empirical cumulative distribution functions in two admixture models

IBM_greenLight_criterion()

Green-light criterion to decide whether to perform full equality test between unknown components between two admixture models

IBM_hessian_contrast()

Hessian matrix of the contrast function in the Inversion - Best Matching (IBM) method

IBM_tabul_stochasticInteg()

Distribution of the contrast in the Inversion - Best Matching (IBM) method

IBM_test_H0()

Equality test of unknown component distributions in two admixture models with IBM approach

IBM_theoretical_contrast()

Theoretical contrast in the Inversion - Best Matching (IBM) method

IBM_theoretical_gap()

Difference between unknown cumulative distribution functions of admixture models at some given point

PatraSen_cv_mixmodel()

Estimate by Patra and Sen the unknown component weight as well as the unknown distribution in an admixture model

PatraSen_density_est()

Compute the estimate of the density of the unknown component in an admixture model

PatraSen_dist_calc()

Compute the distance to be minimized using Patra and Sen estimation technique in admixture models

PatraSen_est_mix_model()

Estimate by Patra and Sen the unknown component weight as well as the unknown distribution in admixture models

allGalaxies

Four galaxies measurements of heliocentric velocities (Carina, Sextans, Sculptor, Fornax)

detect_support_type()

Detect the support of the random variables under study

estimVarCov_empProcess()

Variance-covariance matrix of the empirical process in an admixture model

gaussianity_test()

One-sample test in admixture models using Bordes and Vandekerkhove estimation method

is_equal_knownComp()

Test for equality of the known components between two admixture models

k_samples_clustering()

Clustering of K populations following admixture models

k_samples_test()

Equality test of unknown component distributions in K admixture models, with IBM approach

kernel_cdf()

Kernel estimation

kernel_density()

Kernel estimation

knownComp_to_uniform()

Transforms the known component of the admixture distribution to a Uniform distribution

milkyWay

Heliocentric velocity measured from the Milky Way.

orthoBasis_coef()

Compute expansion coefficients in a given orthonormal polynomial basis.

orthoBasis_test_H0()

Equality test of unknown components between two admixture models using polynomial basis expansions

plot_admix()

Plot the density of some given sample(s)

poly_orthonormal_basis()

Build an orthonormal basis to decompose some given probability density function

rsimmix()

Simulation of a two-component mixture model

rsimmix_mix()

Simulation of a two-component mixture with one component following a two-component mixture

silhouette_criterion()

Compute the silhouette criterion related to the K populations that were clustered

sim_gaussianProcess()

Simulation of a Gaussian process

two_samples_test()

Two-samples hypothesis test on the unknown component in admixture models