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 twocomponent 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 variancecovariance 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()

Greenlight 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()

Variancecovariance matrix of the empirical process in an admixture model 
gaussianity_test()

Onesample 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 twocomponent mixture model 
rsimmix_mix()

Simulation of a twocomponent mixture with one component following a twocomponent 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()

Twosamples hypothesis test on the unknown component in admixture models 