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+7 0CHANGELOG.txt

+3 3README.md

+42 0code/H_I_D_A_C/base_estimators/DEnergyDist_estimation.m

+29 0code/H_I_D_A_C/base_estimators/DEnergyDist_initialization.m

+1 0code/H_I_D_A_C/base_estimators/IdCor_estimation.m

+33 0code/H_I_D_A_C/meta_estimators/DEnergyDist_DMMD_estimation.m

+32 0code/H_I_D_A_C/meta_estimators/DEnergyDist_DMMD_initialization.m

+42 0code/H_I_D_A_C/meta_estimators/IdCov_IHSIC_estimation.m

+32 0code/H_I_D_A_C/meta_estimators/IdCov_IHSIC_initialization.m

+1 2code/ITE_install.m
README.md
 `entropy (H)`: Shannon entropy, R�nyi entropy, Tsallis entropy (Havrda and Charv�t entropy), complex entropy,
 `mutual information (I)`: generalized variance, kernel canonical correlation analysis, kernel generalized variance, HilbertSchmidt independence criterion, Shannon mutual information, L2 mutual information, R�nyi mutual information, Tsallis mutual information, copulabased kernel dependency, multivariate version of Hoeffding's Phi, SchweizerWolff's sigma and kappa, complex mutual information, CauchySchwartz quadratic mutual information, Euclidean distance based quadratic mutual information, distance covariance, distance correlation,
 `divergence (D)`: KullbackLeibler divergence (relative entropy), L2 divergence, R�nyi divergence, Tsallis divergence, Hellinger distance, Bhattacharyya distance, maximum mean discrepancy (kernel distance, an integral probability metric), Jdistance (symmetrised KullbackLeibler divergence), CauchySchwartz divergence, Euclidean distance based divergence,
+ `divergence (D)`: KullbackLeibler divergence (relative entropy), L2 divergence, R�nyi divergence, Tsallis divergence, Hellinger distance, Bhattacharyya distance, maximum mean discrepancy (kernel distance, an integral probability metric), Jdistance (symmetrised KullbackLeibler divergence), CauchySchwartz divergence, Euclidean distance based divergence, energy distance (specially the CramerVon Mises distance),
 `association measures (A)`, including `measures of concordance`: multivariate extensions of Spearman's rho (Spearman's rank correlation coefficient, grade correlation coefficient),
 code: [zip](https://bitbucket.org/szzoli/ite/downloads/ITE0.25_code.zip), [tar.bz2](https://bitbucket.org/szzoli/ite/downloads/ITE0.25_code.tar.bz2),
+ code: [zip](https://bitbucket.org/szzoli/ite/downloads/ITE0.26_code.zip), [tar.bz2](https://bitbucket.org/szzoli/ite/downloads/ITE0.26_code.tar.bz2),
code/H_I_D_A_C/base_estimators/DEnergyDist_estimation.m
+%We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
+% Gabor J. Szekely and Maria L. Rizzo. A new test for multivariate normality. Journal of Multivariate Analysis, 93:5880, 2005. (metric space of negative type)
+% Gabor J. Szekely and Maria L. Rizzo. Testing for equal distributions in high dimension. InterStat, 5, 2004. (R^d)
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
+%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+%You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.
code/H_I_D_A_C/base_estimators/DEnergyDist_initialization.m
+%Initialization of the energy distance estimator. The estimation is based on pairwise distances of the sample points.
+% 2)We use the naming convention 'D<name>_initialization' to ease embedding new divergence estimation methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
+%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+%You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.
code/H_I_D_A_C/meta_estimators/DEnergyDist_DMMD_estimation.m
+%Estimates the energy distance (D) according to the relation: D(f_1,f_2;rho) = 2 [MMD(f_1,f_2;k)]^2, where MMD denotes maximum mean discrepancy and k is a kernel that generates rho, a semimetric of negative type.
+% 1)We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
+% Dino Sejdinovic, Arthur Gretton, Bharath Sriperumbudur, and Kenji Fukumizu. Hypothesis testing using pairwise distances and associated kernels. International Conference on Machine Learning (ICML), pages 11111118, 2012. (semimetric space; energy distance <=> MMD, with a suitable kernel)
+% Russell Lyons. Distance Covariance in metric spaces. Technical report, Indiana University, 2011. http://arxiv.org/abs/1106.5758. (energy distance, metric space of negative type; preequivalence to MMD)
+% Gabor J. Szekely and Maria L. Rizzo. A new test for multivariate normality. Journal of Multivariate Analysis, 93:5880, 2005. (energy distance; metric space of negative type)
+% Gabor J. Szekely and Maria L. Rizzo. Testing for equal distributions in high dimension. InterStat, 5, 2004. (energy distance; R^d)
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
+%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+%You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.
code/H_I_D_A_C/meta_estimators/DEnergyDist_DMMD_initialization.m
+%Initialization of the energy distance estimator. The computation is carried out according to the relation: D(f_1,f_2;rho) = 2 [MMD(f_1,f_2;k)]^2, where MMD denotes maximum mean discrepancy and k is a kernel that generates rho, a semimetric of negative type.
+% 2)We use the naming convention 'D<name>_initialization' to ease embedding new divergence estimation methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
+%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+%You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.
code/H_I_D_A_C/meta_estimators/IdCov_IHSIC_estimation.m
+%Estimates distance covariance based on the formula: [I(y^1,y^2;rho_1,rho_2)]^2 = 4 [HSIC(y^1,y^2;k)]^2, where HSIC stands for the HilbertSchmidt independence criterion, y=[y^1;y^2] has density f, y^is have density f_is, and k=k_1 x k_2, where k_is generates rho_is, semimetrics of negative type used in distance covariance.
+% 1)We use the naming convention 'I<name>_estimation' to ease embedding new mutual information estimation methods.
+% Dino Sejdinovic, Arthur Gretton, Bharath Sriperumbudur, and Kenji Fukumizu. Hypothesis testing using pairwise distances and associated kernels. International Conference on Machine Learning (ICML), pages 11111118, 2012. (equivalence to HSIC)
+% Russell Lyons. Distance Covariance in metric spaces. Technical report, Indiana University, 2011. http://arxiv.org/abs/1106.5758. (generalized distance covariance, rho_i; equivalence to HSIC)
+% Gabor J. Szekely and Maria L. Rizzo and. Brownian distance covariance. The Annals of Applied Statistics, 3:12361265, 2009. (distance covariance)
+% Gabor J. Szekely, Maria L. Rizzo, and Nail K. Bakirov. Measuring and testing dependence by correlation of distances. The Annals of Statistics, 35:27692794, 2007. (distance covariance)
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
+%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+%You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.
code/H_I_D_A_C/meta_estimators/IdCov_IHSIC_initialization.m
+%Initialization of the distance covariance estimator. The estimation is carried out based on the formula: [I(y^1,y^2;rho_1,rho_2)]^2 = 4 [HSIC(y^1,y^2;k)]^2, where HSIC stands for the HilbertSchmidt independence criterion, y=[y^1;y^2] has density f, y^is have density f_is, and k=k_1 x k_2, where k_is generates rho_is, semimetrics of negative type used in distance covariance.
+% 2)We use the naming convention 'I<name>_initialization' to ease embedding new mutual information estimation methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
+%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+%You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.
code/ITE_install.m
 %[FN,status] = urlwrite('http://www.gps.caltech.edu/~tapio/arfit/arfit.zip','arfit.zip');%this webpage seems to unavailable temporarily
 [FN,status] = urlwrite('http://www.mathworks.com/matlabcentral/fileexchange/174arfit?download=true','arfit.zip');
CHANGELOG.txt