Constructing Joint Distributions with Control Over Statistical Properties


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Documentation for package ‘covalchemy’ version 1.0.0

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augment_matrix_random_block Augment Matrix with Random 2x2 Block Adjustment
calculate_tv_distance_empirical Calculate Total Variation (TV) Distance Empirically
entropy_pair Calculate Entropy of a Pair
gaussian_copula_two_vars Generate Gaussian Copula Samples for Two Variables
genCDFInv_akima Generate an Inverse CDF Function Using Akima Spline Interpolation
genCDFInv_linear Generate an Inverse CDF Function Using Linear Interpolation
genCDFInv_poly Generate an Inverse CDF Function Using Polynomial Regression
genCDFInv_quantile Generate an Inverse CDF Function Using Quantiles
generate_gaussian_copula_samples Generate Gaussian Copula Samples
generate_t_copula_samples Generate t-Copula Samples
gen_number_1 Generate a New Number for Stepwise Modification
gen_number_max Generate a New Number for Maximizing Mutual Information
gen_number_min Generate a New Number for Minimizing Mutual Information
get_mutual_information Calculate Mutual Information
get_optimal_grid Get Optimal Grid Assignment
get_simpsons_paradox_c Simpson's Paradox Transformation with Copula and Simulated Annealing
get_simpsons_paradox_d Introduce Simpson's Paradox in Discrete Data
get_target_corr Generate Samples with Target Kendall's Tau Correlation Using a Copula Approach
get_target_entropy Get Target Entropy
log_odds_dc Log-Odds Calculation for Concordant and Discordant Pairs
objective_function_SL Objective Function for Structural Learning (SL)
plot_log_odds Plot Log-Odds Before and After Transformation
simulated_annealing_MI Simulated Annealing Algorithm with Target Entropy Stopping Condition
simulated_annealing_SL Simulated Annealing Optimization with Categorical Variable and R^2 Differences
sinkhorn_algorithm Sinkhorn Algorithm for Matrix Scaling
softmax Softmax Function with Special Handling for Infinite Values
t_copula_two_vars Generate t-Copula Samples for Two Variables