findintercorr2.method = “Fleishman”.calc_theory() and plotting functions which call it to permit pdf specified by fx, lower, and upper.rcorrvar() and rcorrvar2() summary of continuous variables when using method = "Fleishman".rcorrvar(), rcorrvar2(), valid_corr(), valid_corr2(), and error_loop() to permit 0 or 1 continuous variables.calc_lower_skurt() for case of non-convergence when applying Six vector with method = "Polynomial".lower and upper parameters to plot_cdf() to use as inputs for cdf_prob().findintercorr2() so now you can generate 1 ordinal variable using correlation method 2 (with rcorrvar2()).chat_nb() so you can use size (success probability) and mu (mean) parameters for Negative Binomial variables when using correlation method 1 (with rcorrvar1()).find_constants() and calc_lower_skurt() (to remove duplicate rows in solutions before executing pdf_check()) in order to decrease computation time.rcorrvar(), rcorrvar2(), valid_corr(), and valid_corr2() to check for identical continuous distributions before calculating the power method constants in order to decrease computation time. If a distribution is repeated, the constants are only calculated once.error_loop() and error_vars():Sigma is done using the maximum of 0 and the eigenvalues (in case Sigma is not positive-definite and the eigenvalues are negative); this replaces the use of Matrix::nearPD()ifelse() statement in choice of update function (affects negative correlations only)ifelse() statement in choice of update function for ordnorm() (affects negative correlations only).calc_theory():params input accepts up to 4 parametersDist input)plot_pdf_theory(), plot_sim_pdf_theory(), and plot_sim_theory():params input accepts up to 4 parametersDist input) plus Poisson and Negative Binomial for plot_sim_pdf_theory() and plot_sim_theory()ggplot2 parameters to the graphing functions to allow control over the appearance of the legend, axes labels and titles, and plot title.rcorrvar() and rcorrvar2() documentation.Initial package release.