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.