Evaluates complex erf, erfc, Faddeeva, and density of sum of
Gaussian and Student's t
Evaluates the probability density function of the sum of
the Gaussian and Student's t density on 3 degrees of freedom.
Evaluates the p.d.f. of the sphered Student's t density
function. Also evaluates the erf, and erfc functions on
complex-valued arguments. Thanks to Krishna Myneni the function
is calculates the Faddeeva function also!

Evaluates the probability density function of the sum of
the Gaussian and Student's t density on 3 degrees of freedom.
Evaluates the p.d.f. of the sphered Student's t density
function.  Also evaluates the erf, and erfc functions on
complex-valued arguments. Thanks to Krishna Myneni the function
is calculates the Faddeeva function also!

Evaluates Complex Erf, Erfc, Faddeeva, and Density of Sum of
Gaussian and Student's t
Evaluates the probability density function of the sum of the
Gaussian and Student's t density on 3 degrees of freedom.
Evaluates the p.d.f. of the sphered Student's t density function.
Also evaluates the erf, and erfc functions on
complex-valued arguments. Thanks to Krishna Myneni the function
is calculates the Faddeeva function also!

CIPW Norm (acronym from the surnames of the authors: Cross, Iddings, Pirrson and Washington)
is the most commonly used calculation algorithm to estimate the standard mineral assemblages
for igneous rocks from its geochemical composition. NORRRM (acronym from noRm, R language and
Renee) is the highly consistent program to calculate the CIPW Norm.

Generation of Multivariate Data with Arbitrary Marginals
person("Po", "Su", email = "desolator@sjtu.edu.cn",
role = c("aut", "cre"))
An implementation of a specific method for generating
n-dimensional random vectors with given marginal distributions and
correlation matrix. The method uses the NORTA (NORmal To Anything)
approach which generates a standard normal random vector and then
transforms it into a random vector with specified marginal distributions
and the RA (Retrospective Approximation) algorithm which is a generic
stochastic root-finding algorithm. The marginals can be continuous or
discrete. See the vignette of package for more details.