Learn R Programming

PoisBinNonNor (version 1.3.3)

Data Generation with Poisson, Binary and Continuous Components

Description

Generation of multiple count, binary and continuous variables simultaneously given the marginal characteristics and association structure. Throughout the package, the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya et al. (2015) .

Copy Link

Version

Install

install.packages('PoisBinNonNor')

Monthly Downloads

224

Version

1.3.3

License

GPL-2 | GPL-3

Maintainer

Ran Gao

Last Published

March 22nd, 2021

Functions in PoisBinNonNor (1.3.3)

intermediate.corr.PC

Computes the pairwise entries of the intermediate normal correlation matrix for all Poisson-continuous combinations given the specified correlation matrix.
intermediate.corr.BB

Computes an intermediate normal correlation matrix for binary variables given the specified correlation matrix
intermediate.corr.BC

Computes intermediate correlation matrix for binary and continuous variables given the specified correlation matrix
correlation.limits

Computes lower and upper correlation bounds for each pair of variables
PoisBinNonNor-package

Data Generation with Count, Binary and Continuous Components
intermediate.corr.PB

Computes the pairwise entries of the intermediate normal correlation matrix for all Poisson-binary combinations given the specified correlation matrix.
gen.PoisBinNonNor

Simulates a sample of size n from a set of multivariate Poisson, binary, and continuous data
fleishman.coef

Computes the coefficients of Fleishman third order polynomials
intermediate.corr.CC

Computes an intermediate correlation matrix for continuous variables given the specified correlation matrix
validation.bin

Validates the marginal specification of the binary variables
correlation.bound.check

Checks if the pairwise correlation among variables are within the feasible range
validation.corr

Validates the specified correlation matrix
validation.skewness.kurtosis

Validates the marginal specification of the continuous variables
overall.corr.mat

Computes the final intermediate correlation matrix
intermediate.corr.PP

Computes an intermediate normal correlation matrix for Poisson variables given the specified correlation matrix