Package: PoisBinNonNor 1.3.3

PoisBinNonNor: Data Generation with Poisson, Binary and Continuous Components

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) <doi:10.1080/00949655.2014.953534>.

Authors:Gul Inan, Hakan Demirtas, Ran Gao

PoisBinNonNor_1.3.3.tar.gz
PoisBinNonNor_1.3.3.zip(r-4.7-any)PoisBinNonNor_1.3.3.zip(r-4.6-any)PoisBinNonNor_1.3.3.zip(r-4.5-any)
PoisBinNonNor_1.3.3.tgz(r-4.6-any)PoisBinNonNor_1.3.3.tgz(r-4.5-any)
PoisBinNonNor_1.3.3.tar.gz(r-4.7-any)PoisBinNonNor_1.3.3.tar.gz(r-4.6-any)
PoisBinNonNor_1.3.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PoisBinNonNor/json (API)

# Install 'PoisBinNonNor' in R:
install.packages('PoisBinNonNor', repos = c('https://bernice0321.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.15 score 14 scripts 364 downloads 14 exports 6 dependencies

Last updated from:c70434217b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK134
source / vignettesOK170
linux-releaseOK91
macos-releaseOK107
macos-oldrelOK111
windows-develOK75
windows-releaseOK73
windows-oldrelOK79
wasm-releaseOK86

Exports:correlation.bound.checkcorrelation.limitsfleishman.coefgen.PoisBinNonNorintermediate.corr.BBintermediate.corr.BCintermediate.corr.CCintermediate.corr.PBintermediate.corr.PCintermediate.corr.PPoverall.corr.matvalidation.binvalidation.corrvalidation.skewness.kurtosis

Dependencies:BBcorpcorlatticeMatrixmvtnormquadprog

Readme and manuals

Help Manual

Help pageTopics
Data Generation with Count, Binary and Continuous ComponentsPoisBinNonNor-package PoisBinNonNor
Checks if the pairwise correlation among variables are within the feasible rangecorrelation.bound.check
Computes lower and upper correlation bounds for each pair of variablescorrelation.limits
Computes the coefficients of Fleishman third order polynomialsfleishman.coef
Simulates a sample of size n from a set of multivariate Poisson, binary, and continuous datagen.PoisBinNonNor
Computes an intermediate normal correlation matrix for binary variables given the specified correlation matrixintermediate.corr.BB
Computes intermediate correlation matrix for binary and continuous variables given the specified correlation matrixintermediate.corr.BC
Computes an intermediate correlation matrix for continuous variables given the specified correlation matrixintermediate.corr.CC
Computes the pairwise entries of the intermediate normal correlation matrix for all Poisson-binary combinations given the specified correlation matrix.intermediate.corr.PB
Computes the pairwise entries of the intermediate normal correlation matrix for all Poisson-continuous combinations given the specified correlation matrix.intermediate.corr.PC
Computes an intermediate normal correlation matrix for Poisson variables given the specified correlation matrixintermediate.corr.PP
Computes the final intermediate correlation matrixoverall.corr.mat
Validates the marginal specification of the binary variablesvalidation.bin
Validates the specified correlation matrixvalidation.corr
Validates the marginal specification of the continuous variablesvalidation.skewness.kurtosis