ocomposition: Regression for Rank-Indexed Compositional Data
Version 1.1

Regression model where the response variable is a rank-indexed compositional vector (non-negative values that sum up to one and are ordered from the largest to the smallest). Parameters are estimated in the Bayesian framework using MCMC methods.

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AuthorArturas Rozenas, New York University
Date of publication2015-08-10 09:09:58
MaintainerArturas Rozenas <ar199@nyu.edu>
LicenseGPL (>= 2)
Version1.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("ocomposition")

Man pages

auxiliary: auxiliary functions
data: Example data
fitcomp: Gibbs sampler for parameter estimation
negbin: Truncated negative binomial distribution function and...
plot.comphat: Plot predicted composition.
predict.composition: Predicted compositional vector
summary.composition: Summary function

Functions

a Man page Source code
cntr Man page Source code
data Man page
dtnegbin Man page Source code
fitcomp Man page Source code
logit Man page Source code
plot.comphat Man page Source code
predict.composition Man page Source code
rmnorm Man page Source code
rtnegbin Man page Source code
summary.composition Man page Source code
v.y Man page Source code
y.v Man page Source code

Files

NAMESPACE
data
data/data.rda
R
R/predict.composition.R
R/a.R
R/logit.R
R/v.y.R
R/rmnorm.R
R/cntr.R
R/summary.composition.R
R/y.v.R
R/fitcomp.R
R/dtnegbin.R
R/plot.comphat.R
R/rtnegbin.R
MD5
DESCRIPTION
man
man/predict.composition.Rd
man/auxiliary.Rd
man/summary.composition.Rd
man/plot.comphat.Rd
man/data.Rd
man/fitcomp.Rd
man/negbin.Rd
ocomposition documentation built on May 20, 2017, 4:35 a.m.