Package: CountsEPPM 3.1

David M. Smith

CountsEPPM: Mean and Variance Modeling of Count Data

Modeling under- and over-dispersed count data using extended Poisson process models as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .

Authors:David M Smith, Malcolm J Faddy

CountsEPPM_3.1.tar.gz
CountsEPPM_3.1.tar.gz(r-4.5-noble)CountsEPPM_3.1.tar.gz(r-4.4-noble)
CountsEPPM_3.1.tgz(r-4.4-emscripten)CountsEPPM_3.1.tgz(r-4.3-emscripten)
CountsEPPM.pdf |CountsEPPM.html✨
CountsEPPM/json (API)

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

Peer review:

Datasets:

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

2.48 score 30 scripts 236 downloads 24 exports 7 dependencies

Last updated 12 months agofrom:5fbef982eb. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 06 2024
R-4.5-linuxOKDec 06 2024

Exports:coef.CountsEPPMcooks.distance.CountsEPPMCountsEPPMEPPMprobFaddyprob.generalFaddyprob.limitingfitted.CountsEPPMhatvalues.CountsEPPMLL.gradientLL.Regression.CountslogLik.CountsEPPMLRTruncationModel.CountsModel.FaddyModel.FaddyJMV.generalModel.FaddyJMV.limitingplot.CountsEPPMpredict.CountsEPPMprint.CountsEPPMprint.summaryCountsEPPMresiduals.CountsEPPMsummary.CountsEPPMvcov.CountsEPPMwaldtest.CountsEPPM

Dependencies:expmFormulalatticelmtestMatrixnumDerivzoo

Mean and Scale-Factor Modeling of Under- and Overdispersed Count Data

Rendered fromVignette_RSP.pdf.asisusingR.rsp::asison Dec 06 2024.

Last update: 2024-01-11
Started: 2024-01-11

Readme and manuals

Help Manual

Help pageTopics
Fitting of EPPM models to count and binary data.CountsEPPM-package
Ceriodaphnia dataceriodaphnia.group
Extraction of model coefficients for CountsEPPM Objectscoef.CountsEPPM
Cook's distance for CountsEPPM Objectscooks.distance.CountsEPPM
Fitting of EPPM models to count data.CountsEPPM
Calculation of vector of probabilities for a extended Poisson process model (EPPM).EPPMprob
Calculation of vector of probabilities for a Faddy distribution.Faddyprob.general
Calculation of vector of probabilities for the limiting form of the Faddy distribution.Faddyprob.limiting
Extraction of fitted values from CountsEPPM Objectsfitted.CountsEPPM
Extraction of hat matrix values from CountsEPPM Objectshatvalues.CountsEPPM
Green-backed herons as two groupsherons.case
Green-backed herons as two groupsherons.group
Function used to calculate the first derivatives of the log likelihood with respect to the model parameters.LL.gradient
Function called by optim to calculate the log likelihood from the probabilities and hence perform the fitting of regression models to the binary data.LL.Regression.Counts
Method for CountsEPPM objectlogLik.CountsEPPM
Probabilities for distributions truncated on the left (lower) and/or right (upper).LRTruncation
Number of trials (implantations) in data of Luning, et al. (1966)Luningetal.litters
Function for obtaining output from distributional models.Model.Counts
Function for Faddy distribution with log link.Model.Faddy
Function for a general Faddy distribution modeled by means and scale-factors.Model.FaddyJMV.general
Function to fit the limiting form of a Faddy distribution for under-dispersed counts.Model.FaddyJMV.limiting
Diagnostic Plots for CountsEPPM Objectsplot.CountsEPPM
Prediction Method for CountsEPPM Objectspredict.CountsEPPM
Printing of CountsEPPM Objectsprint.CountsEPPM
Printing of summaryCountsEPPM Objectsprint.summaryCountsEPPM
Residuals for CountsEPPM Objectsresiduals.CountsEPPM
Method for CountsEPPM objectsummary.CountsEPPM
Takeover bids data.takeover.bids.case
Titanic survivors dataTitanic.survivors.case
Variance/Covariance Matrix for Coefficientsvcov.CountsEPPM
Wald Test of Nested Models for CountsEPPM Objectswaldtest.CountsEPPM
Number of trials (implantations) of data of Williams (1996).Williams.litters