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pgam (version 0.3.3)

Poisson-Gamma Additive Models.

Description

This work is aimed at extending a class of state space models for Poisson count data, so called Poisson-Gamma models, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

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Version

Install

install.packages('pgam')

Monthly Downloads

232

Version

0.3.3

License

GPL version 2 or later

Maintainer

Washington Junger

Last Published

August 19th, 2022

Functions in pgam (0.3.3)

aihrio

Sample dataset
AIC.pgam

AIC extraction
pgam.likelihood

Likelihood function to be maximized
f

Utility function
intensity

Utility function
framebuilder

Utility function
elapsedtime

Utility function
coef.pgam

Coefficients extraction
envelope

Normal plot with simulated envelope of the residuals.
pgam

Poisson-Gamma Additive Models
backfitting

Backfitting algorithm
periodogram

Raw Periodogram
pgam.filter

Estimation of the conditional distributions parameters of the level
deviance.pgam

Deviance extraction
fnz

Utility function
predict.pgam

Prediction
pgam.parser

Read the model formula and split it into the parametric and nonparametric partitions
summary.pgam

Summary output
pgam.psi2par

Utility function
pgam.smooth

Smoothing of nonparametric terms
plot.pgam

Plot of estimated curves
fitted.pgam

Fitted values extraction
print.summary.pgam

Summary output
logLik.pgam

Loglik extraction
link

Utility function
pgam.par2psi

Utility function
pgam.hes2se

Utility function
lpnorm

Utility function
residuals.pgam

Residuals extraction
pgam.fit

One-step ahead prediction and variance
g

Utility function