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pgam (version 0.3.4)

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

238

Version

0.3.4

License

GPL version 2 or later

Maintainer

Washington Junger

Last Published

August 19th, 2022

Functions in pgam (0.3.4)

coef.pgam

Coefficients extraction
aihrio

Sample dataset
pgam.filter

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

Fitted values extraction
residuals.pgam

Residuals extraction
pgam.smooth

Smoothing of nonparametric terms
intensity

Utility function
AIC.pgam

AIC extraction
pgam.parser

Read the model formula and split it into the parametric and nonparametric partitions
backfitting

Backfitting algorithm
link

Utility function
framebuilder

Utility function
predict.pgam

Prediction
envelope

Normal plot with simulated envelope of the residuals.
pgam.hes2se

Utility function
pgam.fit

One-step ahead prediction and variance
g

Utility function
pgam.psi2par

Utility function
pgam.likelihood

Likelihood function to be maximized
periodogram

Raw Periodogram
fnz

Utility function
pgam.par2psi

Utility function
deviance.pgam

Deviance extraction
logLik.pgam

Loglik extraction
plot.pgam

Plot of estimated curves
print.summary.pgam

Summary output
lpnorm

Utility function
elapsedtime

Utility function
summary.pgam

Summary output
pgam

Poisson-Gamma Additive Models
f

Utility function