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gldrm (version 1.6)

Generalized Linear Density Ratio Models

Description

Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) . Huang and Rathouz (2012) . Rathouz and Gao (2008) .

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Install

install.packages('gldrm')

Monthly Downloads

197

Version

1.6

License

MIT + file LICENSE

Maintainer

Michael Wurm

Last Published

January 24th, 2024

Functions in gldrm (1.6)

gldrm

Fits a generalized linear density ratio model (GLDRM)
beta.control

Control arguments for \(\beta\) update algorithm
getBeta

Beta optimization routing
gldrmCI

Confidence intervals for gldrm coefficients
gldrmLRT

Likelihood ratio test for nested models
f0.control

Control arguments for f0 update algorithm
gldrm.control

Control arguments for gldrm algorithm
gldrmPIT

Confidence intervals for gldrm coefficients
theta.control

Control arguments for \(\theta\) update algorithm
print.gldrmLRT

Print likelihood ratio test results
predict.gldrm

Predict method for a gldrm object
getf0

f0 optimization routine
gldrmFit

Main optimization function
getTheta

getTheta Updates theta. Vectorized but only updates observations that have not converged.
print.gldrmCI

Print confidence interval
print.gldrm

Print summary of gldrm fit