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hisemi (version 1.1-0)

Hierarchical Semiparametric Regression of Test Statistics

Description

Methods for hierarchical semiparametric regression models for test statistics are implemented in this package. Specifically, test statistics given the null/alternative hypotheses are modeled parametrically, whereas the unobservable status of null/alternative hypotheses are modeled using nonparametric additive logistic regression over covariates.

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Version

Install

install.packages('hisemi')

Monthly Downloads

56

Version

1.1-0

License

GPL (>= 2)

Maintainer

Last Published

July 9th, 2017

Functions in hisemi (1.1-0)

confint.hisemit

Extract Wald-type asymptotic confidence intervals from a hisemit object
directSum

Direct sum of matrices
EMupdate

Utility function performing EM algorithm updates
OsplinePen

O-spline penalty matrix
coef.hisemit

Extracts fitted parameters from a hisemit object
hisemi-package

Hierarchical semiparametric regression model to a large number of parametric test statistics
logLik.hisemit

Extract the log likelihood from a hisemit object
fitted.hisemit

Extract fitted values from a hisemit object
internal functions not to be used by users directly

internal functions not to be used by users directly
logistic.enp

Fit a logistic curve to the raw effective number of parameters over log smoothing parameter
logit

Logit link and its inverse
NRupdate

Utility function performing Newton-Raphson algorithm updates
plot.hisemit

Plot a hisemit object
print.hisemit

Print a summary of a hisemit object
residuals.hisemit

Extract residuals from a hisemit object
rt

Pseudo-random number generation from t-distribution
n.knots

Number of spline knots
penLik.EMNewton

Fits hierarchical semiparametric regression model to t-statistics
vcov.hisemit

Extract the asymptotic variance-covariance matrix of a hisemit object
scaledTMix.null

Fit the null model to t-statistics
scaledTMix.psat

Fits a partially saturated model to t-statistics
scaledTMix.sat

Fits saturated model to t-statistics
tPoly.newton

Fits hierarchical global polynomial regression model to t-statistics