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ibr (version 1.3.1)

Iterative Bias Reduction

Description

an R package for multivariate smoothing

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Version

Install

install.packages('ibr')

Monthly Downloads

242

Version

1.3.1

License

GPL (>= 2)

Maintainer

Pierre-Andre Cornillon

Last Published

January 6th, 2011

Functions in ibr (1.3.1)

kernelSx

Evaluates the smoothing matrix at x*
plot.forwardibr

Plot diagnostic for an ibr object
plot.ibr

Plot diagnostic for an ibr object
kernel

Kernel evaluation
iterchoiceA

Selection of the number of iterations for iterative bias reduction smoothers
betaS1

Coefficients for iterative bias reduction method.
ibr-package

Iterative Bias Reduction
cvobs

Selection of the number of iterations for iterative bias reduction smoothers
fittedA

Evaluates the fits for iterative bias reduction method
betaA

Calculates coefficients for iterative bias reduction smoothers
iterchoiceS1cve

Selection of the number of iterations for iterative bias reduction smoothers with base thin-plate splines smoother
summary.ibr

Summarizing iterative bias reduction fits
tpsSx

Evaluate the smoothing matrix at any point
calcA

Decomposition of the kernel smoother
iterchoiceAe

Selection of the number of iterations for iterative bias reduction smoothers
iterchoiceS1e

Number of iterations selection for iterative bias reduction model
bwchoice

Choice of bandwidth achieving a prescribed effective degree of freedom
forward

Iterative bias reduction smoothing
print.summary.ibr

Printing iterative bias reduction summaries
tracekernel

Trace of product kernel smoother
print.summary.npregression

Printing iterative bias reduction summaries
predict.ibr

Predicted values using iterative bias reduction smoothers
ibr

Iterative bias reduction smoothing
departnoyau

Trace of the product kernel smoother
BIC

Information Criterion for ibr
iterchoiceS1

Number of iterations selection for iterative bias reduction model
npregression

Local polynomials smoothing
predict.npregression

Predicted values using using local polynomials
AIC.ibr

Summarizing iterative bias reduction fits
fittedS1

Evaluate the fit for iterative bias reduction model
sumvalpr

Sum of a geometric series
ozone

Los Angeles ozone pollution data, 1976.
poids

Product kernel evaluation
iterchoiceAcv

Selection of the number of iterations for iterative bias reduction smoothers
iterchoiceAcve

Selection of the number of iterations for iterative bias reduction smoothers
tpssmoother

Evaluate the smoothing matrix, the radial basis matrix, the polynomial matrix and their associated coefficients
lambdachoice

Choice of bandwidth according to a given effective degree of freedom
iterchoiceS1cv

Selection of the number of iterations for iterative bias reduction smoothers with base thin-plate splines smoother
summary.npregression

Summarizing local polynomial fits