Linear Regression using I-Priors

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Documentation for package ‘iprior’ version 0.6.4

Help Pages

Canonical Reproducing kernels for the I-prior package
datfbm Simulated data to illustrate one-dimensional smoothing
deviance Obtain the log-likelihood and deviance of an 'ipriorMod' or 'ipriorKernel' object
deviance.ipriorKernel Obtain the log-likelihood and deviance of an 'ipriorMod' or 'ipriorKernel' object
deviance.ipriorMod Obtain the log-likelihood and deviance of an 'ipriorMod' or 'ipriorKernel' object
FBM Reproducing kernels for the I-prior package
fbmOptim Find the Hurst coefficient of a FBM I-prior model
fnH1 Reproducing kernels for the I-prior package
fnH2 Reproducing kernels for the I-prior package
fnH3 Reproducing kernels for the I-prior package
Hlam Extract the scaled kernel matrix
Hlam.ipriorKernel Extract the scaled kernel matrix
Hlam.ipriorMod Extract the scaled kernel matrix
hsb High school and beyond dataset
hsbsmall High school and beyond dataset
iprior Fit an I-prior regression model
iprior.default Fit an I-prior regression model
iprior.formula Fit an I-prior regression model
iprior.ipriorKernel Fit an I-prior regression model
iprior.ipriorMod Fit an I-prior regression model
ipriorColPal Colour palette for 'iprior' plots
ipriorOptim Estimate an I-prior model using a combination of EM algorithm and direct optimisation
kernel Reproducing kernels for the I-prior package
kernL Load the kernel matrices of an I-prior model
kernL.formula Load the kernel matrices of an I-prior model
logLik Obtain the log-likelihood and deviance of an 'ipriorMod' or 'ipriorKernel' object
logLik.ipriorKernel Obtain the log-likelihood and deviance of an 'ipriorMod' or 'ipriorKernel' object
logLik.ipriorMod Obtain the log-likelihood and deviance of an 'ipriorMod' or 'ipriorKernel' object
Pearson Reproducing kernels for the I-prior package
plot Plots for 'ipriorMod' objects
plot.ipriorMod Plots for 'ipriorMod' objects
pollution Air pollution and mortality
predict Predict for I-prior models.
predict.ipriorMod Predict for I-prior models.
progress EM algorithm progression results for fitted 'ipriorMod' objects
sigma Obtain the standard deviation of the residuals 'sigma'
sigma.ipriorMod Obtain the standard deviation of the residuals 'sigma'
simdat Random slopes model simulated data
slope Recover the betas (slopes) of the regression curves
vary Extract the variance of the responses