Smoothed Empirical Likelihood

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Documentation for package ‘smoothemplik’ version 0.0.14

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

brentMin	Brent's local minimisation
brentZero	Brent's local root search
bw.CV	Bandwidth Selectors for Kernel Density Estimation
bw.rot	Silverman's rule-of-thumb bandwidth
ctracelr	Compute empirical likelihood on a trajectory
dampedNewton	Damped Newton optimiser
DCV	Density cross-validation
getSELWeights	Construct memory-efficient weights for estimation
interpTwo	Monotone interpolation between a function and a reference parabola
kernelDensity	Kernel density estimation
kernelDiscreteDensitySmooth	Density and/or kernel regression estimator with conditioning on discrete variables
kernelFun	Basic univatiate kernel functions
kernelMixedDensity	Density with conditioning on discrete and continuous variables
kernelMixedSmooth	Smoothing with conditioning on discrete and continuous variables
kernelSmooth	Local kernel smoother
kernelWeights	Kernel-based weights
logTaylor	Modified logarithm with derivatives
LSCV	Least-squares cross-validation function for the Nadaraya-Watson estimator
pit	Probability integral transform
prepareKernel	Check the data for kernel estimation
smoothEmplik	Smoothed Empirical Likelihood function value
sparseMatrixToList	Convert a weight vector to list
sparseVectorToList	Convert a weight vector to list
svdlm	Least-squares regression via SVD
tlog	d-th derivative of the k-th-order Taylor expansion of log(x)
trimmed.weighted.mean	Weighted trimmed mean
weightedEL	Self-concordant multi-variate empirical likelihood with counts
weightedEL0	Uni-variate empirical likelihood via direct lambda search
weightedEuL	Multi-variate Euclidean likelihood with analytical solution