# Smoothed Empirical Likelihood

## Documentation for package ‘smoothemplik’ version 0.0.15

- 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
EL | Self-concordant multi-variate empirical likelihood with counts
EL0 | Uni-variate empirical likelihood via direct lambda search
EuL | Multi-variate Euclidean likelihood with analytical solution
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