Smoothed Empirical Likelihood
Bartlett correction factor for empirical likelihood with estimating eq...
Brent's local minimisation
Brent's local root search with extended capabilities
Bandwidth Selectors for Kernel Density Estimation
Silverman's rule-of-thumb bandwidth
Compute empirical likelihood on a trajectory
Damped Newton optimiser
Density cross-validation
Unified empirical likelihood wrapper
Uni-variate empirical likelihood via direct lambda search
Self-concordant multi-variate empirical likelihood with counts
Multi-variate Euclidean likelihood with analytical solution
Extrapolated EL of the first kind (Taylor expansion)
Construct memory-efficient weights for estimation
Kernel density estimation
Density and/or kernel regression estimator with conditioning on discre...
Basic univatiate kernel functions
Density with conditioning on discrete and continuous variables
Smoothing with conditioning on discrete and continuous variables
Local kernel smoother
Kernel-based weights
Modified logarithm with derivatives
Least-squares cross-validation function for the Nadaraya-Watson estima...
Probability integral transform
Check the data for kernel estimation
Smoothed Empirical Likelihood function value
Convert a weight vector to list
Least-squares regression via SVD
d-th derivative of the k-th-order Taylor expansion of log(x)
Weighted trimmed mean
Empirical likelihood methods for asymptotically efficient estimation of models based on conditional or unconditional moment restrictions; see Kitamura, Tripathi & Ahn (2004) <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013) <doi:10.1002/cjs.11183>. Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in 'Rcpp' for speed.