Package: smoothemplik 0.0.18

Andreï Victorovitch Kostyrka

smoothemplik: Smoothed Empirical Likelihood

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.

Authors:Andreï Victorovitch Kostyrka [aut, cre]

smoothemplik_0.0.18.tar.gz
smoothemplik_0.0.18.zip(r-4.7-x86_64)smoothemplik_0.0.18.zip(r-4.7-arm64)smoothemplik_0.0.18.zip(r-4.6-x86_64)smoothemplik_0.0.18.zip(r-4.6-arm64)
smoothemplik_0.0.18.tar.gz(r-4.7-arm64)smoothemplik_0.0.18.tar.gz(r-4.7-x86_64)smoothemplik_0.0.18.tar.gz(r-4.6-arm64)smoothemplik_0.0.18.tar.gz(r-4.6-x86_64)
smoothemplik_0.0.18.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
smoothemplik/json (API)

# Install 'smoothemplik' in R:
install.packages('smoothemplik', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fifis/smoothemplik/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.30 score 5 scripts 623 downloads 32 exports 8 dependencies

Last updated from:a30bb63fd6. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK189
linux-devel-x86_64OK229
source / vignettesOK340
linux-release-arm64OK149
linux-release-x86_64OK279
windows-devel-arm64OK258
windows-devel-x86_64OK245
windows-release-arm64OK280
windows-release-x86_64OK220
wasm-releaseOK138

Exports:bartlettFactorbrentMinbrentZerobw.CVbw.rotctracelrdampedNewtonDCVELEL0EL1EuLExEL1ExEL2getSELWeightskernelDensitykernelDiscreteDensitySmoothkernelFunkernelMixedDensitykernelMixedSmoothkernelSmoothkernelWeightslogTaylorLSCVpitprepareKernelsmoothEmpliksparseMatrixToListsparseVectorToListsvdlmtlogtrimmed.weighted.mean

Dependencies:data.tablelatticeMatrixrbibutilsRcppRcppArmadilloRcppParallelRdpack

Choosing weights for likelihood smoothing
Kernel methods | Fixed bandwidth | Rank weights | Nearest-neigbour weights | Simulation in 1 dimension | Simulation in 3 dimensions | Conclusion | References
Rendered fromchoice-of-SEL-weights.Rmdusingknitr::rmarkdown

Last update: 2025-10-27
Started: 2025-07-22

Using Rcpp to speed up non-parametric estimation in R
Kernel methods | Speed-ups | Univariate kernel estimation | Multivariate kernel estimation | Sparsity | Conclusion | References
Rendered fromnon-parametric-rcpp.Rmdusingknitr::rmarkdown

Last update: 2025-07-22
Started: 2025-07-22

Readme and manuals

Help Manual

Help pageTopics
Bartlett correction factor for empirical likelihood with estimating equationsbartlettFactor
Brent's local minimisationbrentMin
Brent's local root search with extended capabilitiesbrentZero
Bandwidth Selectors for Kernel Density Estimationbw.CV
Silverman's rule-of-thumb bandwidthbw.rot
Compute empirical likelihood on a trajectoryctracelr
Damped Newton optimiserdampedNewton
Density cross-validationDCV
Unified empirical likelihood wrapperEL
Uni-variate empirical likelihood via direct lambda searchEL0
Self-concordant multi-variate empirical likelihood with countsEL1
Multi-variate Euclidean likelihood with analytical solutionEuL
Extrapolated EL of the first kind (Taylor expansion)ExEL1 ExEL2
Construct memory-efficient weights for estimationgetSELWeights
Kernel density estimationkernelDensity
Density and/or kernel regression estimator with conditioning on discrete variableskernelDiscreteDensitySmooth
Basic univatiate kernel functionskernelFun
Density with conditioning on discrete and continuous variableskernelMixedDensity
Smoothing with conditioning on discrete and continuous variableskernelMixedSmooth
Local kernel smootherkernelSmooth
Kernel-based weightskernelWeights
Modified logarithm with derivativeslogTaylor
Least-squares cross-validation function for the Nadaraya-Watson estimatorLSCV
Probability integral transformpit
Check the data for kernel estimationprepareKernel
Smoothed Empirical Likelihood function valuesmoothEmplik
Convert a weight vector to listsparseMatrixToList sparseVectorToList
Least-squares regression via SVDsvdlm
d-th derivative of the k-th-order Taylor expansion of log(x)tlog
Weighted trimmed meantrimmed.weighted.mean