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smoothemplik 0.next (2025-XX-XX)

These bug fixes and features are scheduled for the upcoming releases.

• BUG: bw.CV(x, y = y, kernel = "quartic", order = 4) and bw.CV(x, y = y, kernel = "quartic")
• BUG: Fix the DCV code with convolutions (especially the quartic one)
• BUG: The check CV = "DCV" and is.null(y) seems redundant.
• BUG: If x contains duplicates, DCV(x, bw = bw.grid, weights = w) complains that no duplicates were found (see the example)
• BUG: LOO estimation: instead of dropping unique (X, Y) observations, leave each conditioning points (only X)
• BUG: Fix the optimiser control argument in bw.CV(), add log() for non-negativity and better scaling.
• SYNTAX: kernelSmooth(), being a local average, should have na.rm and check the inputs
• SYNTAX: In kernelDiscreteDensitySmooth(), remove the table attribute and change the test.
• SYNTAX: Create a summary class for SEL; print numerical gradients of lambdas; print the number of converged inner optimisation problems
• FEATURE: Speed up interpolation by memoising it
• FEATURE: Check if only 4 points, as opposed to 6, are required for extrapolation in weightedEL0
• FEATURE: Create a class for smoothing that would yield LOESS smoothing matrices, with ranks or distances
• FEATURE: For sparseVectorToList(), the default trim(x) should be such that the sum of sorted weights exceeds 0.99999999: trim = \(w) min(which(cumsum(sort(w / sum(w), decreasing = TRUE)) > 1 - 1e-8))
• FEATURE: Create convolution for kernel orders 4 and 6
• FEATURE: add convergence check in brentZero(), like in uniroot().
• FEATURE: De-duplicate at kernel weights already (via .prepareKernel()), return the attribute
• FEATURE: For .prepareKernel() AND mixed kernel: check if the max. column-wise gap between observations is >= than the bandwidth, otherwise write an informative message
• FEATURE: Make DCV either sparse or memsave, not both; reflect the changes in bw.CV()
• FEATURE: Speed up the computation of kernelWeights when x = xgrid by exploiting the symmetry of kernels.
• FEATURE: Remove parallelisation over workers via setThreadOptions when there is outer parallelisation in .kernelMixed()
• FEATURE: Move the de-duplication of the xout grid inside kernelSmooth
• FEATURE: Create a default value for memsave and when to invoke it (based on nx*ng)
• FEATURE: Add weight support to kernelDiscreteDensitySmooth()
• FEATURE: Eliminate matrices in smoothing completely, try only parallel loops
• FEATURE: Extend the CV to non-Gaussian cases.
• FEATURE: CV: implement leave-K-out CV for speed
• FEATURE: In kernelMixedSmooth(): if LOO, do not de-duplicate xout, copy it from arg$x (currently mitigated via deduplicate.xout = FALSE)
• FEATURE: All LOO to the C++ density function
• FEATURE: Add custom kernels to Silverman’s rule of thumb (with roughness != 1)
• FEATURE: Check: if the kernel is finite-support and bandwidth is smaller than the largest gap between two observations, then, set the bandwidth in that dimension to 1.1 times that gap. kernelSmooth() and kernelDensity() should have an argument for increasing small bandwidths in case of zero weights to match the largest gap divided by 2 (times 1.1 to have at least some coverage)
• FEATURE: Like in the SEL application: de-duplicate the input matrix, replace with weights; allow the user to disable it
• FEATURE: Merging cells: allow arbitrary variables (including continuous ones) for proximity.
• MISC: Add references to AEL and BAEL (Chen 2008, Emerson & Owen 2009, … 2011)
• MISC: Check analytical expressions for all combinations of kernels, convolutions, and orders in Sage and Mathematica, publish the reproducing codes
• MISC: Reproduce the CKT (2019) results with the shift argument (i.e. test the shift)
• MISC: Add a vignette for non-parametric methods to GitHub, finish the mixed-smoothing part
• DEV: Check all instances of kernelSmooth(), kernelDensity(), kernelWeights(), and everything that used obsolete arguments in the examples
• DEV: Too much CPU time in the examples for kernelDensity and kernelSmooth (>5 s) (make dontrun?)
• DEV: Add RcppParallel::setThreadOptions(numThreads = "auto") as the 1st line of parallel-capable functions, use setDTthreads also (check how data.table does it)
• DEV: Write test cases for C++ functions with and without speed-ups
• DEV: Check compatibility with R 3.0.0
• DEV: Add tests reproducing simple hard-coded examples
• DEV: Check the release with todor::todor_package(), lintr::lint_package(), R CMD check --as-cran, and goodpractice::gp()

smoothemplik 0.0.15 (2025-XX-XX)

• Rewrote most of the internal functions in Rcpp for higher speed
• Moved weightedEL to weightedEL0 and cemplik – now that it is in C++ – to weightedEL
• Added weighted Euclidean likelihood
• smoothEmplik() accepts attach.attributes = TRUE as a synonym for "all"
• Fixed a bug in the Taylor expansion that could cause a violation of the spanning condition

smoothemplik 0.0.14 (2025-04-30)

• Fixed a bug in the Taylor expansion for the case of convex hull condition failures
• Removed unfinished functions for optimisation and constrained optimisation
• Implemented adaptive bandwidths at each point of the evaluation grid for better smoothing
• Added a draft version of the vignette showcasing the use of adaptive kernels for smoothing
• Implemented a Wald-like Taylor expansion to allow mu to be outside the convex hull in weightedEL()
• Sped up the fourth-order triangular kernel

smoothemplik 0.0.13 (2025-02-05)

• Implemented Taylor expansion to allow mu to be outside the convex hull in weightedEL(), as in Owen (2013)
• Replaced uniroot() with a C++ version of Brent’s zero search for speed
• Improved handling of influential observations in LSCV

smoothemplik 0.0.12 (2024-06-13)

• Fixed a bug in prepareKernel() where a valid y vector with attributes would not pass the check.
• Implemented a more accurate check for lambda being close to the boundary based on the relative search interval length in weightedEL().
• weightedEL() preserves the names of the input vector in wts.
• Sped up ctracelr() by using the previous lambda value in the search (~4 times faster).
• The output of mllog() now has column names because it was confusing without them.
• The output of svdlm() is now a vector, not a 1-column matrix.
• Replaced certain instances of sapply() with vapply() in smoothing functions.
• Added unit tests for some functions.

smoothemplik 0.0.11 (2024-01-13)

• Added EUPL licence.
• Initialised tests for unit testing.
• Fixed the bug in DCV when weights were not taken into account.
• Removed simulation functions for the paper (to be provided separately).
• Added examples for most functions.

smoothemplik 0.0.10 (2023-12-05)

• Feature: support for weighted kernel density and regression observation.
• Feature: support for sparse weight matrices via sparseKernelWeightsCPP() to save memory.
• Feature: observation de-duplication to speed up the non-parametric functions.
• Feature: low-level C++ parallelisation and chunking to limit maximum RAM use (via RcppParallel).
• Feature: leave-one-out kernel smoothing support for custom output grids.
• Reworked mixed kernel density and regression workflow making use of the block data structure.
• Bug fix: now there are time savings in multi-core mixed estimation.
• Bug fix: in DCV, duplicated observations were not merged (now the LOO is the true LOO: duplicates of the same observation are also left out).
• Improved the initial value choice for cross-validation (using a log-scale grid around the rule-of-thumb value).
• Sped up C++ kernel functions with RcppArmadillo (20–50% speed gains through better iterations, code structure, and condition checks).

smoothemplik 0.0.9 (2023-09-08)

• Added a vignette on non-parametric methods.
• Added 4th-order C++ versions of all kernels (for bias reduction) and their convolutions.
• Auto-detect cross-validation type (density or least-squares) based on the input.
• Changed the default behaviour of Silverman’s rule of thumb: use a robust estimator of SD (IQR/1.34).
• Prepared a stub for discontinuous densities.
• Moving the gradient-related functions to a new package, pnd.

smoothemplik 0.0.8 (2023-06-03)

• Rewrote the C++ smoothers using RcppArmadillo for speed-up, refactored the kernel-related code.
• Feature: Support for the 2nd-order uniform, triangular, Epanechnikov, and quartic kernel.

smoothemplik 0.0.7 (2023-03-29)

• Feature: added functions for parallelised numerical differentiation.
• Rewrote multi-variate weighted empirical likelihood functions to allow for Taylor approximations of the empirical likelihood function of any order.

smoothemplik 0.0.6

• Feature: getSELWeights now renormalises the weights to unity after trimming (by default, can be overridden via renormalise = FALSE).

smoothemplik 0.0.5 (2021-10-12)

• Initial release.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.