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)