Changes in version 0.2.1                        

  - make_kernel() now checks that the kernel is a symmetric probability
    density and stops with an explanation if it is not; check = FALSE
    builds an unsuitable kernel for inspection only. tilt_density(),
    tilt_density_cv(), tilt_cv() and sharpen_density() refuse such a
    kernel, so it cannot be used for estimation by mistake.
  - Kernel objects gain summary() and plot() methods. summary() returns
    the properties check_kernel() reports, as an object; check_kernel()
    is now a thin wrapper that prints it.

                 Changes in version 0.2.0 (2026-09-28)                  

Changes prompted by editorial review at the Journal of Statistical
Software.

  - sharpen_density() gains an optimizer argument. The default, a
    bounded quasi-Newton search from several starting points, reaches
    the same minimum as the genetic algorithm in a fraction of the time
    when the number of distinct shifts is small; the genetic algorithm
    remains available as "ga", and any user-supplied search function of
    (fn, lower, upper) can be used. The polishing step is now bounded
    (L-BFGS-B).
  - make_kernel() builds a kernel object from a density function alone,
    and check_kernel() reports whether a kernel has the properties the
    estimators assume.
  - conventional_density() evaluates the ordinary kernel estimator with
    the package's own code, so that comparisons do not depend on
    stats::density(), whose output changed between R 4.3 and 4.4.
  - bw_flattop() failed on data with a large scale, such as the waiting
    column of faithful, because its search grid could be shorter than
    the stretch it looked for. Fixed, with a regression test.
  - Help pages for tilt_density() and tilt_density_cv() now say that a
    matrix or data frame is accepted; sharpen_density() gives a clear
    message when handed one.
  - Internal: quadratic forms are computed as sum(p * (A %*% p)) rather
    than t(p) %*% A %*% p, and block products with crossprod(); the
    redundant availability check for quadprog, which is in Imports, is
    removed.

                 Changes in version 0.1.1 (2026-09-21)                  

First release.

  - tilt_density() implements the tilted estimator of Doosti and Hall
    (2016), with weights chosen by minimising the L2 distance to a sinc
    or trapezoidal comparator estimator.
  - When the number of distinct weights m is finite, the block
    boundaries can be chosen in three ways through the breaks argument:
    "optimal" selects them jointly with the weights, as Section 4.1 of
    the 2016 paper specifies; "equal" uses near-equal blocks, Algorithm
    A of the 2018 paper; "modal" places them at the troughs of a pilot
    estimate, the refinement suggested in Section 2 of the 2018 paper.
    Explicit boundaries may also be supplied.
  - tilt_density_cv() implements method (III) of Doosti, Hall and Mateu
    (2018), choosing the bandwidth and the weights together by
    cross-validation.
  - sharpen_density() implements the data-sharpened estimator of Doosti
    and Hall (2016).
  - The kernel of the perturbed estimator is selectable through the
    kernel argument: "gaussian" (the default, and the one used for every
    numerical result in both papers), the Laplace-convolution family
    "laplace" through "laplace4" that Section 3.1 of the 2016 paper
    states its condition (3.1) for, and "epanechnikov", "biweight",
    "triweight" and "triangular". Custom kernels may be supplied as a
    list. tilt_kernels() lists them.
  - Bandwidths are rescaled across kernels using the canonical factor of
    Marron and Nolan (1988), so that the same numerical bandwidth
    smooths by the same amount whichever kernel is used.
    bw_canonical_factor() returns the factor and bw_convert() maps a
    bandwidth between kernels; bw_nrd_robust() gained a kernel argument.
    The estimators apply the rescaling to their default bandwidth, so
    changing the kernel changes the shape of the fit rather than how
    much it is smoothed. For the Gaussian the factor is one, so nothing
    about the papers' setting changes. Weight selection stays a convex
    quadratic program whatever the kernel, because the Gram matrix has
    Fourier transform phi^2 >= 0 and so is positive semidefinite by
    construction.
  - Multivariate data are supported by tilt_density() and
    tilt_density_cv(): pass a matrix. This is Section 3.3 of the 2016
    paper. The product kernel makes every quantity factorise across
    dimensions, so the l-variate matrices are elementwise products of
    the univariate ones and the optimisation problem is unchanged.
    plot() draws a contour for bivariate fits. Data sharpening remains
    univariate.
  - Shape constraints are available through the constraint argument of
    tilt_density() and tilt_density_cv(): "unimodal", "increasing" or
    "decreasing". The estimator is linear in the weights, so a shape
    requirement on a grid becomes linear inequalities and the problem
    stays a convex quadratic program. Shape-constrained tilting is due
    to Hall and Huang (2002); applying it to the cross-validation
    criterion follows the extension suggested in the discussion of
    the 2018 paper.
  - sinc_density() and trapezoid_density() provide the infinite-order
    comparator estimators on their own.
  - bw_comparator_cv(), bw_flattop() and bw_nrd_robust() select
    bandwidths.
  - Fitted objects inherit from "density", so base plotting works
    unchanged, and have print(), summary() and predict() methods.
  - bw_comparator_cv() handles tied observations, which real data
    recorded to limited precision routinely contain.