Changes in version 0.1.13                        

  - Fix: GitHub issue #5 -- simplified the dimension check for Jacobians
  - Fix: eliminated a typo in the documentation

                 Changes in version 0.1.2 (2026-02-12)                  

  - Fix: relaxed tolerances in some tests
  - Fix: made a better default zero tolerance for generic derivative and
    accuracy orders

                 Changes in version 0.1.1 (2025-09-03)                  

  - Fix: added a simpler and more reliable fall-back option for step.M
  - Feature: added plotting methods for step... functions
  - Feature: added arbitrary derivation and accuracy order to
    Curtis--Reid method

                 Changes in version 0.1.0 (2025-05-20)                  

  - Feature: original kink-based algorithm for step size selection
    step.K()
  - Feature: added safety shrinking if FUN(x) is finite but FUN(x+h) is
    not in all SSS routines
  - Feature: added S3 printing methods for derivatives and step sizes
  - Feature: removed the diagnostics and report arguments; the iteration
    information is always saved, but not printed
  - Feature: added support for max.rel.error for all step-selection
    methods
  - Feature: all step-search methods now return both the truncation and
    the rounding-error estimate
  - Fix: corrected the wrong formula for the plug-in step size
  - Fix: now 1x1 Hessians can be computed (why, though, if second
    derivatives exist?)
  - Fix: added the v argument for numDeriv compatibility

                 Changes in version 0.0.10 (2025-04-02)                 

  - Fix: GitHub issue #2 -- checkDimensions could not handle character h
    passed for auto-selection
  - Fix: GitHub issue #1 -- function arguments in ... did non propagate
    properly to step... functions

                 Changes in version 0.0.9 (2025-03-11)                  

  - Fix: fixed a regression with the default step size
  - Fix: parallelised Hessians in the same manner as gradients
  - Feature: compatibility of Hessian() with the arguments for methods
    "Richardson" and "simple" from numDeriv

                 Changes in version 0.0.8 (2025-03-06)                  

  - Fix: sped up CPU core request diagnostics for 1-core operations
  - Fix: Using full paths on Macs

                 Changes in version 0.0.7 (2025-03-01)                  

  - Fix: removed obsolete environment creation for cluster export
  - Fix: changed physical core detection on Macs
  - Misc: the package has been added to CRAN, fewer syntax changes are
    expected

                 Changes in version 0.0.6 (2025-02-25)                  

  - Fix: Derivatives of vectorised functions are working. Example:
    Grad(sin, 1:4)
  - Feature: Auto-detecting the number of cores available on multi-core
    machines to speed up computations
  - Feature: Added plug-in step size selection with an estimated f'''
    with a rule of thumb
  - Feature: Auto-detection of parallel type
  - Feature: Added zero tolerance to the default step for a fixed step

                        Changes in version 0.0.5                        

  - Feature: Extended the step-selection routines to gradients (vector
    input x)
  - Feature: Parallelisation of step selection in all algorithms
  - Feature: Mathur's AutoDX algorithm for step size selection step.M()
  - Feature: Added Hessian() that supports central differences (for the
    moment) and arbitrary accuracy
  - Feature: Separate Grad() and Jacobian() that call the workhorse,
    GenD(), for compatibility with numDeriv

                        Changes in version 0.0.4                        

  - Feature: Stepleman--Winarsky algorithm for step size selection
    step.SW()
  - Feature: Automated wrapper for step size selection gradstep()
  - Improvement: Safe handling of function errors and non-finite returns
    in step-size selection procedures
  - Improvement: Finite-difference coefficients gained attributes:
    Taylor expansion, coefficient on the largest truncated term, and
    effective accuracy (useful for custom stencils)
  - Improvement: added unit tests for core features

                        Changes in version 0.0.3                        

  - Feature: solveVandermonde() to solve ill-conditioned problems that
    arise in weight calculation
  - Feature: Dumontet--Vignes algorithm for step size selection
    step.DV()
  - Feature: Curtis--Reid algorithm for step size selection step.CR()
    and its modification
  - Feature: Different step sizes for the gradient
  - Fix: If the user supplies a short custom stencil and requests a high
    accuracy order, it will provide the best available order and produce
    a warning
  - Fix: The output of Grad() preserves the names of x and FUN(x), which
    prevents errors in cases where names are required

                        Changes in version 0.0.2                        

  - Fix: bug in stencil calculation

                        Changes in version 0.0.1                        

  - Initial release
  - Computing finite-difference coefficients on arbitrary stencils
  - Computing numerical gradients with reasonable default step sizes
  - Numerical Jacobians
  - Support for mclapply() on *nix systems only