NEWS
pnd 0.1.13
- Fix: GitHub issue #5 -- simplified the dimension check for Jacobians
- Fix: eliminated a typo in the documentation
pnd 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
pnd 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
pnd 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
pnd 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
pnd 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
pnd 0.0.8 (2025-03-06)
- Fix: sped up CPU core request diagnostics for 1-core operations
- Fix: Using full paths on Macs
pnd 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
pnd 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
pnd 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
pnd 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
pnd 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
pnd 0.0.2
- Fix: bug in stencil calculation
pnd 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