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nabla 0.7.0

• Added vignette “Higher-Order MLE Analysis” demonstrating third-order derivative tensors (D(f, x, order = 3)) for computing asymptotic skewness of Gamma MLEs, with Monte Carlo validation.
• Renamed package from dualr to nabla. The S4 class dualr retains its name (it describes the object type — a dual number in R).
• Added composable total derivative operator D(f):
  • D(f) returns the derivative of f as a new function.
  • D(f, x) evaluates the derivative at x.
  • D(f, x, order = k) applies D k times for k-th order derivative tensors.
  • D(D(f)) composes naturally for higher-order derivatives.
  • Output tensor shape: each application of D appends one n-dimension. For f: R^n -> R: gradient (n), Hessian (n,n), etc. For f: R^n -> R^m: Jacobian (m,n), (m,n,n), etc.
• Unified gradient(), hessian(), and jacobian() as thin wrappers around D, replacing separate seeding strategies with a single composable mechanism. This simplifies the codebase at the cost of O(p) gradient (was O(1) passes) and O(p^2) Hessian (was O(p) passes).
• Removed deprecated second-order functions: dual2_variable(), dual2_constant(), value2(), first_deriv(), second_deriv(), differentiate2(). Use dual_variable_n(), dual_constant_n(), deriv_n(), and differentiate_n() instead.
• Removed internal helpers .make_grad_vector() and .make_grad2_vector().
• Updated higher-order vignette to use current API and demonstrate D operator.

nabla 0.6.0

• Replaced MLE-specific API with general-purpose derivative functions:
  • score() -> gradient() — computes the gradient of any scalar-valued function (still single-pass via vector-valued derivatives).
  • hessian() — unchanged (already mathematically general).
  • observed_information() — removed (trivial: just -hessian()).
  • score_and_hessian() -> jacobian() — generalized to compute the full m x p Jacobian matrix of any f: R^p -> R^m. Accepts functions returning lists, numeric vectors, or scalar dualr objects.
• Renamed R/mle-helpers.R to R/derivatives.R.
• Updated vignettes to use general terminology (gradient/Hessian/Jacobian).

nabla 0.5.0

• Generalized to arbitrary-order exact derivatives via recursive nesting. New API: dual_variable_n(), dual_constant_n(), deriv_n(), differentiate_n().
• Removed Rcpp/C++ fast paths — package is now pure R with no compiled code. This simplifies installation and aligns with the package’s focus on exact arbitrary-order derivatives rather than scalar speed.
• Deprecated second-order-only functions (dual2_variable(), dual2_constant(), value2(), first_deriv(), second_deriv(), differentiate2()) as thin wrappers around the new generalized API.
• Removed benchmarks (speed is not this package’s value proposition).

nabla 0.4.0

• score() now computes the full gradient in 1 forward pass (was p passes) using vector-valued derivatives, exploiting the ANY slots of the dualr class.
• hessian() now computes the full Hessian in p forward passes (was p(p+1)/2) using vector-gradient inner duals with nested outer duals.
• Internal .is_scalar_dual() now also checks length() == 1L to correctly distinguish scalar duals (C++ fast path) from vector-gradient duals (R path).

nabla 0.3.0

• Added Rcpp-based C++ fast paths for first-order dual arithmetic (+, -, *, /, ^), math (exp, sqrt, log), and sum. Provides 3-10x speedup on scalar dual operations while preserving full R fallback for nested (second-order) duals.
• New internal .is_scalar_dual() predicate gates C++ vs R paths using is.double() on slot contents.
• Added Rcpp to Imports and LinkingTo; package now requires C++ compilation.

nabla 0.2.0

• Renamed S4 class from dual to dualr to avoid conflict with base R’s dual usage.
• Added dedicated setMethod dispatches for hot-path arithmetic (+, -, *, /, ^) and math (exp, sqrt) operations, bypassing group generic overhead.
• Extracted .dual_min() / .dual_max() internal helpers, deduplicating 6 inline lambdas across dual-arithmetic.R and dual-math.R.
• Removed dead switch branches (sqrt, exp, log) from Math group generic that were shadowed by dedicated methods.
• Standardized sum() in Summary group generic to use .as_dual() promotion, consistent with prod, min, max, and range.
• Fixed stale \code{compositional.mle} reference in score() documentation.

nabla 0.1.0

• Initial CRAN release.
• S4 dual number class with full arithmetic and math function support.
• Nested duals for exact second-order derivatives (dual2_variable, differentiate2).
• MLE workflow helpers: score, hessian, observed_information, score_and_hessian.
• Special functions: erf, erfc, beta, lbeta, psigamma.
• Four vignettes: introduction, MLE workflow, higher-order derivatives, optimizer integration.

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.