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Changelog

Source: NEWS.md

gkwdist 1.0.7

gkwdist 1.0.5

Documentation Improvements

  • Enhanced Examples for Likelihood Functions: All ll*, gr*, and hs* functions now include comprehensive examples demonstrating:

    • Maximum likelihood estimation with analytical gradients
    • Univariate profile likelihoods with confidence thresholds
    • 2D likelihood surfaces with confidence regions (90%, 95%, 99%)
    • Confidence ellipses with marginal intervals for parameter pairs
    • Numerical vs analytical derivative verification
    • Likelihood ratio tests and score tests

  • Professional Visualization Standards:

    • Consistent color scheme across all examples
    • Grid-adaptive algorithms for computational efficiency
    • Base R only - no external dependencies required

  • Complete Coverage: Enhanced documentation for all distribution families (Kw, EKw, KKw, GKw) covering 2 to 5 parameters

  • Theoretical References: Documentation cites foundational work by Carrasco et al. (2010), Jones (2009), Kumaraswamy (1980), and standard inference theory from Casella & Berger (2002)

gkwdist 1.0.3

  • README.md: Fix typos and faill link
    • Fix zzz.R file by removing useless texts

gkwdist 1.0.2

gkwdist 1.0.1

Major Improvements

Enhanced gkwgetstartvalues() Function

  • NEW: Added family parameter to support all distribution families
    • Automatically returns correct number of parameters for each family
    • Family-specific initial value strategies for better convergence
    • Supported families: "gkw", "bkw", "kkw", "ekw", "mc", "kw", "beta"
    • Case-insensitive family names for user convenience

Documentation Enhancements

  • README.md: Complete rewrite with mathematical rigor
    • All LaTeX formulas corrected and verified for proper rendering
    • Eight comprehensive examples using optim() with analytical gradients
    • Corrected function signatures: all ll*(), gr*(), and hs*() functions use (par, data) signature
    • Added performance benchmarks demonstrating 10-50× speedup with C++ implementation
    • Hierarchical structure diagram for all distribution families
    • Model selection workflow and practical guidelines
    • Removed all references to deprecated gkwfit() function

CRAN Submission Readiness

  • DESCRIPTION: Fixed to meet CRAN requirements
    • Proper Authors@R field formatting
    • Removed unused dependencies (numDeriv)
    • Corrected package dependencies (RcppArmadillo only in LinkingTo)
    • Enhanced description with DOI references
    • Fixed maintainer email formatting

Bug Fixes

  • Fixed function call signatures in all README examples to match actual implementation
  • Corrected parameter passing in optimization examples (now consistently use (par, data))
  • Fixed LaTeX rendering issues with \left/\right delimiters in GitHub Markdown

Testing

  • NEW: Comprehensive test suite using testthat
    • 100+ tests covering all exported functions
    • Tests for all 7 distribution families (GKw, BKw, KKw, EKw, MC, Kw, Beta)
    • PDF, CDF, quantile, and random generation tests
    • Log-likelihood, gradient, and Hessian validation
    • Parameter recovery tests with MLE
    • Edge cases and boundary condition handling
    • Integration tests for PDF-CDF consistency

Performance

  • All functions implemented in C++ for maximum computational efficiency
  • Analytical derivatives (gradient and Hessian) provide exact computations
  • Optimized numerical stability for extreme parameter values

Notes

  • This is the initial CRAN submission
  • Package focuses exclusively on distribution functions (no high-level fitting interface)
  • Companion package gkwreg provides regression modeling capabilities
  • All user-facing functions maintain backward compatibility
  • C++ implementation uses RcppArmadillo for linear algebra operations
  • Analytical functions use robust log-scale computations to prevent overflow/underflow
  • Random generation uses inverse CDF method where closed-form solutions exist

gkwdist 0.1.0

New Features

  • Initial CRAN release
  • Generalized Kumaraswamy distribution (5 parameters)
  • Six nested sub-families: Beta, Kumaraswamy, Exponentiated-Kumaraswamy, Kumaraswamy-Kumaraswamy, Beta-Kumaraswamy, and McDonald distributions
  • Complete set of distribution functions (d/p/q/r)
  • Log-likelihood, gradient, and Hessian functions for all families

Performance

  • Optimized C++ implementation via Rcpp
  • Vectorized operations for speed