gkwdist 1.0.7

gkwdist 1.0.5

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)

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

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

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

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

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

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

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

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