NEWS

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fingerPro 2.1 (2026-04-15)

API Refinement and CRAN Preparation

This version focuses on standardizing the package for CRAN, refining the public API, and improving data ingestion workflows.

Key Features

• Parameter Standardization: The seed parameter is renamed to rng_init across the entire package (unmix, CR, CTS_explore, CTS_select, CI, etc.) to clearly indicate its role in initializing the random generator.
• Semantic Renaming:
  • CTS_seeds() is now CTS_explore() to better describe the exploration of tracer combinations.
  • CTS_error() is now CTS_select(), featuring an improved interface that returns a filtered dataset.
• New Utility Functions:
  • read_database(): A centralized entry point that handles file reading, validation, mixture selection, and zero-value detection.
  • validate_results(): Diagnostic function for post-unmixing analysis.
• Function Visibility: Internal validation functions are now hidden from the public API using @keywords internal.

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fingerPro 2.0 (2025-08-27)

Refinement and Variability Propagation

A major development cycle aimed at polishing the package for CRAN. While the initial objective was to include the Conservative Balance (CB) method, the project evolved into a deep revision of almost all existing functions and documentation.

Key Features

• Advanced Unmixing API: Supports variability (SD vs SEM), Linear Variability Propagation (lvp), and Latin Hypercube resolution.
• C++ Backend Expansion: Core C++ code includes unmix_c_lvp and variability correction helpers (correct_sem and correct_sd).
• Technical Metrics:
  • 900 lines of code added or modified.
  • 500 lines of new documentation and help files.
• Workflow: Transitioned to a Pull-Request (PR) based development model to facilitate collaborative updates.

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fingerPro 1.4

**Restructuring and Standardization

This phase modernized the codebase by gathering dispersed functions and adopting consistent naming conventions.

Key Features

• Naming Convention: Transitioned all files and functions to snake_case (e.g., box_plot.R, DFA_test.R).
• Method Generalization: CR and CTS logic rewritten to support any number of sources (previously limited to 3 or 4).
• New Methods: Introduced Conservative Balance (CB_method) for isotopic tracers (initial implementation).
• Technical Metrics:
  • 2,400 lines of code added or modified.
  • 560 lines of new documentation.

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fingerPro 1.3

Major Expansion: Selection and Logic

A significant growth phase that introduced the core tracer selection framework.

Key Features

• Codebase Size: Total of 3,200 lines of R code (800 of which are documentation) and 1,620 lines of C code.
• Tracer Selection Suite: Introduced Consensus Ranking (CR), Consistency Error (CTS), and the Conservativeness Index (CI).
• Analytic Solving: Added C++ support for least_squares_c and "triangles" methods.
• Pre-calculated Data: Added support for pre-averaged data (mean/SD/n) via the Means parameter.

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fingerPro 1.1 (2018-08-28)

Maintenance and Metadata

• 1.2: Updated README.md to include CRAN/Zenodo badges and formal DOI citations.
• 1.1: Maintenance release; identical to the 1.0 code base.

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fingerPro 1.0 (2018-02-20)

The Full R Package Transition

This version marked the transformation from a single-function bridge into a comprehensive statistical toolset.

• Algorithm: New optimization using Latin Hypercube sampling with iterative variability corrections.
• Statistical Testing: Built-in functions for Kruskal-Wallis (KW) and Discriminant Function Analysis (DFA).
• Graphic Suite: Implemented a wide range of plots using ggplot2, including PCA biplots, LDA plots, and contribution density plots.

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fingerPro 0.9

The Rcpp Bridge

The first iteration as an R package, originally named fingerprinting. It provided a single Rcpp wrapper around the original C algorithm, implementing a Goodness of Fit (GOF).

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fingerPro 0.8

Standalone C Program

The original implementation of the unmixing logic.

• Interface: Command-line execution using input.txt and solution.txt.
• Dependencies: GNU Scientific Library (GSL).
• Algorithm: Random weight generation (sorted uniforms) with quadratic GOF optimization.