BreedReliabR 0.1.1

• Add Santosh Patil as a package author for substantial R programming and coding contribution. Ashutosh Sawarkar remains the sole maintainer.
• Update the maintainer email and add author contact and ORCID metadata.
• Metadata-only release; no statistical, API, or package behaviour changes.

BreedReliabR 0.1.0

First research release

• Freeze the seven-function research API for externally supplied prediction means and full joint prediction-error covariance.
• Provide M0–M3 comparison, expected selected-set reliability, candidate-level advancement probabilities, and the relative uncertainty diagnostic Omega(H).
• Add geometric target-environment support diagnostics and an H-curve workflow.
• Clarify assumptions, fixed-k interpretation, Monte Carlo error, RNG behavior, candidate-major covariance ordering, and the limits of geometric support.
• Retain the simulated maize data and add a reproducible methods/empirical validation vignette. No G2F raw data are included.
• External evidence: 2024 validation and forward year-held-out 2022 replication found poor absolute calibration, M2 worst, a small M3 Brier advantage over M1, and overpredicted selected-set reliability. The corrected L3a 2022 baseline is canonical; the original denominator error and correction are retained in the separate audit archive.

Known limitations: probabilities are conditional on the supplied upstream predictive distribution and target-environment specification. Empirical calibration is poor in the two validation settings; software release readiness does not imply calibrated probabilities or guaranteed genetic gain.

BreedReliabR 0.0.3

• Added br_compare() and br_curve() as wrappers around the unchanged H2 core.
• Added range and standardized nearest-environment support diagnostics.
• Added compact S3 print, summary and plot methods for the new result classes.
• Added entirely simulated maize data, a reproducible generation script, and an executable end-to-end vignette with explicit fit-import and calibration limits.
• Added executable examples to all principal public functions and regression tests.
• Clarified expected-fraction set reliability, Monte Carlo limitations and fixed TPE assumptions. No core mathematics or H2 tests were changed.