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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.

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.
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