NEWS

heritable 0.2.0

Added a glmmTMB model backend for Gaussian (identity-link) models #19. All broad- and narrow-sense estimators (H2_*() / h2_*(), including Standard, Cullis, Oakey, Piepho, and Delta) are supported and reproduce the lme4 backend exactly. Non-Gaussian glmmTMB models fail with an informative error.
Added reliability (r², the coefficient of determination) as an additional metric #24. h2_Reliability() / H2_Reliability() return the overall (mean) reliability r̄², h2_Reliability_by_genotype() / H2_Reliability_by_genotype() return the per-genotype values, and H2_Reliability_parameters() computes them from variance components. The measure is also available via h2(method = "Reliability") and H2(method = "Reliability") (not part of the default method set).
h2() and H2() now fail with an informative error for non-Gaussian lme4 models (e.g. glmer()) instead of a cryptic method-dispatch error #48.
Added narrow-sense heritability estimation through h2() and the h2_*() estimator functions for models with a known additive genetic covariance structure.
Added a consistent interface across broad-sense (H2()), narrow-sense (h2()), and their Standard, Cullis, Oakey, Piepho, and Delta estimators.
Added support for more complex asreml and lme4 random-effect structures, including known genomic relationship matrices and structured residuals.
Added richer heritable objects that retain the fitted model and calculation metadata for downstream methods.

Update Maintainer to Dr. Emi Tanaka
Refactored asreml integration #37, #32
- Added var_comp.asreml() for extracting and assembling variance components for asreml model objects
Refactored H2() for asremlmodels so that it now takes arguments like marginal, stratification
- Improved handling of vm() specification
Refactored H2_standard()
Compute confidence intervals for heritable objects #30