NEWS

ALDEx3 1.4.0

Made BLMM reject invalid or ill-conditioned raw anchor Hessians instead of ridge-regularising and inverting them. Draw-specific Newton updates are now checked against their profiled REML objective, and rejected draws fall back selectively to exact lme4 rather than returning pathological uncertainty.

Registered TMB native routines and disabled dynamic symbol lookup for portable compiled-code checks.
Made the ALDEx2 compatibility regression test robust to the R 4.7.0 binomial RNG change by checking overall p-value agreement and matching significance decisions instead of a strict elementwise tolerance.

Harmonized independent scale-uncertainty inputs on standard deviations: sample.sm() now accepts s.sd, and coefficient.sm() now accepts c.sd.
Added explicitly named s.cov and c.cov arguments for covariance matrices. The legacy s.var, s.cor, and c.cor arguments retain their original semantics with deprecation warnings until ALDEx3 2.0.0.
Clarified that gamma is a standard deviation in clr.sm() and tss.sm(), and documented that sample-level and coefficient-level priors have different covariance structures even when their SD inputs are numerically equal.
Added a two-group coefficient-scale example explaining how factor reference levels determine the sign of a group offset.

Added method = "blmm": an ALDEx3-specific approximate mixed-effects engine using a batched profiled REML anchor fit per feature, draw-specific local covariance updates, and exact conditional GLS fixed-effect solves. Falls back to lme4 for features where the approximation cannot be evaluated cleanly.
On small datasets (with S\approx 20) blmm is approximately 40x faster than lme4, that factor should increase substantially as S increases to more realistic levels.
Added feature-level parallelism (n.cores) for method = "blmm" and method = "lme4".
Added dedicated mixed-effects vignette covering model setup, BLMM formulation, validation guidance, and runtime comparison with exact lme4.

Added aldex.plot() for pairwise ALDEx3 model contrasts, with volcano, effect, MA, and waterfall plot modes.
Added aldex.effect() for ALDEx2-inspired effect diagnostics on a single binary ALDEx3 contrast. The function reports the mean contrast estimate, mean pooled within-group standard deviation, mean Cohen's d, and an ALDEx2-style directional overlap diagnostic.
Updated cohensd() documentation and preserved its legacy return value: a feature by Monte Carlo sample matrix of Cohen's d draws.
Effect diagnostics and MA plots now use reconstructed log abundance, logComp + logScale, and clearly report that they require stored Monte Carlo arrays.
Fixed the Quickstart vignette setup so package data such as gut_crohns_data is available during vignette rebuilds.

1000x (approx) speed up in HC3 and HC0 standard error calculations. Note this was rate limiting before so this is a huge performance boost.
Fixed error with how the streamsize variable was calibrated. Leads to massive improvement in memory management.