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mixedbiastest 1.1.0

• Add compact simulation printing and distinguish numerical validation failures from refit errors. Document plug-in conditioning, no-intercept autoscaling, and use an unbalanced simulation example.

• Correct the Karl-Zimmerman journal volume and page range and link the author manuscript and revisions.

• Clarify that Bias_Estimate is the internal random-effects alignment diagnostic, not an unbiased estimate of total bias or an automatic correction. Explain the additional residual-weight contribution when covariance estimates depend on the response, and the limits of fitted permutation-reference p-values.

• Add simulate_bias() for fixed-design conditional response-refit sensitivity analysis. The default holds the fitted random effects fixed, generates Gaussian residual errors, and re-estimates covariance parameters for every response. Custom generators can specify latent effects and errors for a stated scenario.

• Return replicate-level alignment, residual, total error and internal diagnostic, with means, Monte Carlo standard errors, convergence/failure records and singular fit counts. A centered-error control variate is available without discarding the raw errors; its summaries are withheld after any failed refit because selection can invalidate the mean-zero control-variate argument. This does not reproduce stochastic assignment automatically.

• Share fitted-model algebra between the existing diagnostic and the simulation; preserve the diagnostic API, retained Bias_Estimate column, permutation RNG ordering and two-sided +1 p-value convention.

mixedbiastest 1.0.3

• Correct term-specific covariance handling when several random-effects terms share a grouping factor. Earlier versions could use the first term’s variance for later terms.
• Correct permutations of multi-coefficient diagonal terms to preserve lme4’s group-major coefficient ordering. Handle diagonal covariance representations introduced in lme4 2.0, including the diag_special double-bar option.
• Transform diagnostics from automatically scaled fixed-effect designs back to the original coefficient scale, including intercepts and single predictors.
• Use lme4’s sparse fitted-model factors to compute the diagnostic without inverting the random-effects covariance or flooring zero variances.
• Enforce the documented exclusion of non-unit observation weights using the supported weights accessor, including fits with excluded observations.
• Validate permutation counts, contrasts, contrast names, and verbosity before calculation. List only retained fixed-effect coefficients for rank-deficient fits.
• Preserve full precision in returned bias estimates, permutation means and p-values; small nonzero permutation p-values are no longer rounded to zero. The two-sided test and +1 correction are unchanged.
• Run the regression suite during CRAN checks. Add independent dense-GLS and permutation-reference tests for supported inputs and recent lme4 changes. Gaussian unweighted fits with diagonal random-effects covariance remain the supported scope; correlated structures and GLMMs are not added.

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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