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inferMM 0.1.1

• Fixed the profile estimator’s covariance inverse for singleton clusters by explicitly retaining one-by-one diagonal matrix dimensions.
• Clustered data cleaning now removes rows with a missing value in any clustering column, with a warning reporting the number removed. Both estimators, variance-model screening and reported cluster counts use the same retained data. Added regression tests for singleton clusters, missing and multi-column IDs, unused factor levels, NaN IDs and refitting.
• Strengthened projected-cluster numerical diagnostics: non-finite tail grid evaluations no longer discard an otherwise verified interior root, and refinement warnings are consolidated and recorded. Both the scalar and joint scaled scores are checked. Failures report their stage, reason and numerical diagnostics without returning estimates or intervals; there is no automatic switch to the profile method. Non-finite variance shapes, unrepresentable scales and non-positive joint covariance diagonals fail explicitly. Boundary variance moments and incomplete searches have distinct structured warning reasons. Legacy profile iteration-limit warnings now report the last parameter change and explicitly mark the fit/intervals as unconverged.
• Added opt-in cluster_mm(method = "projected"), an MM-specific Ma-Genton-style conditional projected-score construction with the identity instrument. It retains conditionally Gaussian residuals but does not specify the random-effect density. A scalar Km score is followed by explicit moment updates and a stacked independent-cluster sandwich covariance, not the legacy mean-information approximation or a general efficiency guarantee. Refits retain this method; pointwise mean-confidence intervals are available, but distribution-free prediction quantiles are not. The legacy profile method remains the default. Root, boundary and covariance checks stop invalid fits without returning estimates or intervals.
• Single-curve profile fits now verify standardized mean-score residuals, compare detected acceptable roots by weighted residual sum of squares, and reject boundary, non-positive, non-finite or numerically degenerate solutions. Score minimization supplies candidates, not automatic success. Single-curve covariance uses scaled unregularized information and errors on singular or poorly conditioned matrices instead of silently adding ridge. Diagnostics are stored in convergence; failures carry class inferMM_profile_error, also inheriting from inferMM_convergence_error. No estimates or intervals are returned after a validation error. Automatic variance selection records failed candidates and selects only successful fits. Screening/grouping and the higher-moment profile pilot share the new root checks; the separate clustered solver is unchanged.
• Expanded higher-moment convergence errors to identify the stage, stopping reason, iterations, scaled score norm, tolerance, and continuation progress. Failures can be caught as inferMM_convergence_error; no fit or confidence intervals are returned. Non-Gaussian continuation failures suggest a manual retry with normal working moments and the same variance function, without automatic fallback or a guarantee of convergence or non-Gaussian efficiency.
• Added the opt-in fit_mm_moments() estimator for independent curves. It jointly solves MM mean/variance estimating equations weighted using third and fourth conditional moments, without calling nls().
• Working standardized moments can be estimated from pilot residuals, set using centered-gamma, Student-t or normal moments, or supplied as numbers or concentration-dependent functions. Fixed user-defined variance shapes are supported. This is not a parametric error-density likelihood fit.
• Added full joint working-information (default) and optional sandwich covariance, Wald intervals, pairs-bootstrap refits, and pointwise mean-confidence bands. The nuisance scale is included before extracting the mean covariance.
• Added continuation/root diagnostics and explicit failure for unconverged, boundary, or ill-conditioned solutions. Higher moments do not identify predictive quantiles, so this estimator does not report prediction bands.
• Added standardized Student-t simulation errors with degrees of freedom greater than four. The existing centered-gamma simulation remains available.
• Existing fit_mm(), variance screening/grouping, and clustered estimators retain their defaults. The new estimator does not claim automatic finite- sample coverage, robustness to an incorrect variance shape, or dominance over all weighted or transformation-based methods.

inferMM 0.1.0

• fit_mm() accepts a fixed user-defined positive function of concentration; variance_function() can attach a descriptive label to it.
• screen_mm() and group_mm() accept named variance_functions lists for comparison with built-in candidates, including custom-only screening.
• Custom functions are validated without silently clipping invalid values and retained for prediction, plotting, and wild/pairs bootstrap refits.
• Fixed custom shapes retain the scalar Km profile-score calculation and closed-form Vmax and variance-scale updates. Shape parameters are not fitted; inference and information criteria condition on the supplied shape.
• The clustered fitting algorithm and built-in variance choices are unchanged. User-defined shapes do not implement higher-moment efficient scores.
• Corrected the help pages to call set.seed() outside package functions; random-number handling is unchanged.

inferMM 0.0.3

• Removed the nls fitting path so all single-curve fits now use the manuscript’s Michaelis-Menten profile-score estimator.
• Updated clustered initialization to use the pooled single-curve profile-score fit without an nls fallback.
• Refreshed package documentation to reflect the unified profile-score implementation.

inferMM 0.0.1

• Initial development version.
• Added variance-aware Michaelis-Menten fitting and screening functions.
• Added grouped workflows, simulation helpers, tests, and demo data.

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.