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algebraic.mle 2.0.1

Bug fixes

• Fixed pred() using point estimate instead of random draw for first Monte Carlo sample, breaking the parameter-uncertainty integration
• Fixed confint() ignoring the parm argument. Now honors parameter subsetting by name or index, matching the stats::confint contract.
• Fixed confint() producing NaN without informative error when variance-covariance has negative diagonal (bad Hessian). Now gives actionable error message.
• Fixed combine() giving cryptic “non-conformable arrays” error when MLEs have different parameter dimensions. Now validates upfront.
• Fixed sampler.mle_fit_boot() unreachable code branch (length(x$t) == 1 is never true for boot objects; corrected to ncol(x$t) == 1L)
• Removed dead stud case in confint.mle_fit_boot() switch
• Fixed stale point() references in roxygen and vignette prose (now params())
• Fixed bias.mle_normal vignette example missing ... in method signature

algebraic.mle 2.0.0

Breaking changes

• S3 class renamed from "mle" to "mle_fit" to resolve name collision with stats4::mle (S4 class). Subclasses follow: "mle_fit_numerical", "mle_fit_boot", "mle_fit_rmap". Constructor function names (mle(), mle_numerical(), mle_boot()) are unchanged.

• Removed aic(), bic(), and loglik_val() generics. Use standard R generics AIC(), BIC(), and logLik() instead. logLik() returns a proper "logLik" object with df and nobs attributes, so AIC() and BIC() work automatically via stats::AIC.default.

• Added coef.mle_fit() method delegating to params() for standard R compatibility.

• Removed mle_weighted() constructor and "mle_weighted" class. Use combine() instead — same inverse-variance weighting with better error handling and a variadic API.

Improvements

• Replaced 1:n patterns with seq_len() throughout to avoid edge-case bugs
• Vectorized PI computation in pred()
• Modernized package documentation ("_PACKAGE" sentinel)
• Cleaned up fixing/ directory (removed dead experimental code)
• Integer coercion for n parameter in rmap()

algebraic.mle 1.2.0

New features

• joint() composes independent MLEs with disjoint parameter sets into a joint MLE with block-diagonal covariance structure
• combine() optimally weights independent MLEs for the same parameter via inverse-variance (Fisher information) weighting
• as_dist() converts MLE objects to their asymptotic normal distributions, bridging to the algebraic.dist distribution algebra
• Distribution methods on MLE objects: density(), cdf(), inv_cdf(), sup(), dim(), mean(), conditional()

Documentation

• Rewrote package Description around “MLE as technology” narrative
• Rewrote README to lead with the algebra (joint, combine, rmap, as_dist)
• Added “The Algebra of MLEs” vignette demonstrating the full composition pipeline

Bug fixes

• Fixed name-propagation issue in rmap() where c() merged parameter names with transformation output names

algebraic.mle 1.1.0

• Add coef() S3 method for base R compatibility (delegates to params())
• Add logLik() S3 method returning proper logLik object with df and nobs attributes, enabling automatic AIC() and BIC() support from base R
• Fix rmap() to accept numeric n parameter (previously required integer)

algebraic.mle 1.0.0

• Initial CRAN release
• Core MLE class (mle) with methods for:
  • Parameter extraction (params, nparams)
  • Variance-covariance (vcov, se)
  • Confidence intervals (confint)
  • Model comparison (AIC, logLik)
  • Bias and MSE estimation (bias, mse)
  • Fisher information (observed_fim)
  • Sampling from MLE distribution (sampler)
  • Predictive intervals (pred)
  • Expected values (expectation)
  • Marginal distributions (marginal)
• Numerical optimization wrapper (mle_numerical) for optim() results
• Bootstrap MLE (mle_boot) for small samples
• MLE transformations via invariance property (rmap)
• Weighted combination of MLEs (mle_weighted)
• Three vignettes demonstrating usage:
  • Fitting common distributions to a DGP
  • Statistics and characteristics of the MLE
  • Data generating processes

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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