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compositional.mle 2.0.0

Breaking Changes

• All solvers now return solver_result objects (class vector: c("solver_result", "mle_fit_numerical", "mle_fit")). Per-solver subclasses (mle_gradient_ascent, mle_bfgs, etc.) are removed.
• Solver metadata moved to $solver_info nested list. Access solver name via result$solver_info$name, iterations via result$solver_info$iterations, trace data via result$solver_info$trace_data.
• Composition metadata moved to $solver_info$composition. Access strategy via result$solver_info$composition$strategy, chain results via result$solver_info$composition$chain, etc.
• Solver-specific diagnostics moved to $solver_info$diagnostics (e.g., $diagnostics$final_temp for sim_anneal, $diagnostics$cycles for coordinate_ascent).
• is_mle_numerical() replaced by is_solver_result().
• compose() removed from exports — use chain() instead (superset with early_stop).
• mle_problem() is now an S3 generic dispatching on its first argument.
• Requires algebraic.mle >= 2.0.0.

New Features

• mle_problem.likelihood_model(): Bridge to the likelihood.model package. Pass a likelihood_model object and data to auto-extract loglik/score/fisher.
• fisher_scoring() now correctly reports "fisher_scoring" as solver name (previously reported "newton_raphson").
• coordinate_ascent() gains learning_rate parameter for non-line-search mode.
• grid_search() now errors if the grid would exceed 1 million points.
• race() warns when parallel = TRUE but future is not installed.

Bug Fixes

• sim_anneal() now correctly reports convergence based on temperature cooling (previously always reported converged = TRUE).
• coordinate_ascent() non-line-search mode uses scaled gradient step instead of hardcoded 0.1 step.
• print.mle_trace_data() no longer accesses vector elements before null-check.
• print.mle_problem() correctly detects default (unconstrained) problems.

compositional.mle 1.0.0

CRAN Release

• First CRAN release
• Moved algebraic.mle from Imports to Depends so that generics (params(), se(), confint(), loglik_val(), aic(), nparams(), vcov()) are available immediately when the package is loaded
• Converted \dontrun{} to \donttest{} in examples (CRAN policy)
• Added hypothesize to Suggests
• Added CI via GitHub Actions

compositional.mle 0.2.0

New Features

• Simulated annealing solver (sim_anneal())
• Coordinate ascent solver (coordinate_ascent())
• race() function for explicit parallel solver racing with future support
• chain() function for sequential composition with early stopping
• Convergence diagnostics: plot.mle_numerical() and optimization_path()
• Derivative caching in mle_problem() to avoid redundant computation
• Verbose/progress output via cli for long-running solvers
• Trace aggregation with merge_traces() across composed solvers

Documentation

• Four vignettes: getting-started, theory-and-intuition, case-studies, strategy-design
• pkgdown site at https://queelius.github.io/compositional.mle/

Bug Fixes

• Fixed R CMD check issues (missing imports, example errors)
• Fixed LICENSE copyright holder

compositional.mle 0.1.0

• Initial release with core solver factories and composition operators
• Solvers: gradient_ascent, newton_raphson, bfgs, lbfgsb, nelder_mead, grid_search, random_search
• Composition: %>>% (sequential), %|% (racing), with_restarts(), unless_converged()
• Problem specification with mle_problem() and constraint support
• Tracing system for optimization iteration recording
• Penalty/regularization transformers (L1, L2, elastic net)

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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