NEWS

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compositional.mle 2.0.0 (2026-03-19)

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)