Compositional Maximum Likelihood Estimation
BFGS Solver
Chain Solvers with Early Stopping
Clear derivative cache
Compose Multiple Function Transformations
Compose Multiple Solvers Sequentially
compositional.mle: Compositional Maximum Likelihood Estimation
Coordinate Ascent Solver
Backtracking line search
Golden section line search along one coordinate
Check if cli package is available
Create a progress handler for optimization
Finalize trace recorder into trace data
Fisher Scoring Solver
Get Fisher information function from problem
Get score function from problem
Gradient Ascent Solver
Sequential Solver Composition
Grid Search Solver
Check if solver converged
Check if object is an mle_constraint
Check if object is an mle_numerical
Check if object is an mle_problem
Check if tracing is enabled
L-BFGS-B Solver (Box Constrained)
Merge trace data from multiple results
Create domain constraint specification
Create an MLE Problem Specification
Create a Trace Configuration
Nelder-Mead Solver (Derivative-Free)
Create a trace recorder
Newton-Raphson Solver
Normal Sampler Factory
Get number of iterations
Extract Optimization Path as Data Frame
Elastic net penalty (combination of L1 and L2)
L1 penalty function (LASSO)
L2 penalty function (Ridge)
Plot Optimization Convergence
Plot Trace Data Directly
Print MLE Trace Data
Parallel Solver Racing (Operator)
Race Multiple Solvers
Random Search Solver
Record an iteration to trace
Simulated Annealing Solver
Uniform Sampler Factory
Conditional Refinement
Update an mle_problem
Verbose Output Utilities
Add penalty term to log-likelihood
Multiple Random Restarts
Create stochastic log-likelihood with subsampling
Provides composable optimization strategies for maximum likelihood estimation (MLE). Solvers are first-class functions that combine via sequential chaining, parallel racing, and random restarts. Implements gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free methods with support for constrained optimization and tracing. Returns 'mle' objects compatible with 'algebraic.mle' for downstream analysis. Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization" <doi:10.1007/978-0-387-40065-5>.