compositional.mle1.0.2 package

Compositional Maximum Likelihood Estimation

bfgs

BFGS Solver

chain

Chain Solvers with Early Stopping

clear_cache

Clear derivative cache

compose_transforms

Compose Multiple Function Transformations

compose

Compose Multiple Solvers Sequentially

compositional.mle-package

compositional.mle: Compositional Maximum Likelihood Estimation

coordinate_ascent

Coordinate Ascent Solver

dot-backtracking_line_search

Backtracking line search

dot-coordinate_line_search

Golden section line search along one coordinate

dot-has_cli

Check if cli package is available

dot-progress_handler

Create a progress handler for optimization

finalize_trace

Finalize trace recorder into trace data

fisher_scoring

Fisher Scoring Solver

get_fisher

Get Fisher information function from problem

get_score

Get score function from problem

gradient_ascent

Gradient Ascent Solver

grapes-greater-than-greater-than-grapes

Sequential Solver Composition

grid_search

Grid Search Solver

is_converged

Check if solver converged

is_mle_constraint

Check if object is an mle_constraint

is_mle_numerical

Check if object is an mle_numerical

is_mle_problem

Check if object is an mle_problem

is_tracing

Check if tracing is enabled

lbfgsb

L-BFGS-B Solver (Box Constrained)

merge_traces

Merge trace data from multiple results

mle_constraint

Create domain constraint specification

mle_problem

Create an MLE Problem Specification

mle_trace

Create a Trace Configuration

nelder_mead

Nelder-Mead Solver (Derivative-Free)

new_trace_recorder

Create a trace recorder

newton_raphson

Newton-Raphson Solver

normal_sampler

Normal Sampler Factory

num_iterations

Get number of iterations

optimization_path

Extract Optimization Path as Data Frame

penalty_elastic_net

Elastic net penalty (combination of L1 and L2)

penalty_l1

L1 penalty function (LASSO)

penalty_l2

L2 penalty function (Ridge)

plot.mle_numerical

Plot Optimization Convergence

plot.mle_trace_data

Plot Trace Data Directly

print.mle_trace_data

Print MLE Trace Data

race_operator

Parallel Solver Racing (Operator)

race

Race Multiple Solvers

random_search

Random Search Solver

record_iteration

Record an iteration to trace

sim_anneal

Simulated Annealing Solver

uniform_sampler

Uniform Sampler Factory

unless_converged

Conditional Refinement

update.mle_problem

Update an mle_problem

verbose-utils

Verbose Output Utilities

with_penalty

Add penalty term to log-likelihood

with_restarts

Multiple Random Restarts

with_subsampling

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>.

  • Maintainer: Alexander Towell
  • License: MIT + file LICENSE
  • Last published: 2026-02-09 12:20:02 UTC