Compositional Maximum Likelihood Estimation

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Documentation for package ‘compositional.mle’ version 1.0.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

%>>%	Sequential Solver Composition
%|%	Parallel Solver Racing (Operator)
bfgs	BFGS Solver
chain	Chain Solvers with Early Stopping
clear_cache	Clear derivative cache
compose	Compose Multiple Solvers Sequentially
compose_transforms	Compose Multiple Function Transformations
coordinate_ascent	Coordinate Ascent Solver
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
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)
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)
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_problem	Create an MLE Problem Specification
print.mle_trace	Create a Trace Configuration
print.mle_trace_data	Print MLE Trace Data
race	Race Multiple Solvers
race_operator	Parallel Solver Racing (Operator)
random_search	Random Search Solver
sim_anneal	Simulated Annealing Solver
uniform_sampler	Uniform Sampler Factory
unless_converged	Conditional Refinement
update.mle_problem	Update an mle_problem
with_penalty	Add penalty term to log-likelihood
with_restarts	Multiple Random Restarts
with_subsampling	Create stochastic log-likelihood with subsampling