Function reference • datafsm

Overview Overview of the package
`datafsm`	datafsm: A package for estimating FSM models.
High-level functions These are the functions you will use most of the time
`evolve_model()`	Use a Genetic Algorithm to Estimate a Finite-state Machine Model
`evolve_model_cv()`	Estimate Optimal Number of States of a Finite-state Machine Model
`evolve_model_ntimes()`	Use a Genetic Algorithm to Estimate a Finite-state Machine Model n-times
FSM methods Class methods for ga_fsm object
`print(<ga_fsm>)` `show(<ga_fsm>)` `summary(<ga_fsm>)` `plot(<ga_fsm>,<ANY>)` `barplot(<ga_fsm>)` `dotchart(<ga_fsm>)` `estimation_details(<ga_fsm>)` `best_performance(<ga_fsm>)` `varImp(<ga_fsm>)` `action_vec(<ga_fsm>)` `states(<ga_fsm>)` `predict(<ga_fsm>)`	An S4 class to return the results of using a GA to estimate a FSM with `evolve_model`.
Model Diagnostics Assessing the performance of models generated by this algorithm
`performance()`	Measure Model Performance
`best_performance()`	Extracts performance
`varImp()`	Extracts slot of variable importances
`compare_fsm()`	Compares FSMs
`degeneracy_check()`	Determines if State Matrix is Degenerate for Given Data Set.
Data sets Data sets included in the package
`NV_games`	Empirical prisoner's dilemma games from Nay and Vorobeychik
Details Functions for dealing with the details of data wrangling, fitting models, and extracting data from fitted models
`add_interact_num()`	Add interaction numbers for panel data
`states()`	Extracts number of states
`action_vec()`	Extracts slot of action_vec
`find_wildcards()`	Find Indices for Non-identifiable Elements of State Matrix.
`estimation_details()`	Extracts slot relevant to estimating the fsm
`fitnessCPP()`	Fitness Function in C++
`var_imp()`	Variable Importance Measure for A FSM Model
`build_bitstring()`	Builds Bitstring
`decode_action_vec()`	Decodes Action Vector
`decode_state_mat()`	Decodes State Matrix

Reference

Overview

High-level functions

FSM methods

Model Diagnostics

Data sets

Details