# Continuous Time Stochastic Modelling using Template Model Builder

## Documentation for package ‘ctsmTMB’ version 1.1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

create.Ornstein1D.model | Create a 1D (1 state, 1 observation) Ornstein-Uhlenbeck model with input-driven mean value. The state is directly observed.
create.Ornstein2D.model | Create a 2D (2 states, 2 observations) Ornstein-Uhlenbeck model with input-driven mean value in the first state and a lagged second state. The states are directly observed.
ctsmTMB | Methods for the 'ctsmTMB' R6 class
EstimateReferenceData | A list of outputs generated from calling the 'estimate' method with the state reconstruction methods 'ekf', 'lkf', 'ukf', 'laplace' and 'laplace.thygesen'.
newModel | Create a ctsmTMB model faster avoiding $...
Ornstein | Sample from a simulated Ornstein-Uhlenbeck process with time-dependent mean
Ornstein2D | Sample from a simulated two-state Ornstein-Uhlenbeck process
OutputReferenceData | A list of outputs generated from calling the 'filter', 'predict' and 'simulate' method on the Ornstein2D data.
plot.ctsmTMB.fit | This function creates residual plots for an estimated ctsmTMB object
plot.ctsmTMB.pred | Plot of k-step predictions from a ctsmTMB prediction object
plot.ctsmTMB.profile | Plot a profile likelihood ctsmTMB object
print.ctsmTMB | Basic print of ctsmTMB objects
print.ctsmTMB.fit | Basic print of objects ctsmTMB fit objects
profile.ctsmTMB.fit | Performs full multi-dimensional profile likelihood calculations
summary.ctsmTMB.fit | Basic summary of ctsmTMB fit object