prep.tfun

a list of formulae for transforming freed parameters 
other than variance-covariance parameters during the optimization process. 
These transformation functions may be helpful for transforming parameters 
that would normally appear on a constrained scale to an unconstrained 
scale (e.g., parameters that can only take on positive values can be 
subjected to exponential transformation to ensure positivity.)

formula.trans

a list of formulae that inverse the transformation 
on the free parameters and will be used to calculate the starting values 
of the parameters.

formula.inv

a logical value indicating whether the functions in 
formula.trans need to be transformed to functions in C. The default 
for transCcode is TRUE, which means that the formulae will be translated 
to C functions and utilized during the optimization process. 
If transCcode = FALSE, the transformations are only performed at the end 
of the optimization process for standard error calculations but not 
during the optimization process.

transCcode


Create a dynrTrans object to handle the transformations and inverse 
transformations of model paramters


Dynamic modeling of all kinds in R. These include models of
processes in discrete time or continuous time. They also include processes
that are linear or nonlinear. Latent variables can be continuous (e.g. state
space models) or discrete (e.g. regime-switching models). The general approach
involves maximum likelihood estimation of single- and multi-subject models of
latent time series with the extended Kalman filter and Kim filter. The user
provides recipes and data which are combined into a model that is then cooked to
obtain free parameter estimates.

prep.tfun: Create a dynrTrans object to handle the transformations and inverse transformations of model paramters

Description

Usage

Arguments