dynr.cook

a dynr model compiled using dynr.model, consisting of recipes for submodels, 
starting values, parameter names, and C code for each submodel

dynrModel

a cumulative proportion indicating the level of desired confidence intervals for
the final parameter estimates (default is .95)

conf.level

(not required for models specified through the recipe functions) the name of a file 
that has the C codes for all dynr submodels for those interested in specifying a model directly in C

infile

a flag (TRUE/FALSE) indicating whether more detailed intermediate output during the 
estimation process should be printed

verbose

a flag (TRUE/FALSE) indicating whether the negative log likelihood function should 
be weighted by the length of the time series for each individual

weight_flag

a flag (TRUE/FALSE) indicating whether users want additional dynr output that can 
be used for diagnostic purposes

debug_flag


Cook a dynr model to estimate its free parameters


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.

dynr.cook: Cook a dynr model to estimate its free parameters

Description

Usage

Arguments

Examples