Description Usage Arguments Details Value
Random Walk with timevarying autocorrelation
1 2 3 
data 
a dataframe of observations (see details) 
tstep 
time step as a proportion of 24 h 
formula 
regression formula for the covariates; default: 
center 
center the covariates; default = FALSE 
nu 
fixed df for tdistributed measurement errors 
g0 
initial value for stationary gamma (move autocorrelation) on 0, 1 
span 
degree of loess smoothing for location initial values 
optim 
numerical optimizer 
verbose 
report progress during minimization 
amfCRAWL 
Argos error class multiplication factors 
Fit a random walk with timevarying autocorelation, via TMB, to ssmfiltered track data.
The input track is given as a data_frame where each row is a location 'observed' without error and columns
observation time (POSIXct, GMT),
observed longitude,
observed latitude,
a list with components

a dataframe of estimated states: gamma (tva model); lon, lat, gamma (tvae model) 

model parameter summmary 

input data 

the TMB object 

the object returned by the optimizer 

AIC statistic: 2 * NLL + 2 * k 
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