dt_rcpp

Probability density function of non-central student t (written in C++)

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Brett T McClintock

momentuHMM

Maximum Likelihood Analysis of Animal Movement Behavior Using
Multivariate Hidden Markov Models

dt_rcpp function

<dl><dt>x</dt>
<dd>Vector of quantiles</dd>
<dt>df</dt>
<dd>degrees of freedom</dd>
<dt>ncp</dt>
<dd>non-centrality parameter</dd></dl>

Arguments

student t density function — dt_rcpp

<dl>

<dt>x</dt>
<dd>Vector of quantiles</dd>


<dt>df</dt>
<dd>degrees of freedom</dd>


<dt>ncp</dt>
<dd>non-centrality parameter</dd>

</dl>

dt_rcpp: student t density function

Description

Usage

Value

Arguments