Function reference • bayesmove

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`CumSumInv()`	Internal function that calculates the inverted cumsum
`SampleZAgg()`	Internal function that samples z1 aggregate
`StoreZ()`	This function helps store z from all iterations after burn in
`SummarizeDat()`	Internal function that generates nmat matrix to help with multinomial draws
`assign_behavior()`	Assign behavior estimates to observations
`assign_tseg()`	Add segment numbers to observations
`assign_tseg_internal()`	Internal function that adds segment numbers to observations
`behav_gibbs_sampler()`	Internal function that runs RJMCMC on a single animal ID
`behav_seg_image()`	Internal function that transforms a vector of bin numbers to a presence-absence matrix
`cluster_obs()`	Cluster observations into behavioral states
`cluster_segments()`	Cluster time segments into behavioral states
`df_to_list()`	Convert data frame to a list by animal ID
`discrete_move_var()`	Discretize movement variables
`expand_behavior()`	Expand behavior estimates from track segments to observations
`extract_prop()`	Extract behavior proportion estimates for each track segment
`filter_time()`	Filter observations for time interval of interest
`find_breaks()`	Find changes for integer variable
`get.llk.mixmod()`	Internal function to calculate the log-likelihood for iteration of mixture model
`get.theta()`	Internal function to calculate theta parameter
`get_MAP()`	Find the maximum a posteriori (MAP) estimate of the MCMC chain
`get_behav_hist()`	Extract bin estimates from Latent Dirichlet Allocation or mixture model
`get_breakpts()`	Extract breakpoints for each animal ID
`get_summary_stats()`	Internal function that calculates the sufficient statistics for the segmentation model
`insert_NAs()`	Insert NA gaps to regularize a time series
`log_marg_likel()`	Internal function that calculates the log marginal likelihood of each model being compared
`plot_breakpoints()`	Plot breakpoints over a time series of each movement variable
`plot_breakpoints_behav()`	Internal function for plotting breakpoints over each of the data streams
`prep_data()`	Calculate step lengths, turning angles, net-squared displacement, and time steps
`prep_data_internal()`	Internal function to calculate step lengths, turning angles, and time steps
`rmultinom1()`	Internal function that samples z's from a categorical distribution
`rmultinom2()`	Internal function that samples z's from a multinomial distribution
`round_track_time()`	Round time to nearest interval
`samp_move()`	Internal function for the Gibbs sampler within the reversible-jump MCMC algorithm
`sample.gamma.mixmod()`	Internal function to sample the gamma hyperparameter
`sample.phi()`	Internal function to sample bin estimates for each movement variable
`sample.phi.mixmod()`	Internal function to sample bin estimates for each movement variable
`sample.v()`	Internal function to sample parameter for truncated stick-breaking prior
`sample.v.mixmod()`	Internal function to sample parameter for truncated stick-breaking prior
`sample.z()`	Internal function to sample latent clusters
`sample.z.mixmod()`	Internal function to sample latent clusters (for observations)
`segment_behavior()`	Segmentation model to estimate breakpoints
`shiny_tracks()`	Dynamically explore tracks within Shiny app
`summarize1()`	Internal function that summarizes bin distributions of track segments
`summarize_tsegs()`	Summarize observations within bins per track segment
`traceplot()`	View trace-plots of output from Bayesian segmentation model
`tracks`	Simulated set of three tracks.
`tracks.list`	Tracks discretized and prepared for segmentation.
`tracks.seg`	Segmented tracks for all IDs.

Reference

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