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