Package: bayesmove 0.2.3

bayesmove: Non-Parametric Bayesian Analyses of Animal Movement

Methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.

Authors:Joshua Cullen [aut, cre, cph], Denis Valle [aut, cph]

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bayesmove.pdf |bayesmove.html✨
bayesmove/json (API)
NEWS

# Install 'bayesmove' in R:

install.packages('bayesmove', repos = c('https://joshcullen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/joshcullen/bayesmove/issues

Pkgdown site:https://joshcullen.github.io

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

tracks - Simulated set of three tracks.
tracks.list - Tracks discretized and prepared for segmentation.
tracks.seg - Segmented tracks for all IDs.

On CRAN:

cpp

4.13 score 8 stars 34 scripts 284 downloads 22 exports 109 dependencies

Last updated 1 years agofrom:07d20c7a7a. Checks:OK: 4 NOTE: 5. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 07 2024
R-4.5-win-x86_64	NOTE	Dec 07 2024
R-4.5-linux-x86_64	NOTE	Dec 07 2024
R-4.4-win-x86_64	NOTE	Dec 07 2024
R-4.4-mac-x86_64	NOTE	Dec 07 2024
R-4.4-mac-aarch64	NOTE	Dec 07 2024
R-4.3-win-x86_64	OK	Dec 07 2024
R-4.3-mac-x86_64	OK	Dec 07 2024
R-4.3-mac-aarch64	OK	Dec 07 2024

Exports:%>%assign_behavior assign_tseg cluster_obs cluster_segments df_to_list discrete_move_var expand_behavior extract_prop filter_time find_breaks get_behav_hist get_breakpts get_MAP insert_NAs plot_breakpoints prep_data round_track_time segment_behavior shiny_tracks summarize_tsegs traceplot

Dependencies:base64enc bslib cachem class classInt cli coda codetools colorspace commonmark cpp11 crayon crosstalk DBI digest dplyr dygraphs e1071 evaluate fansi farver fastmap fontawesome fs furrr future generics ggplot2 globals glue gtable highr hms htmltools htmlwidgets httpuv isoband jquerylib jsonlite KernSmooth knitr labeling later lattice lazyeval leaflet leaflet.providers lifecycle listenv lubridate magrittr MASS Matrix MatrixModels mcmc MCMCpack memoise mgcv mime munsell nlme parallelly pillar pkgconfig png prettyunits progress progressr promises proxy purrr quantreg R6 rappdirs raster RColorBrewer Rcpp RcppArmadillo rlang rmarkdown s2 sass scales sf shiny sourcetools sp SparseM stringi stringr survival terra tibble tictoc tidyr tidyselect timechange tinytex units utf8 vctrs viridisLite withr wk xfun xtable xts yaml zoo

Citation

Development and contributors

Readme and manuals

Help Manual

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