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'))

Peer review:

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

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:

22 exports 7 stars 1.59 score 109 dependencies 250 downloads

Last updated 10 months agofrom:07d20c7a7a

Exports:%>%assign_behaviorassign_tsegcluster_obscluster_segmentsdf_to_listdiscrete_move_varexpand_behaviorextract_propfilter_timefind_breaksget_behav_histget_breakptsget_MAPinsert_NAsplot_breakpointsprep_dataround_track_timesegment_behaviorshiny_trackssummarize_tsegstraceplot

Dependencies:base64encbslibcachemclassclassIntclicodacodetoolscolorspacecommonmarkcpp11crayoncrosstalkDBIdigestdplyrdygraphse1071evaluatefansifarverfastmapfontawesomefsfurrrfuturegenericsggplot2globalsgluegtablehighrhmshtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleafletleaflet.providerslifecyclelistenvlubridatemagrittrMASSMatrixMatrixModelsmcmcMCMCpackmemoisemgcvmimemunsellnlmeparallellypillarpkgconfigpngprettyunitsprogressprogressrpromisesproxypurrrquantregR6rappdirsrasterRColorBrewerRcppRcppArmadillorlangrmarkdowns2sassscalessfshinysourcetoolsspSparseMstringistringrsurvivalterratibbletictoctidyrtidyselecttimechangetinytexunitsutf8vctrsviridisLitewithrwkxfunxtablextsyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Assign behavior estimates to observationsassign_behavior
Add segment numbers to observationsassign_tseg
Internal function that adds segment numbers to observationsassign_tseg_internal
Internal function that runs RJMCMC on a single animal IDbehav_gibbs_sampler
Internal function that transforms a vector of bin numbers to a presence-absence matrixbehav_seg_image
Cluster observations into behavioral statescluster_obs
Cluster time segments into behavioral statescluster_segments
Internal function that calculates the inverted cumsumCumSumInv
Convert data frame to a list by animal IDdf_to_list
Discretize movement variablesdiscrete_move_var
Expand behavior estimates from track segments to observationsexpand_behavior
Extract behavior proportion estimates for each track segmentextract_prop
Filter observations for time interval of interestfilter_time
Find changes for integer variablefind_breaks
Extract bin estimates from Latent Dirichlet Allocation or mixture modelget_behav_hist
Extract breakpoints for each animal IDget_breakpts
Find the maximum a posteriori (MAP) estimate of the MCMC chainget_MAP
Internal function that calculates the sufficient statistics for the segmentation modelget_summary_stats
Internal function to calculate the log-likelihood for iteration of mixture modelget.llk.mixmod
Internal function to calculate theta parameterget.theta
Insert NA gaps to regularize a time seriesinsert_NAs
Internal function that calculates the log marginal likelihood of each model being comparedlog_marg_likel
Plot breakpoints over a time series of each movement variableplot_breakpoints
Internal function for plotting breakpoints over each of the data streamsplot_breakpoints_behav
Calculate step lengths, turning angles, net-squared displacement, and time stepsprep_data
Internal function to calculate step lengths, turning angles, and time stepsprep_data_internal
Internal function that samples z's from a categorical distributionrmultinom1
Internal function that samples z's from a multinomial distributionrmultinom2
Round time to nearest intervalround_track_time
Internal function for the Gibbs sampler within the reversible-jump MCMC algorithmsamp_move
Internal function to sample the gamma hyperparametersample.gamma.mixmod
Internal function to sample bin estimates for each movement variablesample.phi
Internal function to sample bin estimates for each movement variablesample.phi.mixmod
Internal function to sample parameter for truncated stick-breaking priorsample.v
Internal function to sample parameter for truncated stick-breaking priorsample.v.mixmod
Internal function to sample latent clusterssample.z
Internal function to sample latent clusters (for observations)sample.z.mixmod
Internal function that samples z1 aggregateSampleZAgg
Segmentation model to estimate breakpointssegment_behavior
Dynamically explore tracks within Shiny appshiny_tracks
This function helps store z from all iterations after burn inStoreZ
Summarize observations within bins per track segmentsummarize_tsegs
Internal function that summarizes bin distributions of track segmentssummarize1
Internal function that generates nmat matrix to help with multinomial drawsSummarizeDat
View trace-plots of output from Bayesian segmentation modeltraceplot
Simulated set of three tracks.tracks
Tracks discretized and prepared for segmentation.tracks.list
Segmented tracks for all IDs.tracks.seg