bayesmove v0.1.0

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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.

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bayesmove

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Introduction

The goal of bayesmove is to analyze animal movement using a non-parametric Bayesian framework, which addresses a number of limitations of existing segmentation methods and state-space models. This framework allows the analysis of multiple telemetry and biologging data streams, which must be discretized into a set of bins before they can be analyzed. This package also includes features to check model convergence. Model output are often returned in a format that is tidyverse-friendly, which allows for easy visualization using ggplot2.

Installation

You can install the latest CRAN release with:

install.packages("bayesmove")

You can install the latest stable version of the package from GitHub with:

# install.packages("remotes")
remotes::install_github("joshcullen/bayesmove")

Support

If you are receiving errors from the model output that you believe to be bugs, please report them as issues in the GitHub repo. Additionally, if there are any other features you would like added to this package, please submit them to the issue tracker.

Functions in bayesmove

Name Description
behav_seg_image Internal function that transforms a vector of bin numbers to a presence-absence matrix
assign_behavior Assign behavior estimates to observations
df_to_list Convert data frame to a list by animal ID
assign_tseg_internal Internal function that adds segment numbers to observations
CumSumInv Internal function that calculates the inverted cumsum
discrete_move_var Discretize movement variables
cluster_segments Cluster time segments into behavioral states
behav_gibbs_sampler Internal function that runs RJMCMC on a single animal ID
assign_tseg Add segment numbers to observations
SampleZAgg Internal function that samples z1 aggregate
get_breakpts Extract breakpoints for each animal ID
prep_data Calculate step lengths, turning angles, and time steps
prep_data_internal Internal function to calculate step lengths, turning angles, and time steps
get_summary_stats Internal function that calculates the sufficient statistics for the segmentation model
get_MAP Find the maximum a posteriori (MAP) estimate of the MCMC chain
%>% Pipe operator
log_marg_likel Internal function that calculates the log marginal likelihood of each model being compared
get.theta Internal function to calculate theta parameter
tracks.seg Segmented tracks for all IDs.
get_behav_hist Extract bin estimates from Latent Dirichlet Allocation model
get_MAP_internal Internal function to find the maximum a posteriori (MAP) estimate of the MCMC chain
plot_breakpoints Plot breakpoints over a time series of each movement variable
summarize_tsegs Summarize observations within bins per track segment
plot_breakpoints_behav Internal function for plotting breakpoints over each of the data streams
rmultinom1 Internal function that samples z's from a categorical distribution
sample.phi Internal function to sample bin estimates for each movement variable
sample.v Internal function to sample parameter for truncated stick-breaking prior
traceplot View trace-plots of output from Bayesian segmentation model
rmultinom2 Internal function that samples z's from a multinomial distribution
tracks Simulated set of three tracks.
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
tracks.list Tracks discretized and prepared for segmentation.
round_track_time Round time to nearest interval
find_breaks Find changes for integer variable
sample.z Internal function to sample latent clusters
samp_move Internal function for the Gibbs sampler within the reversible-jump MCMC algorithm
segment_behavior Segmentation model to estimate breakpoints
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Details

Type Package
License GPL-3
Encoding UTF-8
LazyData true
URL https://github.com/joshcullen/bayesmove, https://joshcullen.github.io/bayesmove/
BugReports https://github.com/joshcullen/bayesmove/issues
Language en-US
RoxygenNote 7.1.1
LinkingTo Rcpp, RcppArmadillo
NeedsCompilation yes
Packaged 2020-10-09 01:32:23 UTC; joshcullen
Repository CRAN
Date/Publication 2020-10-16 14:10:02 UTC

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