Bivariate Segmentation/Clustering Methods and Tools
Covariate Calculations
Calculate angular speed along a path
apply_rowSums
Internal function for subsampling
Check for argument 'diag.var'
Check for argument 'Kmax'
Check for argument 'lmin'
Check for argument 'ncluster'
Check for argument 'order.var'
Check for argument 'order'
Check for argument 'scale.variable'
Check for argument 'seg.var'
Check for argument 'ncluster' and 'nseg'
Check for argument 'nseg'
Check for deprecated 'type' and 'coord.names' argument
arma_repmat
Generic function for augment
bisig_plot draws the plots of the bivariate signal on the same plot (s...
Calculate BIC
Calculate distance between locations
Calculate speed along a path
Calculate state statistics
Check for repetition in the series
Finding best segmentation with a different threshold S
Internal Function for choosing optimal number of segment
colsums_sapply
cumsum_cpp
DynProg computes the change points given a cost matrix matD and a maxi...
DynProg_algo_cpp
EM.algo_simultanee calculates the MLE of phi for given change-point in...
EM.algo_simultanee calculates the MLE of phi for given change-point in...
EM.init_simultanee proposes an initial value for the EM algorithm base...
Estep_simultanee computes posterior probabilities and incomplete-data ...
Find mean and standard deviation of segments
Gmean_simultanee calculates the cost matrix for a segmentation model w...
Gmixt_algo_cpp
Gmixt_simultanee calculates the cost matrix for a segmentation/cluster...
Gmixt_simultanee_fullcpp
hybrid_simultanee performs a simultaneous seg - clustering for bivar...
initialisePhi is the constructor for a set of parameters for a segclus...
Generic function for likelihood
logdens_simultanee_cpp
plot_segm plot segmented movement data on a map.
matrixRupt transforms a vector of change point into a data.frame with ...
Mstep_simultanee computes the MLE within the EM framework
Mstep_simultanee computes the MLE within the EM framework
neighbors tests whether neighbors of point k,P can be used to re-initi...
Plot segmentation on time-serie
Plot states statistics
Find segment and states for a Picard model
Internal function for HMM
Internal function for HMM
Prepare HMM output for proper comparison plots
Prepare shiftfit output for proper comparison plots
Relabel states of a segmentation/clustering output
repmat repeats a matrix
ruptAsMat is an internal function to transform a vector giving the cha...
Segmentation/Clustering of movement data - Generic function
Internal segmentation/clustering function
segclust2d: tools for segmentation of animal GPS movement data
segmap_list create maps with a list of object of segmentationclass
segmentation class description
Segmentation of movement data - Generic function
Calculate spatial angle along a path
Calculate statistics on a given segmentation
Get segment statistic for HMM model
Get segment statistic for shiftfit model
Internal function for subsampling
Test function generating fake data
DynProg Rcpp DynProg computes the change points given a cost matrix ma...
Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) <doi:10.1101/444794>.