Package: distantia 1.0.2

distantia: Assessing Dissimilarity Between Multivariate Time Series

Provides tools to assess the dissimilarity between multivariate time-series. It is based on the psi measure described by Birks and Gordon (1985 <doi:10.1002/jqs.3390020110>), which computes dissimilarity between irregular time-series constrained by sample order. However, in this package the original idea has been extended to work with any kind of multivariate time-series, no matter whether they are regular, irregular, aligned or unaligned. Furthermore, the package allows to assess the significance of dissimilarity values by applying a restricted permutation test, allows to measure the contribution of individual variables to dissimilarity, and offers tools to transfer attributes (generally time or age, but other are possible) between sequences based on the similarity of their samples.

Authors:Blas M. Benito

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distantia.pdf |distantia.html✨
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NEWS

# Install 'distantia' in R:
install.packages('distantia', repos = c('https://blasbenito.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/blasbenito/distantia/issues

Datasets:
  • climate - Dataframe with palaeoclimatic data.
  • climateLong - Dataframe with palaeoclimatic data.
  • climateShort - Dataframe with palaeoclimatic data.
  • pollenGP - Pollen dataset.
  • sequenceA - Multivariate and irregular time series with pollen counts.
  • sequenceB - Multivariate and irregular time series with pollen counts.
  • sequencesMIS - Dataframe with pollen counts for different MIS stages.

On CRAN:

27 exports 13 stars 1.74 score 43 dependencies 6 scripts 828 downloads

Last updated 10 months agofrom:5a3a07bb44. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 12 2024
R-4.5-winNOTESep 12 2024
R-4.5-linuxNOTESep 12 2024
R-4.4-winNOTESep 12 2024
R-4.4-macNOTESep 12 2024
R-4.3-winNOTESep 12 2024
R-4.3-macNOTESep 12 2024

Exports:.psiToDataframeautoSumdistancedistance_chidistance_euclideandistance_hellingerdistance_manhattandistanceMatrixdistancePairedSamplesformatPsihandleNAleastCostleastCostMatrixleastCostPathleastCostPathNoBlocksplotMatrixprepareSequencespsiworkflowImportanceworkflowImportanceHPworkflowNullPsiworkflowNullPsiHPworkflowPartialMatchworkflowPsiworkflowPsiHPworkflowSlottingworkflowTransfer

Dependencies:arrangementsclicodetoolscolorspacedata.tabledoParalleldotCall64fansifarverfieldsforeachggplot2gluegmpgridExtragtableisobanditeratorslabelinglatticelifecyclemagrittrmapsMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcpprlangscalesspamtibbleutf8vctrsviridisviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
distantia: Assessing Dissimilarity Between Multivariate Time Seriesdistantia-package distantia
Computes sum of distances between consecutive samples in a multivariate time-series.autoSum
Dataframe with palaeoclimatic data.climate
Dataframe with palaeoclimatic data.climateLong
Dataframe with palaeoclimatic data.climateShort
Distance Between Two Vectorsdistance
Chi Distance Between Two Vectorsdistance_chi
Euclidean Distance Between Two Vectorsdistance_euclidean
Hellinger Distance Between Two Vectorsdistance_hellinger
Manhattan Distance Between Two Vectorsdistance_manhattan
Computes distance matrices among the samples of two or more multivariate time-series.distanceMatrix
Computes distance among pairs of aligned samples in two or more multivariate time-series.distancePairedSamples
Formats the output of 'psi'.formatPsi
Handles emtpy and NA data in a multivariate time series.handleNA
Extracts the least cost from a least-cost path.leastCost
Computes a least cost matrix from a distance matrix.leastCostMatrix
Find the least cost path in a least cost matrix.leastCostPath
Extracts the least-cost from a least cost matrix by trimming blocks.leastCostPathNoBlocks
Plots distance matrices and least cost paths.plotMatrix
Pollen dataset.pollenGP
Prepare sequences for a comparison analysis.prepareSequences
Computes sum of distances between consecutive samples in a multivariate time-series.psi
Multivariate and irregular time series with pollen counts.sequenceA
Multivariate and irregular time series with pollen counts.sequenceB
Dataframe with pollen counts for different MIS stages.sequencesMIS
Computes the contribution to dissimilarity of each variable.workflowImportance
Computes the contribution to dissimilarity of each variable using workflowPsiHP.workflowImportanceHP
Computes the dissimilarity measure _psi_ on restricted permutations of two or more sequences.workflowNullPsi
Computes the dissimilarity measure _psi_ on restricted permutations of two or more sequences. High performance version with limited optionsworkflowNullPsiHP
Finds the section in a long sequence that better matches a short sequence.workflowPartialMatch
Computes the dissimilarity measure _psi_ on two or more sequences.workflowPsi
A refactored version of 'workflowPsi' with a higher performance (hence the suffix HP).workflowPsiHP
Slots two sequences into a single composite sequence.workflowSlotting
Transfers an attribute (time, age, depth) from one sequence to anotherworkflowTransfer