Advanced Toolset for Efficient Time Series Dissimilarity Analysis
Get Column Names from a Time Series Lists
Append Prefix to Column Names of Time Series List
Set Column Names in Time Series Lists
Append Suffix to Column Names of Time Series List
Count NA Cases in Time Series Lists
Diagnose Issues in Time Series Lists
Handle NA Cases in Time Series Lists
Transform Raw Time Series Data to Time Series List
Join Time Series Lists
Clean Time Series Names in a Time Series List
Get Time Series Names from a Time Series Lists
Set Time Series Names in a Time Series List
Tests Naming Issues in Time Series Lists
Get Number of Columns in Time Series Lists
Get Number of Rows in Time Series Lists
Plot Time Series List
Repair Issues in Time Series Lists
Resample Time Series Lists to a New Time
Simulate a Time Series List
Smoothing of Time Series Lists
Summary Statistics of Time Series Lists
Subset Time Series Lists by Time Series Names, Time, and/or Column Nam...
Time Features of Time Series Lists
Transform Time Series List to Data Frame
Transform Values in Time Series Lists
Ensures Correct Class for Time Arguments
Default Block Size for Restricted Permutation in Dissimilarity Analyse...
Common Boxplot Component of distantia_boxplot() and `momentum_boxplo...
Check Input Arguments of distantia()
Checks Input Matrix
Check Input Arguments of momentum()
Checks Least Cost Path
Structural Check for Time Series Lists
Checks Argument x
Check Distance Argument
Checks Classes of List Elements Against Expectation
Clean Character Vector of Names
Optimize the Silhouette Width of Hierarchical Clustering Solutions
Optimize the Silhouette Width of K-Means Clustering Solutions
Compute Silhouette Width of a Clustering Solution
Coerces Vector to a Given Time Class
Auto Breaks for Matrix Plotting Functions
Number of Decimal Places
Split Dissimilarity Analysis Data Frames by Combinations of Arguments
Removes Geometry Column from SF Data Frames
Global Centering and Scaling Parameters of Time Series Lists
Title
Handles Line Colors for Sequence Plots
Guide for Time Series Plots
Color Guide for Matrix Plot
Plot Distance or Cost Matrix and Least Cost Path
New Time for Time Series Aggregation
Optimize Loess Models for Time Series Resampling
Optimize Spline Models for Time Series Resampling
Convert Data Frame to a List of Data Frames
Convert List of Matrices to List of Data Frames
Convert Matrix to Data Frame
Handles Time Column in a List of Data Frames
Convert List of Vectors to List of Data Frames
Convert List of Data Frames to List of Zoo Objects
Rescale Numeric Vector to a New Data Range
Dictionary of Time Keywords
Translates The User's Time Keywords Into Valid Ones
Valid Aggregation Keywords
Data Frame with Supported Time Units
Data Frame with Pairs of Time Series in Time Series Lists
Aggregate Cases in Zoo Time Series
Clean Name of a Zoo Time Series
Get Name of a Zoo Time Series
Set Name of a Zoo Time Series
Random or Restricted Permutation of Zoo Time Series
Plot Zoo Time Series
Resample Zoo Objects to a New Time
Simulate a Zoo Time Series
Exponential Smoothing of Zoo Time Series
Rolling Window Smoothing of Zoo Time Series
Get Time Features from Zoo Objects
Convert Individual Zoo Objects to Time Series List
Coerce Coredata of Univariate Zoo Time Series to Matrix
(C++) Sum Distances Between Consecutive Samples in a Time Series
(C++) Sum Distances Between Consecutive Samples in Two Time Series
(C++) Sum Distances Between All Consecutive Samples in Two Time Series
(C++) Sum Distances Between All Consecutive Samples in the Least Cost ...
Default Continuous Color Palette
Default Discrete Color Palettes
(C++) Compute Orthogonal and Diagonal Least Cost Matrix from a Distanc...
(C++) Compute Orthogonal and Weighted Diagonal Least Cost Matrix from ...
(C++) Compute Orthogonal Least Cost Matrix from a Distance Matrix
Least Cost Path
(C++) Orthogonal and Diagonal Least Cost Path Restricted by Sakoe-Chib...
(C++) Orthogonal and Diagonal Least Cost Path
(C++) Orthogonal Least Cost Path
(C++) Orthogonal Least Cost Path
(C++) Least Cost Path for Sequence Slotting
(C++) Sum Distances in a Least Cost Path
(C++) Remove Blocks from a Least Cost Path
(C++) Bray-Curtis Distance Between Two Vectors
(C++) Canberra Distance Between Two Binary Vectors
(C++) Chebyshev Distance Between Two Vectors
(C++) Normalized Chi Distance Between Two Vectors
(C++) Cosine Dissimilarity Between Two Vectors
(C++) Euclidean Distance Between Two Vectors
(C++) Hamming Distance Between Two Binary Vectors
(C++) Hellinger Distance Between Two Vectors
(C++) Jaccard Distance Between Two Binary Vectors
(C++) Sum of Pairwise Distances Between Cases in Two Aligned Time Seri...
(C++) Manhattan Distance Between Two Vectors
(C++) Distance Matrix of Two Time Series
Data Frame to Distance Matrix
(C++) Russell-Rao Distance Between Two Binary Vectors
(C++) Sørensen Distance Between Two Binary Vectors
Distance Between Two Numeric Vectors
Aggregate distantia() Data Frames Across Parameter Combinations
Distantia Boxplot
Hierarchical Clustering of Dissimilarity Analysis Data Frames
K-Means Clustering of Dissimilarity Analysis Data Frames
Two-Way Dissimilarity Plots of Time Series Lists
Dynamic Time Warping Dissimilarity Analysis of Time Series Lists
Lock-Step Dissimilarity Analysis of Time Series Lists
Convert Dissimilarity Analysis Data Frame to Distance Matrix
Dissimilarity Model Frame
Spatial Representation of distantia() Data Frames
Stats of Dissimilarity Data Frame
Time Shift Between Time Series
distantia: A Toolset for Time Series Dissimilarity Analysis
Dissimilarity Analysis of Time Series Lists
Transform Zoo Object to Binary
Data Transformation: Rowwise Centered Log-Ratio
Data Transformation: Detrending and Differencing
Data Transformation: Linear Detrending of Zoo Time Series
Data Transformation: Polynomial Linear Detrending of Zoo Time Series
Data Transformation: Rowwise Hellinger Transformation
Lists Available Transformation Functions
Data Transformation: Log
Data Transformation: Rowwise Percentages
Data Transformation: Rowwise Square Root of Proportions
Data Transformation: Rowwise Proportions
Data Transformation: Global Rescaling of to a New Range
Data Transformation: Local Rescaling of to a New Range
Data Transformation: Global Centering and Scaling
Data Transformation: Local Centering and Scaling
Data Transformation: Linear Trend of Zoo Time Series
Data Transformation: Polynomial Linear Trend of Zoo Time Series
(C++) Contribution of Individual Variables to the Dissimilarity Betwee...
(C++) Contribution of Individual Variables to the Dissimilarity Betwee...
(C++) Contribution of Individual Variables to the Dissimilarity Betwee...
Aggregate momentum() Data Frames Across Parameter Combinations
Momentum Boxplot
Dynamic Time Warping Variable Importance Analysis of Multivariate Time...
Lock-Step Variable Importance Analysis of Multivariate Time Series Lis...
Dissimilarity Model Frame
Spatial Representation of momentum() Data Frames
Stats of Dissimilarity Data Frame
Momentum Data Frame to Wide Format
Contribution of Individual Variables to Time Series Dissimilarity
(C++) Unrestricted Permutation of Complete Rows
(C++) Unrestricted Permutation of Cases
(C++) Restricted Permutation of Complete Rows Within Blocks
(C++) Restricted Permutation of Cases Within Blocks
Cumulative Sum of Distances Between Consecutive Cases in a Time Series
Auto Sum
Cost Matrix
Sum of Distances in Least Cost Path
Least Cost Path
Lock-Step Distance
Distance Matrix
(C++) Psi Dissimilarity Score of Two Time-Series
(C++) Equation of the Psi Dissimilarity Score
Normalized Dissimilarity Score
(C++) Psi Dissimilarity Score of Two Aligned Time Series
(C++) Null Distribution of Dissimilarity Scores of Two Time Series
(C++) Null Distribution of the Dissimilarity Scores of Two Aligned Tim...
(C++) Subset Matrix by Rows
Aggregate Time Series List Over Time Periods
Multivariate TSL to Univariate TSL
Clean Column Names in Time Series Lists
Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) <doi:10.1111/ecog.04895>, provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data.