cost_matrix_diagonal_cpp

Computes the least cost matrix from a distance matrix.
Considers diagonals during computation of least-costs.

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.

Blas M. Benito

distantia

Advanced Toolset for Efficient Time Series Dissimilarity
Analysis

cost_matrix_diagonal_cpp function

<dl><dt>dist_matrix</dt>
<dd>(required, distance matrix). Square distance matrix, output of <code>distance_matrix_cpp()</code>.</dd></dl>

Arguments

(C++) Compute Orthogonal and Diagonal Least Cost Matrix from a Distance Matrix — cost_matrix_diagonal_cpp

<dl>

<dt>dist_matrix</dt>
<dd>(required, distance matrix). Square distance matrix, output of <code>distance_matrix_cpp()</code>.</dd>

</dl>

cost_matrix_diagonal_cpp: (C++) Compute Orthogonal and Diagonal Least Cost Matrix from a Distance Matrix

Description

Usage

Value

Arguments

See Also