permute_free_by_row_cpp

Unrestricted shuffling of rows within the whole sequence.

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

permute_free_by_row_cpp function

<dl><dt>x</dt>
<dd>(required, numeric matrix). Numeric matrix to permute.</dd>
<dt>block_size</dt>
<dd>(optional, integer) this function ignores this argument and sets it to x.nrow().</dd>
<dt>seed</dt>
<dd>(optional, integer) random seed to use.</dd></dl>

Arguments

(C++) Unrestricted Permutation of Complete Rows — permute_free_by_row_cpp

<dl>

<dt>x</dt>
<dd>(required, numeric matrix). Numeric matrix to permute.</dd>


<dt>block_size</dt>
<dd>(optional, integer) this function ignores this argument and sets it to x.nrow().</dd>


<dt>seed</dt>
<dd>(optional, integer) random seed to use.</dd>

</dl>

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permute_free_by_row_cpp: (C++) Unrestricted Permutation of Complete Rows

Description

Usage

Value

Arguments

See Also