utils_check_args_path

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

utils_check_args_path function

<dl><dt>path</dt>
<dd>(required, data frame) least cost path generated with <code>psi_cost_path()</code>. This data frame must have the attribute <code>type == "cost_path</code>, and must have been computed from the same time series used to compute the matrix <code>m</code>. Default: NULL.</dd>
<dt>arg_name</dt>
<dd>(optional, character string) name of the argument being checked. Default: NULL</dd></dl>

Arguments

Checks Least Cost Path — utils_check_args_path

<dl>

<dt>path</dt>
<dd>(required, data frame) least cost path generated with <code>psi_cost_path()</code>. This data frame must have the attribute <code>type == "cost_path</code>, and must have been computed from the same time series used to compute the matrix <code>m</code>. Default: NULL.</dd>


<dt>arg_name</dt>
<dd>(optional, character string) name of the argument being checked. Default: NULL</dd>

</dl>

utils_check_args_path: Checks Least Cost Path

Description

Usage

Value

Arguments

See Also