statistics.h File Reference

Header file for statistical functions and classes. More...

#include <concepts>
#include <numeric>
#include <vector>
#include <iterator>
#include <algorithm>
#include <cmath>
#include <Eigen/Dense>
#include <cmp_defines.h>
#include <unsupported/Eigen/FFT>
#include <complex>

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Classes
class	cmp::statistics::KFold
	K-Fold cross-validation partition generator. More...
class	cmp::statistics::KFold::KFoldIterator
	Iterator class to traverse folds for K-Fold cross-validation. More...
class	cmp::statistics::Bootstrap
	Bootstrap resampling index generator. More...
class	cmp::statistics::PairwiseDistanceStats
	Computes and stores pairwise spatial distances between high-dimensional data points. More...

Namespaces
namespace	cmp
namespace	cmp::statistics

Functions
Eigen::VectorXd	cmp::statistics::mean (const Eigen::Ref< const Eigen::MatrixXd > &data)
	Computes the empirical mean vector of a dataset.
Eigen::MatrixXd	cmp::statistics::covariance (const Eigen::Ref< const Eigen::MatrixXd > &data)
	Computes the sample covariance matrix of a dataset.
double	cmp::statistics::quantile (const Eigen::Ref< const Eigen::VectorXd > &data, double quantile)
	Computes a specific quantile of a 1D vector of data.
Eigen::VectorXd	cmp::statistics::interQuantileRange (const Eigen::Ref< const Eigen::MatrixXd > &data, double lowerQuantile, double upperQuantile)
	Computes the interquartile range (IQR) column-wise for a dataset.
Eigen::MatrixXd	cmp::statistics::pearsonCorrelation (const Eigen::Ref< const Eigen::MatrixXd > &data1, const Eigen::Ref< const Eigen::MatrixXd > &data2)
	Computes the Pearson correlation matrix between two datasets.
Eigen::MatrixXd	cmp::statistics::laggedCorrelation (const Eigen::Ref< const Eigen::MatrixXd > &data1, const Eigen::Ref< const Eigen::MatrixXd > &data2, int lag)
	Computes the cross-correlation matrix between two datasets with a specified temporal lag.
std::vector< Eigen::MatrixXd >	cmp::statistics::laggedCorrelation (const Eigen::Ref< const Eigen::MatrixXd > &data1, const Eigen::Ref< const Eigen::MatrixXd > &data2, int minLag, int maxLag)
	Returns the cross-correlation matrices between two datasets for a sequence of lags.
std::pair< Eigen::VectorXd, double >	cmp::statistics::selfCorrelationLength (const Eigen::Ref< const Eigen::MatrixXd > &data)
	Computes the self-correlation (autocorrelation) length and the effective sample size.
std::vector< Eigen::MatrixXd >	cmp::statistics::selfCrossCorrelationFFT (const Eigen::Ref< const Eigen::MatrixXd > &data)
	Computes the complete auto- and cross-correlation functions across all lags efficiently using the Fast Fourier Transform (FFT).

Detailed Description

Header file for statistical functions and classes.

This file contains declarations for various statistical functions, resampling techniques (K-Fold, Bootstrap), and spatial statistics utilities used throughout the project.