An Analysis Toolbox for Hermitian Positive Definite Matrices
Riemannian HPD exponential map
Orthonormal basis expansion of a Hermitian matrix
Inverse AI wavelet transform for curve of HPD matrices
Inverse AI wavelet transform for surface of HPD matrices
Riemannian HPD logarithmic map
Geodesic midpoint between HPD matrices
Tree-structured trace thresholding of wavelet coefficients
Data depth for HPD matrices
Compute distance between two HPD matrices
K-means clustering for HPD matrices
Weighted Karcher mean of HPD matrices
Weighted intrinsic median of HPD matrices
Polynomial interpolation of curves (1D) or surfaces (2D) of HPD matric...
Riemannian HPD parallel transport
Multitaper HPD periodogram matrix
Multitaper HPD time-varying periodogram matrix
Generate intrinsic HPD polynomial curves
Rank-based hypothesis tests for HPD matrices
Intrinsic wavelet HPD spectral matrix clustering
Intrinsic wavelet HPD time-varying spectral clustering
pdSpecEst: An Analysis Toolbox for Hermitian Positive Definite Matrice...
Intrinsic wavelet HPD spectral estimation
Intrinsic wavelet HPD time-varying spectral estimation
Cubic smoothing spline regression for HPD matrices
Simulate vARMA(2,2) time series
Several example curves of HPD matrices
Several example surfaces of HPD matrices
Forward AI wavelet transform for curve of HPD matrices
Forward AI wavelet transform for surface of HPD matrices
An implementation of data analysis tools for samples of symmetric or Hermitian positive definite matrices, such as collections of covariance matrices or spectral density matrices. The tools in this package can be used to perform: (i) intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive definite matrices with applications to dimension reduction, denoising and clustering in the space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference for samples of positive definite matrices by means of intrinsic data depth functions and rank-based hypothesis tests in the space of Hermitian positive definite matrices.