Robust Estimators for Multi-Group and Spatial Data
Biplot Visualization for msPCA Objects
Calculation of the cellwise robust multi-group Gaussian mixture model
Objective Function for Init Object
Calculation of Objective Function
Diagnostic Plots for Local Outlier Detection (locOuts)
Plot Method for msPCA Objects
Align Loadings of Principal Components
Perform Concentration Step
Computes Mahalanobis Distances for a Given Set of H-Subsets
Inverse Geographic Weight Matrix
Creates Grid-Based Neighborhood Structure
Local Outlier Detection using Spatially Smoothed MRCD
Compute Sparse Multi-Source Principal Components
Plot Method for ssMRCD Object
Regularized Covariance Matrix Calculation
Calculation of Residuals for the Multi-Group GMM
Residual Method from an ssMRCD Object
Locally Center and/or Scale or Data Using an ssMRCD Object
Calculate Scores and Distances for Multi-Source PCA
Scree Plot and Cumulative Explained Variance for msPCA Objects
Spatially Smoothed MRCD Estimator
Summary Method for msPCA Objects
Band weight matrix for time series groupings
Estimation of robust estimators for multi-group and spatial data including the casewise robust Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage for local outlier detection as described in Puchhammer and Filzmoser (2023) <doi:10.1080/10618600.2023.2277875> as well as for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2024) <doi:10.48550/arXiv.2407.16299>. Moreover, a cellwise robust multi-group Gaussian mixture model (MG-GMM) is implemented as described in Puchhammer, Wilms and Filzmoser (2024) <doi:10.48550/arXiv.2504.02547>. Included are also complementary visualization and parameter tuning tools.