Depth-Based Classification and Calculation of Data Depth
Fast Computation of the Uniform Metric for Sets of Functional Data
Classify using Functional Componentwise Classifier
Functional Componentwise Classifier
Using Custom Depth Functions and Classifiers
Functional Data Sets
Gene Expression Profile Data
Berkeley Growth Study Data
Relationship of Age Patterns of Fecundity to Mortality for Female Medf...
World Historical Population-by-Country Dataset
World Historical Population-by-Country Dataset (2010 Revision)
Converts data from fdata class to the functional class.
Model 1 from Cuevas et al. (2007)
Model 2 from Cuevas et al. (2007)
Functional Data Set Spectrometric Data (Tecator)
Transform a dataf Object to Raw Functional Data
Depth-Based Classification and Calculation of Data Depth
Classify using DD-Classifier
Test DD-Classifier
Test DD-Classifier
Test DD-Classifier
Train DD-Classifier
Classify using Functional DD-Classifier
Test Functional DD-Classifier
Test Functional DD-Classifier
Test Functional DD-Classifier
Functional DD-Classifier
Calculate Depth
Calculate Beta-Skeleton Depth
Depth Contours
Depth Contours
Depth Graph
Calculate Halfspace Depth
Calculate L2-Depth
Calculate Mahalanobis Depth
Calculate Potential of the Data
Calculate Projection Depth
Calculate Convex Hull Peeling Depth
Fast Depth Computation for Univariate and Bivariate Random Samples
Calculate Simplicial Depth
Calculate Simplicial Volume Depth
Calculate Depth Space using the Given Depth
Calculate Depth Space using Halfspace Depth
Calculate Depth Space using Mahalanobis Depth
Calculate Potential Space
Calculate Depth Space using Projection Depth
Calculate Depth Space using Simplicial Depth
Calculate Depth Space using Simplicial Volume Depth
Calculate Depth Space using Spatial Depth
Calculate Depth Space using Zonoid Depth
Calculate Spatial Depth
Calculate Zonoid Depth
Calculate Functional Depth
Adjusted Band Depth for Functional Data
Band Depth for Functional Data
Univariate Integrated and Infimal Depth for Functional Data
Bivariate Integrated and Infimal Depth for Functional Data
h-Mode Depth for Functional Data
Bivariate h-Mode Depth for Functional Data Based on the Metric
Half-Region Depth for Functional Data
Univariate Random Projection Depths for Functional Data
Bivariate Random Projection Depths for Functional Data
Calculate Simplicial Band Depth
Estimation of the First Two Derivatives for Functional Data
Depth-Based kNN
Depth-Based kNN
Depth-Based kNN
Draw DD-Plot
Fast Kernel Smoothing
Data for Classification
Adjusted Ranking of Functional Data Based on the Infimal Depth
Check Outsiderness
Fast Computation of the Metric for Sets of Functional Data
Plots for the "ddalpha" Class
Plots for the "ddalphaf" Class
Plot functions for the Functional Data
Transform Raw Functional Data to a dataf Object
Reset Graphical Parameters
Diagnostic Plot for First and Second Order Integrated and Infimal Dept...
Functional Depth-Based Shape Outlier Detection
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 <doi:10.18637/jss.v091.i05>).