DCC Models with GARCH and GARCH-MIDAS Specifications in the
Univariate Step, RiskMetrics, Moving Covariance and Scalar and
Diagonal BEKK Models
R package dccmidas version 0.1.2

Fast Computation of Distance Correlations

Clustering Big Data using Expectation Maximization Star (EM*)
Algorithm
R package DCEM version 2.0.5

Efficient and Accessible Discrete Choice Experiments

Data Cloud Geometry (DCG): Using Random Walks to Find Community
Structure in Social Network Analysis
R package DCG version 0.9.3

Genetic Relatedness Between Polyclonal Infections

Claims Reserving under the Double Chain Ladder Model

Distance Based Cell Lineage Reconstruction

Data Cloning and MCMC Tools for Maximum Likelihood Methods

Functions for the Detection of Spatial Clusters of Diseases

Model-Based Detection of Disease Clusters

Calculating the M2 Model Fit Statistic for Diagnostic
Classification Models
R package dcm2 version 1.0.2

Hierarchical Models Made Easy with Data Cloning

Modify Data Using Externally Defined Modification Rules

List Linear n-Peptide Constraints for Overlapping Protein
Regions
R package DCODE version 1.0

Providing Fast and Flexible Functions for Distance Correlation
Analysis

Variable Selection Algorithms Using the Distance Correlation

A Fast Implementation of Distance Covariance

Distance Covariance and Correlation for Time Series Analysis

Nonparametric Regression and Bandwidth Selection for Spatial
Models

Decision Curve Analysis for Model Evaluation

Depth-Based Classification and Calculation of Data Depth

Variance Stabilization by Data-Driven Haar-Fisz (for
Microarrays)

The Data Defect Index for Samples that May not be IID

Distance & Density-Based Outlier Detection

Diagonally Dominant Principal Component Analysis

Analysis and Visualization of Droplet Digital PCR in R and on
the Web

Data Sets for Discrete Probability Models

Disease-Drived Differential Proteins Co-Expression Network
Analysis

Data-Driven Sparse Partial Least Squares

Latent Class Analysis with Dirichlet Diffusion Tree Process
Prior

Learning Bayesian Networks with Mixed Variables