Statistical inference in sparse high-dimensional additive models

Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series

Random Forest Two Sample Testing

R Package: Adaptively weighted group lasso for semiparametic quantile regression models

R package for Non-local Prior Based Iterative Variable Selection for Genome-Wide Association Studies, or Other High-Dimensional Data

An R package for regression analysis of data from extreme sampling

R codes and dataset for the estimation of the high-dimensional state space model proposed in the paper "A dynamic factor model approach to incorporate Big Data in state space models for official statistics" with Franz Palm, Stephan Smeekes and Jan van den Brakel.

Biomarker selection in penalized regression models

Replicate the results of nowcasting housing sales by Google Queries, using Bayesian Structural Time-Series Model (Choi & Varian, 2009, 2012).

Implementation MWPCR with R