Software

Time Series Analysis:

• ml-crosstemporal-reconciliation: R code, developed by Marie Ternes, on cross-temporal forecast reconciliation through machine learning

• HDLP: R code, developed by Robert Adamek, for local projection inference in high-dimensions

• changepoint.influence: R code, developed by Rebecca Killick and Ines Wilms, to calculate the influence of the data on a changepoint segmentation

• hierarchical-MFVAR: R code, developed by Marie Ternes, on hierarchical regularizers for mixed-frequency vector autoregressions.

• desla: R package, developed by Robert Adamek, for inference in high-dimensional time series models via the desparsified lasso. 

• bootUR: R package, developed by Stephan Smeekes and Ines Wilms, for bootstrap unit root testing. userguide

• bigtime: R package, developed by Ines Wilms, David S. Matteson, Jacob Bien, Sumanta Basu, Will Nicholson and Enrico Wegner, for sparse estimation of large Vector AutoRegressive (VAR) Models, Vector AutoRegressive with Exogenous Variables X (VARX) Models and VectorAutoRegressive Moving Average (VARMA) Models: userguide

• Sparse estimation of the Vector AutoRegressive Model: Code, Example. 

• Sparse estimation of the Multi-class Vector AutoRegressive Model: Code.

• Sparse cointegration: Code.

• High-dimensional Granger Causality testing: Code, Example.

Graphical Modeling:

• taglasso: Tree-based node aggregation in sparse graphical models.

Canonical Correlation Analysis:

• Sparse Canonical Correlation Analysis: Code.

• Robust sparse Canonical Correlation Analysis: Code.

Discriminant Analysis:

• Cellwise robust regularized Discriminant Analysis: Code.