Detailed output of DataSHIELD continuous integration tests

Set of functions to automate and simplify DataSHIELD processes.

ROC-GLM and calibration analysis for DataSHIELD

Ansible Playbook for Installing/Updating DataSHIELD servers

Upload tools for DataSHIELD backends

We propose dsDid, a federated learning package implemented in DataSHIELD with a federated version of the DID approach of Callaway and Sant'Anna (2022) at its core. It allows for the federated estimation of treatment effects per period and the corresponding federated uncertainty quantification.

This is a package with miscellaneous tools, a set of computational tools and a graphical user interface (GUI) for privacy preserving federated survival model meta-analysis using the DataSHIELD platform. The primary function is to enable data and model documentation.

ROC-GLM for DataSHIELD

Survival functions (client side) for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.

Survival functions for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.

Helper functions for accessing and working with the IMI SOPHIA federated database.

Miscellaneous tools for use in DataSHIELD

Distributed component-wise boosting using DataSHIELD

Shiny app for DataSHIELD omic data analyses

Synthetic data generation for DataSHIELD

dsSynthetic library to generate synthetic data in DataSHIELD

A bookdown demonstrating how to build survival models using the dsSurvival package in DataSHIELD

A bookdown demonstrating how to build survival models using the dsSurvival package in DataSHIELD

A bookdown demonstrating how to build survival models using the dsSurvival 2.0 package in DataSHIELD

Documentation for dsSynthetic package to generate synthetic data