Monitoring overall survival in pivotal trials for indolent cancer

Deep survival analysis made easy

Create and edit docx files from R

Repository collecting risk assessment data on the packages installed on our Statistical Computing Environment at Novartis.

A Python library for benchmarking uncertainty estimation and quantification methods for Machine Learning models predictions.

The package analyzes chromatin and multi-omic experiments. It extends the MultiAssayExperiment object and builds a ChrawExperiment object from ENCODE’s output. It performs QC plotting, identifies differential events and other functionalities. More details in package vignettes.

Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models

25 Years of Small-Molecule Optimization at Novartis: A Retrospective Analysis of Chemical Series Evolution

An Object-Oriented approach to Shiny modules

An R-package for performing dynamic path analysis on survival data with estimation of the corresponding direct, indirect, and total effects.