mlr3proba

mlr3proba provides probabilistic survival analysis methods for time-to-event prediction and model evaluation within the mlr3 ecosystem.

Key Features:

• Probabilistic Supervised Learning: Facilitates probabilistic predictions for time-to-event data.
• Integration with mlr3 Ecosystem: Integrates with mlr3 infrastructure to enable model tuning and benchmarking for systematic survival modeling and evaluation.
• Core and Extended Learners: Includes core learners for survival analysis and supports additional models via mlr3learners and mlr3extralearners.

Scientific Applications:

• Medicine: Modeling patient survival times and evaluating treatment efficacy.
• Bioinformatics: Studying time-dependent biological processes.
• Economics: Predicting product or system lifespan and failure times.
• Engineering: Predicting product or system lifespan and failure times.

Methodology:

Integrates with mlr3 to leverage model tuning and benchmarking tools for evaluation and optimization of survival models.