From 60b3f54ecad9467de7f2dbacfab4436a68aff739 Mon Sep 17 00:00:00 2001 From: corolth1 Date: Wed, 24 Jul 2024 10:03:22 -0400 Subject: [PATCH] added links to TorchSurv intro --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 62954dd..3c6fd15 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -34,7 +34,7 @@ bibliography: paper.bib # Summary -`TorchSurv` (available on GitHub and PyPI) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. +`TorchSurv` ([GitHub](https://github.com/Novartis/torchsurv) and [PyPI](https://pypi.org/project/torchsurv/)) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. `TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the[`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). `TorchSurv` provides a user-friendly workflow for training and evaluating `PyTorch`-based deep survival models.