Hi there ! I wanted to officially announce JuliaSurv/NetSurvival.jl, a new Julia package that gathers implementation of net survival related routines, joint work with @rimhajal.
Net survival is a specialized branch of Survival Analysis that focuses on estimating the probability of survival from a specific event of interest, for example a given cancer, without considering other causes of death, in the (unfortunately quite common) case where the cause of death indicatrix is unavailable (or e.g. untrustworthy). Consequently, the so-called missing indicatrix issue forbids the use of standard competitive risks survival analysis methods on these datasets. Thus, a few standard estimators were established in the last 50 years, backed by a constantly growing literature.
This package is an attempt to bring standard relative survival analysis modeling routines to Julia, while providing an interface that is close to the R package relsurv, albeit significantly faster and easier to maintain in the future. We aim at covering the standard estimators, needed for routines and comparisons, but also to provide the most up-to-date state of the art for the use of biostatisticians and oncologists around the world.
An extensive list of standard estimators in the field is implemented: Ederer I & II, Hakulinen, Pohar Perme, Grafféo’s test, Croning-Feuer’s crude probabilities, etc… But most importantly, thanks to Julia, the obtained codebase is compact, readable, well documented and well tested, and provides significant performance improvements compared to the R standard.
This is also the first output of a more general work at the JuliaSurv GitHub organization, that aims at covering survival analysis more generally. More will come, and contributions are of course welcomed.
Check out our documentation to learn about the field or simply learn about our implementation ! You may star the project on github to show support! 
We’ll also be at Juliacon2024 next week if you want to know more.