survivalmodels implements models for survival analysis
that are either not already implemented in R, or novel implementations
for speed improvements. Currently implemented are five neural networks
from the Python packages pycox, DNNSurv, and the
Akritas non-parametric conditional estimator. Further updates will
include implementations of novel survival models.
For a hands-on demonstration of model training, tuning, and
comparison see this
article I wrote, which uses the mlr3proba interface with
models from survivalmodels.
# load dependencies
library(survival)
train <- simsurvdata(100)
test <- simsurvdata(50)
fit <- akritas(Surv(time, status) ~ ., data = train)
predict(fit, newdata = test)
# Use distr6 = TRUE to return a distribution
predict_distr <- predict(fit, newdata = test, distr6 = TRUE)
predict_distr$survival(100)
# Return a relative risk ranking with type = "risk"
predict(fit, newdata = test, type = "risk")
Or both survival probabilities and a rank
predict(fit, newdata = test, type = "all", distr6 = TRUE)survivalmodels implements models from Python using reticulate. In
order to use these models, the required Python packages must be
installed following with reticulate::py_install.
survivalmodels includes a helper function to install the
required pycox function (with pytorch if also required).
Before running any models in this package, if you have not already
installed pycox please run
install_pycox(pip = TRUE, install_torch = FALSE)With the arguments changed as you require, see ?install_pycox for more.
For DNNSurv the model depends on keras and
tensorflow, which require installation via:
install_keras(pip = TRUE, install_tensorflow = FALSE)Install the latest release from CRAN:
install.packages("survivalmodels")Install the development version from GitHub:
remotes::install_github("RaphaelS1/survivalmodels")