`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")
```

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