Flexible parametric spline learner. Calls flexsurv::flexsurvspline() from flexsurv.

## Details

The distr prediction is estimated using the fitted custom distributions from flexsurv::flexsurvspline() and the estimated coefficients however the prediction takes place in this package and not in flexsurv for a much faster and more efficient implementation.

As flexible spline models estimate the baseline hazard as the intercept, the linear predictor, lp, can be calculated as in the classical setting. i.e. For fitted coefficients, $$\beta = (\beta_0,...,\beta_P)$$, and covariates $$X^T = (X_0,...,X_P)^T$$, where $$X_0$$ is a column of $$1$$s: $$lp = \beta X$$.

## Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

### Method clone()

The objects of this class are cloneable with this method.

LearnerSurvFlexible$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples learner = mlr3::lrn("surv.flexible") print(learner) #> <LearnerSurvFlexible:surv.flexible>: Flexible Parametric Splines #> * Model: - #> * Parameters: k=1 #> * Packages: mlr3, mlr3proba, mlr3extralearners, flexsurv, pracma #> * Predict Types: crank, [distr], lp #> * Feature Types: logical, integer, numeric, factor #> * Properties: weights # available parameters: learner$param_set\$ids()
#>  [1] "bhazard"       "k"             "knots"         "bknots"
#>  [5] "scale"         "timescale"     "inits"         "rtrunc"
#>  [9] "fixedpars"     "cl"            "maxiter"       "rel.tolerance"
#> [13] "toler.chol"    "debug"         "outer.max"