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

## Details

This learner returns two prediction types:

1. lp: a vector of linear predictors (relative risk scores), for each test observation. Calculated using flexsurv::flexsurvspline() and the estimated coefficients. 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, the linear predictor (lp) is $$lp = \beta X$$.

2. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using predict.flexsurvreg()

## 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"