mlr_learners_dens.logspline.Rd
Calls logspline::logspline from package logspline.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("dens.logspline") lrn("dens.logspline")
Packages: logspline
Predict Types: pdf, cdf
Feature Types: integer, numeric
Properties:
Charles Kooperberg and Charles J. Stone. Logspline density estimation for censored data (1992). Journal of Computational and Graphical Statistics, 1, 301–328.
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerDens
-> LearnerDensLogspline
new()
Creates a new instance of this R6 class.
LearnerDensLogspline$new()
clone()
The objects of this class are cloneable with this method.
LearnerDensLogspline$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("dens.logspline")) print(learner)#> <LearnerDensLogspline:dens.logspline> #> * Model: - #> * Parameters: list() #> * Packages: logspline #> * Predict Type: pdf #> * Feature types: integer, numeric #> * Properties: -# available parameters: learner$param_set$ids()#> [1] "lbound" "ubound" "maxknots" "knots" "nknots" #> [6] "penalty" "silent" "mind" "error.action"