Merge branch 'master' into dev

Author: Raphael Sonabend

.editorconfig

@@ -0,0 +1,21 @@
+# See http://editorconfig.org
+root = true
+
+[*]
+charset = utf8
+end_of_line = lf
+insert_final_newline = true
+indent_style = space
+trim_trailing_whitespace = true
+
+[*.{r,R,md,Rmd}]
+indent_size = 2
+
+[*.{c,h}]
+indent_size = 4
+
+[*.{cpp,hpp}]
+indent_size = 4
+
+[{NEWS.md,DESCRIPTION,LICENSE}]
+max_line_length = 80

DESCRIPTION

@@ -44,6 +44,7 @@ Imports:
     R6,
     survival
 Suggests:
+    bibtex,
     flexsurv,
     gbm,
     glmnet,
@@ -68,6 +69,7 @@ LazyData: true
 NeedsCompilation: no
 Roxygen: list(markdown = TRUE, r6 = FALSE)
 RoxygenNote: 7.0.2
+RdMacros: mlr3misc
 Collate:
     'LearnerDensity.R'
     'LearnerDensityHist.R'

NEWS.md

@@ -4,9 +4,8 @@
 * Added notes to IGS documentation regarding default methods and comparison to other packages
 * Added `method` to `MeasureSurvIntegrated` constructor and fields
 * Fixed mistake in documentation of: `TaskSurv`, `MeasureSurvUnoC`
-* Fixed errors in r-patched-solaris
 * Added missing `LearnerSurvRpart` parameter `parms` and `cost`
-
+* Fixed errors in r-patched-solaris and r-devel debian-clang
 
 # mlr3proba 0.1.0
 

R/LearnerSurvBlackboost.R

@@ -8,22 +8,15 @@
 #' @template learner_boost
 #'
 #' @references
-#' Peter Buehlmann and Torsten Hothorn (2007),
-#' Boosting algorithms: regularization, prediction and model fitting.
-#' Statistical Science, 22(4), 477–505.
+#' \cite{mlr3proba}{buehlmann_2007}
 #'
-#' Torsten Hothorn, Kurt Hornik and Achim Zeileis (2006).
-#' Unbiased recursive partitioning: A conditional inference framework.
-#' Journal of Computational and Graphical Statistics, 15(3), 651–674.
+#' \cite{mlr3proba}{hothorn_2006}
 #'
-#' Yoav Freund and Robert E. Schapire (1996), Experiments with a new boosting algorithm.
-#' In Machine Learning: Proc. Thirteenth International Conference, 148–156.
+#' \cite{mlr3proba}{freund_1996}
+#' 
+#' \cite{mlr3proba}{friedman_2001}
 #'
-#' Jerome H. Friedman (2001), Greedy function approximation: A gradient boosting machine.
-#' The Annals of Statistics, 29, 1189–1232.
-#'
-#' Greg Ridgeway (1999), The state of boosting.
-#' Computing Science and Statistics, 31, 172–181.
+#' \cite{mlr3proba}{ridgeway_1999}
 #'
 #' @export
 #' @examples

R/LearnerSurvCVGlmnet.R

@@ -11,10 +11,7 @@
 #' \CRANpkg{mlr3tuning} and [LearnerSurvGlmnet] will likely give better results.
 #'
 #' @references
-#' Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010).
-#' Regularization Paths for Generalized Linear Models via Coordinate Descent.
-#' Journal of Statistical Software, 33(1), 1-22.
-#' \doi{10.18637/jss.v033.i01}.
+#' \cite{mlr3proba}{friedman_2010}
 #'
 #' @export
 LearnerSurvCVGlmnet = R6Class("LearnerSurvCVGlmnet", inherit = LearnerSurv,

R/LearnerSurvCoxPH.R

@@ -6,10 +6,7 @@
 #' @templateVar lp by [survival::predict.coxph()]
 #'
 #' @references
-#' Cox, David R. (1972).
-#' Regression models and life-tables.
-#' Journal of the Royal Statistical Society: Series B (Methodological) 34.2 (1972): 187-202.
-#' \doi{10.1111/j.2517-6161.1972.tb00899.x}.
+#' \cite{mlr3proba}{cox_1972}
 #'
 #' @export
 LearnerSurvCoxPH = R6Class("LearnerSurvCoxPH", inherit = LearnerSurv,

R/LearnerSurvFlexible.R

@@ -25,11 +25,7 @@
 #' of \eqn{1}s: \eqn{lp = \beta X}{lp = \betaX}.
 #'
 #' @references
-#' Royston, P. and Parmar, M. (2002).
-#' Flexible parametric proportional-hazards and proportional-odds models for censored survival data,
-#' with application to prognostic modelling and estimation of treatment effects.
-#' Statistics in Medicine, 21(15), 2175-2197.
-#' \doi{10.1002/sim.1203}.
+#' \cite{mlr3proba}{royston_2002}
 #'
 #' @template seealso_learner
 #' @export

R/LearnerSurvGBM.R

@@ -9,29 +9,21 @@
 #' in [gbm::gbm()] for survival analysis; parameter `keep.data` is set to `FALSE` for efficiency.
 #'
 #' @references
-#' Y. Freund and R.E. Schapire (1997)
-#' A decision-theoretic generalization of on-line learning and an application to boosting.
-#' Journal of Computer and System Sciences, 55(1):119-139.
+#' \cite{mlr3proba}{freund_1997}
 #'
-#' G. Ridgeway (1999). The state of boosting. Computing Science and Statistics 31:172-181.
+#' \cite{mlr3proba}{ridgeway_1999}
 #'
-#' J.H. Friedman, T. Hastie, R. Tibshirani (2000).
-#' Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics 28(2):337-374.
+#' \cite{mlr3proba}{friedman_2000}
 #'
-#' J.H. Friedman (2001).
-#' Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics 29(5):1189-1232.
+#' \cite{mlr3proba}{friedman_2001}
 #'
-#' J.H. Friedman (2002).
-#' Stochastic Gradient Boosting. Computational Statistics and Data Analysis 38(4):367-378.
+#' \cite{mlr3proba}{friedman_2001}
 #'
-#' B. Kriegler (2007).
-#' Cost-Sensitive Stochastic Gradient Boosting Within a Quantitative Regression Framework.
-#' Ph.D. Dissertation. University of California at Los Angeles, Los Angeles, CA, USA. Advisor(s) Richard A. Berk.
-#' \url{https://dl.acm.org/citation.cfm?id=1354603}
+#' \cite{mlr3proba}{friedman_2002}
 #'
-#' C. Burges (2010).
-#' From RankNet to LambdaRank to LambdaMART: An Overview.
-#' Microsoft Research Technical Report MSR-TR-2010-82.
+#' \cite{mlr3proba}{kriegler_2007}
+#'
+#' \cite{mlr3proba}{burges_2010}
 #'
 #' @export
 LearnerSurvGBM = R6Class("LearnerSurvGBM", inherit = LearnerSurv,

R/LearnerSurvGamboost.R

@@ -14,28 +14,17 @@
 #' specific definition of the corresponding base-learner.
 #'
 #' @references
-#' Peter Buehlmann and Bin Yu (2003), Boosting with the L2 loss: regression and classification.
-#' Journal of the American Statistical Association, 98, 324–339.
+#' \cite{mlr3proba}{buehlmann_2003}
 #'
-#' Peter Buehlmann and Torsten Hothorn (2007),
-#' Boosting algorithms: regularization, prediction and model fitting.
-#' Statistical Science, 22(4), 477–505.
+#' \cite{mlr3proba}{buehlmann_2007}
 #'
-#' Thomas Kneib, Torsten Hothorn and Gerhard Tutz (2009),
-#' Variable selection and model choice in geoadditive regression models,
-#' Biometrics, 65(2), 626–634.
+#' \cite{mlr3proba}{kneib_2008}
 #'
-#' Matthias Schmid and Torsten Hothorn (2008),
-#' Boosting additive models using component-wise P-splines as base-learners.
-#' Computational Statistics \& Data Analysis, 53(2), 298–311.
+#' \cite{mlr3proba}{schmid_2008}
 #'
-#' Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Mattthias Schmid and Benjamin Hofner (2010),
-#' Model-based Boosting 2.0. Journal of Machine Learning Research, 11, 2109 – 2113.
+#' \cite{mlr3proba}{hothorn_2010}
 #'
-#' Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid (2014).
-#' Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost.
-#' Computational Statistics, 29, 3–35.
-#' \doi{10.1007/s00180-012-0382-5}
+#' \cite{mlr3proba}{hofner_2012}
 #'
 #' @export
 #' @examples

R/LearnerSurvGlmboost.R

@@ -8,20 +8,15 @@
 #' @template learner_boost
 #'
 #' @references
-#' Peter Buehlmann and Bin Yu (2003), Boosting with the L2 loss: regression and classification.
-#' Journal of the American Statistical Association, 98, 324–339.
+#' \cite{mlr3proba}{buehlmann_2003}
 #'
-#' Peter Buehlmann (2006), Boosting for high-dimensional linear models. The Annals of
-#' Statistics, 34(2), 559–583.
+#' \cite{mlr3proba}{buehlmann_2006}
 #'
-#' Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization,
-#' prediction and model fitting. Statistical Science, 22(4), 477–505.
+#' \cite{mlr3proba}{buehlmann_2007}
 #'
-#' Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Mattthias Schmid and Benjamin Hofner
-#' (2010), Model-based Boosting 2.0. Journal of Machine Learning Research, 11, 2109–2113.
+#' \cite{mlr3proba}{hothorn_2010}
 #'
-#' Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost. Computational Statistics, 29, 3–35.
-#' \doi{10.1007/s00180-012-0382-5}.
+#' \cite{mlr3proba}{hofner_2012}
 #'
 #' @export
 #' @examples