0.4.4

Author: sebffischer

.gitignore

@@ -7,4 +7,6 @@ test/testthat/snaps
 docs/
 docs
 /doc/
+.DS_Store
 /Meta/
+CRAN-SUBMISSION

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mcboost
 Type: Package
 Title: Multi-Calibration Boosting
-Version: 0.4.3-9000
+Version: 0.4.4
 Authors@R:
     c(person(given = "Florian",
              family = "Pfisterer",
@@ -69,7 +69,7 @@ Suggests:
     covr,
     testthat (>= 3.1.0)
 Roxygen: list(markdown = TRUE, r6 = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 VignetteBuilder: knitr
 Collate:
     'AuditorFitters.R'

NEWS.md

@@ -1,5 +1,9 @@
 # dev
 
+# mcboost 0.4.4
+
+* Fix link anchors
+
 # mcboost 0.4.3
 
 * Compatibility with upcoming 'paradox' release.

R/MCBoost.R

@@ -219,7 +219,6 @@ MCBoost = R6::R6Class("MCBoost",
       labels = private$assert_labels(labels, ...)
       pred_probs = private$assert_prob(do.call(self$predictor, discard(list(data, predictor_args), is.null)), data, ...)
       buckets = private$create_buckets(pred_probs)
-      
       # Compute residuals and multiply with weighting for low-degree MC.
       resid = private$compute_residuals(pred_probs, labels)
       weighted_preds = private$compute_weighted_preds(pred_probs)
@@ -285,7 +284,7 @@ MCBoost = R6::R6Class("MCBoost",
     },
     #' @description
     #' Predict a dataset with multi-calibrated predictions
-    #' @param x [`data.table`] \cr
+    #' @param x [`data.table`][data.table::data.table] \cr
     #'   Prediction data.
     #' @param t [`integer`] \cr
     #'   Number of multi-calibration steps to predict. Default: `Inf` (all).
@@ -320,7 +319,7 @@ MCBoost = R6::R6Class("MCBoost",
     #' Compute the auditor effect for each instance which are the cumulative
     #' absolute predictions of the auditor. It indicates "how much"
     #' each observation was affected by multi-calibration on average across iterations.
-    #' @param x [`data.table`] \cr
+    #' @param x [`data.table`][data.table::data.table] \cr
     #'   Prediction data.
     #' @param aggregate [`logical`] \cr
     #'   Should the auditor effect be aggregated across iterations? Defaults to `TRUE`.

R/PipeOpLearnerPred.R

@@ -2,7 +2,7 @@
 #'
 #' @usage NULL
 #' @name mlr_pipeops_mcboost
-#' @format [`R6Class`] inheriting from [`mlr3pipelines::PipeOp`].
+#' @format [`R6Class`][R6::R6Class] inheriting from [`mlr3pipelines::PipeOp`].
 #'
 #' @description
 #'   [`mlr3pipelines::PipeOp`] that trains a [`Learner`][mlr3::Learner] and passes its predictions forward during training and prediction.
@@ -33,17 +33,17 @@
 #' [`mlr3pipelines::PipeOpTaskPreproc`]. It is a named `list` with the inherited members, as well as:
 #' * `model` :: `any`\cr
 #'   Model created by the [`Learner`][mlr3::Learner]'s `$.train()` function.
-#' * `train_log` :: [`data.table`] with columns `class` (`character`), `msg` (`character`)\cr
+#' * `train_log` :: [`data.table`][data.table::data.table] with columns `class` (`character`), `msg` (`character`)\cr
 #'   Errors logged during training.
 #' * `train_time` :: `numeric(1)`\cr
 #'   Training time, in seconds.
-#' * `predict_log` :: `NULL` | [`data.table`] with columns `class` (`character`), `msg` (`character`)\cr
+#' * `predict_log` :: `NULL` | [`data.table`][data.table::data.table] with columns `class` (`character`), `msg` (`character`)\cr
 #'   Errors logged during prediction.
 #' * `predict_time` :: `NULL` | `numeric(1)`
 #'   Prediction time, in seconds.
 #'
 #' @section Fields:
-#' Fields inherited from [`PipeOp`], as well as:
+#' Fields inherited from [`PipeOp`][mlr3pipelines::PipeOp], as well as:
 #' * `learner` :: [`Learner`][mlr3::Learner]\cr
 #'   [`Learner`][mlr3::Learner] that is being wrapped. Read-only.
 #' * `learner_model` :: [`Learner`][mlr3::Learner]\cr
@@ -61,7 +61,7 @@ PipeOpLearnerPred = R6Class("PipeOpLearnerPred",
     #' @description
     #'   Initialize a Learner Predictor PipeOp. Can be used to wrap trained or untrainted 
     #'   mlr3 learners.
-    #' @param learner [`Learner`]\cr
+    #' @param learner [`Learner`][mlr3::Learner]\cr
     #'   The learner that should be wrapped.  
     #' @param id [`character`] \cr
     #'   The `PipeOp`'s id. Defaults to "mcboost".

R/PipeOpMCBoost.R

@@ -2,11 +2,11 @@
 #'
 #' @usage NULL
 #' @name mlr_pipeops_mcboost
-#' @format [`R6Class`] inheriting from [`mlr3pipelines::PipeOp`].
+#' @format [`R6Class`][R6::R6Class] inheriting from [`mlr3pipelines::PipeOp`].
 #'
 #' @description
 #' Post-process a learner prediction using multi-calibration.
-#' For more details, please refer to \url{https://arxiv.org/pdf/1805.12317.pdf} (Kim et al. 2018)
+#' For more details, please refer to \url{http://arxiv.org/pdf/1805.12317} (Kim et al. 2018)
 #' or the help for [`MCBoost`].
 #' If no `init_predictor` is provided, the preceding learner's predictions
 #' corresponding to the `prediction` slot are used as an initial predictor for `MCBoost`.

R/PipelineMCBoost.R

@@ -11,7 +11,7 @@
 #'   Note: An initial predictor can also be supplied via the `init_predictor` parameter.
 #' @param param_vals `list` \cr
 #'   List of parameter values passed on to `MCBoost$new`.
-#' @return (mlr3pipelines) [`Graph`]
+#' @return (mlr3pipelines) [`Graph`][mlr3pipelines::Graph]
 #' @examples
 #'   \dontrun{
 #'   library("mlr3pipelines")

R/Predictor.R

@@ -16,8 +16,8 @@ Predictor = R6::R6Class("Predictor",
     },
     #' @description
     #' Predict a dataset with constant predictions.
-    #' @param data [`data.table`] \cr
-    #'   Prediction data.
+    #' @param data [`data.table`][data.table::data.table] \cr
+    #r   Prediction data.
     #' @param ... [`any`] \cr
     #'   Not used, only for compatibility with other methods.
     predict = function(data, ...) {
@@ -56,7 +56,7 @@ ConstantPredictor = R6::R6Class("ConstantPredictor",
     },
     #' @description
     #' Predict a dataset with constant predictions.
-    #' @param data [`data.table`] \cr
+    #' @param data [`data.table`][data.table::data.table] \cr
     #'   Prediction data.
     #' @param ... [`any`] \cr
     #'   Not used, only for compatibility with other methods.
@@ -95,7 +95,7 @@ LearnerPredictor = R6::R6Class("LearnerPredictor",
     },
     #' @description
     #' Predict a dataset with leaner predictions.
-    #' @param data [`data.table`] \cr
+    #' @param data [`data.table`][data.table::data.table] \cr
     #'   Prediction data.
     #' @param ... [`any`] \cr
     #'   Not used, only for compatibility with other methods.
@@ -169,7 +169,7 @@ SubpopPredictor = R6::R6Class("SubpopPredictor",
     },
     #' @description
     #' Predict a dataset with sub-population predictions.
-    #' @param data [`data.table`] \cr
+    #' @param data [`data.table`][data.table::data.table] \cr
     #'   Prediction data.
     #' @param ... [`any`] \cr
     #'   Not used, only for compatibility with other methods.
@@ -211,7 +211,7 @@ SubgroupModel = R6::R6Class("SubgroupModel",
     },
     #' @description
     #' Predict a dataset with sub-population predictions.
-    #' @param data [`data.table`] \cr
+    #' @param data [`data.table`][data.table::data.table] \cr
     #'   Prediction data.
     #' @param subgroup_masks [`list`] \cr
     #'   List of subgroup masks for the data.
@@ -274,7 +274,7 @@ CVLearnerPredictor = R6::R6Class("CVLearnerPredictor",
     },
     #' @description
     #' Predict a dataset with leaner predictions.
-    #' @param data [`data.table`] \cr
+    #' @param data [`data.table`][data.table::data.table] \cr
     #'   Prediction data.
     #' @param ... [`any`] \cr
     #'   Not used, only for compatibility with other methods.

README.md

@@ -11,7 +11,7 @@
 
 ## What does it do?
 
-**mcboost** implements Multi-Calibration Boosting ([Hebert-Johnson et al., 2018](https://proceedings.mlr.press/v80/hebert-johnson18a.html); [Kim et al., 2019](https://arxiv.org/pdf/1805.12317.pdf)) for the multi-calibration of a machine learning model's prediction. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased but a bias is introduced within the algorithm's fitting procedure. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
+**mcboost** implements Multi-Calibration Boosting ([Hebert-Johnson et al., 2018](https://proceedings.mlr.press/v80/hebert-johnson18a.html); [Kim et al., 2019](https://arxiv.org/pdf/1805.12317)) for the multi-calibration of a machine learning model's prediction. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased but a bias is introduced within the algorithm's fitting procedure. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
 
 For more information and example, see the package's [website](https://mlr-org.github.io/mcboost/).
 
@@ -99,7 +99,7 @@ mc$predict_probs(test_data)
 
 ### Multi-Calibration
 
-While `mcboost` in its defaults implements Multi-Accuracy ([Kim et al., 2019](https://arxiv.org/pdf/1805.12317.pdf)),
+While `mcboost` in its defaults implements Multi-Accuracy ([Kim et al., 2019](http://arxiv.org/pdf/1805.12317)),
 it can also multi-calibrate predictors ([Hebert-Johnson et al., 2018](http://proceedings.mlr.press/v80/hebert-johnson18a.html)).
 In order to achieve this, we have to set the following hyperparameters:
 
@@ -152,7 +152,7 @@ If you use `mcboost`, please cite our package as well as the two papers it is ba
 
 ```
   @article{pfisterer2021,
-    author = {Pfisterer, Florian and Kern, Christoph and Dandl, Susanne and Sun, Matthew and 
+    author = {Pfisterer, Florian and Kern, Christoph and Dandl, Susanne and Sun, Matthew and
     Kim, Michael P. and Bischl, Bernd},
     title = {mcboost: Multi-Calibration Boosting for R},
     journal = {Journal of Open Source Software},

attic/PipeOpMCBoostSurv.R

@@ -2,11 +2,11 @@
 #'
 #' @usage NULL
 #' @name mlr_pipeops_mcboostsurv
-#' @format [`R6Class`] inheriting from [`mlr3pipelines::PipeOp`].
+#' @format [`R6Class`][R6::R6Class] inheriting from [`PipeOp`][mlr3pipelines::PipeOp].
 #'
 #' @description
 #' Post-process a survival learner prediction using multi-calibration.
-#' For more details, please refer to \url{https://arxiv.org/pdf/1805.12317.pdf} (Kim et al. 2018)
+#' For more details, please refer to \url{https://arxiv.org/pdf/1805.12317} (Kim et al. 2018)
 #' or the help for [`MCBoostSurv`].
 #' If no `init_predictor` is provided, the preceding learner's predictions
 #' corresponding to the `prediction` slot are used as an initial predictor for `MCBoostSurv`.
@@ -87,7 +87,7 @@ PipeOpMCBoostSurv = R6Class("PipeOpMCBoostSurv",
       super$initialize(id,
         param_set = param_set, param_vals = param_vals, packages = c("mlr3proba", "survival"),
         input = data.table(
-          name = c("data", "prediction"), 
+          name = c("data", "prediction"),
           train = c("TaskSurv", "TaskSurv"),
           predict = c("TaskSurv", "TaskSurv")
         ),
@@ -162,7 +162,7 @@ PipeOpMCBoostSurv = R6Class("PipeOpMCBoostSurv",
 #'   All parameters can be adjusted through the resulting Graph's `param_set`.
 #' @param param_vals `list` \cr
 #'   List of parameter values passed on to `MCBoostSurv$new`
-#' @return (mlr3pipelines) [`Graph`]
+#' @return (mlr3pipelines) [`Graph`][mlr3pipelines::Graph]
 #' @examples
 #' library("mlr3pipelines")
 #' gr = ppl_mcboostsurv()