From 996bf2db7ed9e9825b4e1ddd3d835231182950e7 Mon Sep 17 00:00:00 2001
From: rachaelvp <rachaelvphillips@gmail.com>
Date: Fri, 30 Jun 2023 09:18:50 -0700
Subject: [PATCH 1/2] add weights to args depending on hal9001 version

---
 R/Lrnr_hal9001.R | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/R/Lrnr_hal9001.R b/R/Lrnr_hal9001.R
index e9e5120e..34403283 100644
--- a/R/Lrnr_hal9001.R
+++ b/R/Lrnr_hal9001.R
@@ -92,7 +92,6 @@ Lrnr_hal9001 <- R6Class(
         verbose <- getOption("sl3.verbose")
       }
 
-
       args$X <- as.matrix(task$X)
 
       outcome_type <- self$get_outcome_type(task)
@@ -125,7 +124,11 @@ Lrnr_hal9001 <- R6Class(
       }
 
       if (task$has_node("weights")) {
-        args$weights <- task$weights
+        if (packageVersion("hal9001") >= "0.4.5") {
+          args$weights <- task$weights
+        } else {
+          args$fit_control$weights <- task$weights
+        }
       }
 
       if (task$has_node("offset")) {

From 9fae3f27d39fe1a5f08d4ca9ece415175f905314 Mon Sep 17 00:00:00 2001
From: rachaelvp <rachaelvphillips@gmail.com>
Date: Fri, 30 Jun 2023 09:19:00 -0700
Subject: [PATCH 2/2] make pr changes

---
 R/Lrnr_cv.R                                  | 10 ++---
 R/Lrnr_dbarts.R                              | 10 ++---
 R/Lrnr_density_discretize.R                  | 15 ++++---
 R/Lrnr_density_hse.R                         | 13 +++---
 R/Lrnr_density_semiparametric.R              | 13 +++---
 R/Lrnr_grf.R                                 | 15 ++++---
 R/Lrnr_gts.R                                 |  8 ++--
 R/Lrnr_h2o_glm.R                             | 15 ++++---
 R/Lrnr_h2o_grid.R                            | 16 ++++---
 R/Lrnr_independent_binomial.R                | 15 ++++---
 R/Lrnr_multiple_ts.R                         | 38 ++++++++--------
 R/Lrnr_multivariate.R                        | 15 ++++---
 R/Lrnr_nnet.R                                | 10 ++---
 R/Lrnr_pca.R                                 | 10 ++---
 R/Lrnr_pooled_hazards.R                      | 10 ++---
 R/Lrnr_screener_augment.R                    | 14 +++---
 R/Lrnr_screener_coefs.R                      | 16 +++----
 R/Lrnr_screener_correlation.R                | 16 +++----
 R/Lrnr_stratified.R                          | 12 ++---
 R/Lrnr_subset_covariates.R                   | 14 +++---
 man/Lrnr_cv.Rd                               | 19 ++++++++
 man/Lrnr_dbarts.Rd                           | 15 +++++++
 man/Lrnr_density_discretize.Rd               | 17 +++++++
 man/Lrnr_density_hse.Rd                      | 15 +++++++
 man/Lrnr_density_semiparametric.Rd           | 17 +++++++
 man/Lrnr_grf.Rd                              | 15 +++++++
 man/Lrnr_gts.Rd                              | 25 +++++++++++
 man/Lrnr_h2o_glm.Rd                          | 18 ++++++++
 man/Lrnr_h2o_grid.Rd                         | 23 ++++++++++
 man/Lrnr_independent_binomial.Rd             | 20 +++++++++
 man/Lrnr_multiple_ts.Rd                      | 47 ++++++++++++++++++++
 man/Lrnr_multivariate.Rd                     | 27 +++++++++++
 man/Lrnr_nnet.Rd                             | 16 +++++++
 man/Lrnr_pca.Rd                              | 24 ++++++++++
 man/Lrnr_pooled_hazards.Rd                   | 29 ++++++++++++
 man/Lrnr_screener_augment.Rd                 | 29 ++++++++++++
 man/Lrnr_screener_coefs.Rd                   | 29 ++++++++++++
 man/Lrnr_screener_correlation.Rd             | 29 ++++++++++++
 man/Lrnr_stratified.Rd                       | 22 +++++++++
 man/Lrnr_subset_covariates.Rd                | 22 +++++++++
 tests/testthat/test-add_many_columns.R       |  4 +-
 tests/testthat/test-bartMachine.R            |  8 ++--
 tests/testthat/test-binomial_learners.R      |  2 +-
 tests/testthat/test-caret.R                  |  6 +--
 tests/testthat/test-character_covariates.R   |  4 +-
 tests/testthat/test-cv_sl.R                  |  8 ++--
 tests/testthat/test-dbarts.R                 |  2 +-
 tests/testthat/test-delayed_sl3.R            |  9 ++--
 tests/testthat/test-density-pooled_hazards.R |  9 ++--
 tests/testthat/test-density-semiparametric.R | 30 ++++++-------
 tests/testthat/test-density_hse.R            | 17 +++----
 tests/testthat/test-density_utils.R          | 14 +++---
 tests/testthat/test-ga.R                     |  6 +--
 tests/testthat/test-lightgbm.R               |  3 +-
 tests/testthat/test-mean.R                   |  4 +-
 tests/testthat/test-nnet.R                   | 12 +++--
 tests/testthat/test-offset.R                 | 10 ++---
 tests/testthat/test-pipeline.R               | 14 +++---
 tests/testthat/test-randomForest.R           | 18 ++++----
 tests/testthat/test-ranger.R                 | 15 ++++---
 tests/testthat/test-rugarch.R                | 26 ++++++-----
 tests/testthat/test-sl-timeseries.R          | 19 ++++----
 tests/testthat/test-sl_fold.R                |  8 ++--
 63 files changed, 736 insertions(+), 255 deletions(-)

diff --git a/R/Lrnr_cv.R b/R/Lrnr_cv.R
index c626278e..00c65b64 100644
--- a/R/Lrnr_cv.R
+++ b/R/Lrnr_cv.R
@@ -76,22 +76,22 @@ interpret_fold_number <- function(fold_number) {
 #'   This can then be accessed with predict_fold(task, fold_number="full")
 #'   }
 #' }
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(origami)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' covars <- c(
 #'   "apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn"
 #' )
 #' outcome <- "haz"
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
 #' glm_learner <- Lrnr_glm$new()
 #' cv_glm <- Lrnr_cv$new(glm_learner, folds = make_folds(cpp_imputed, V = 10))
-#' 
+#'
 #' # train cv learner
 #' cv_glm_fit <- cv_glm$train(task)
 #' preds <- cv_glm_fit$predict()
diff --git a/R/Lrnr_dbarts.R b/R/Lrnr_dbarts.R
index 4fbf1003..11dd33dc 100644
--- a/R/Lrnr_dbarts.R
+++ b/R/Lrnr_dbarts.R
@@ -99,18 +99,18 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' set.seed(123)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
 #' dbart_learner <- make_learner(Lrnr_dbarts, ndpost = 200)
-#' 
+#'
 #' # train dbart learner and make predictions
 #' dbart_fit <- dbart_learner$train(task)
 #' preds <- dbart_fit$predict()
diff --git a/R/Lrnr_density_discretize.R b/R/Lrnr_density_discretize.R
index af02846a..20160f39 100644
--- a/R/Lrnr_density_discretize.R
+++ b/R/Lrnr_density_discretize.R
@@ -23,17 +23,18 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train density discretize learner and make predictions
 #' lrnr_discretize <- Lrnr_density_discretize$new(
 #'   categorical_learner = Lrnr_glmnet$new()
diff --git a/R/Lrnr_density_hse.R b/R/Lrnr_density_hse.R
index cde26515..3c9e0459 100644
--- a/R/Lrnr_density_hse.R
+++ b/R/Lrnr_density_hse.R
@@ -26,16 +26,17 @@
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train density hse learner and make predictions
 #' lrnr_density_hse <- Lrnr_density_hse$new(mean_learner = Lrnr_glm$new())
 #' fit_density_hse <- lrnr_density_hse$train(task)
diff --git a/R/Lrnr_density_semiparametric.R b/R/Lrnr_density_semiparametric.R
index 8cd9fe95..916f112d 100644
--- a/R/Lrnr_density_semiparametric.R
+++ b/R/Lrnr_density_semiparametric.R
@@ -26,16 +26,17 @@
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train density hse learner and make predictions
 #' lrnr_density_semi <- Lrnr_density_semiparametric$new(
 #'   mean_learner = Lrnr_glm$new()
diff --git a/R/Lrnr_grf.R b/R/Lrnr_grf.R
index 95a61a54..5ef96fe2 100644
--- a/R/Lrnr_grf.R
+++ b/R/Lrnr_grf.R
@@ -68,17 +68,18 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train grf learner and make predictions
 #' lrnr_grf <- Lrnr_grf$new(seed = 123)
 #' lrnr_grf_fit <- lrnr_grf$train(task)
diff --git a/R/Lrnr_gts.R b/R/Lrnr_gts.R
index b9c7ee80..cdf1f818 100644
--- a/R/Lrnr_gts.R
+++ b/R/Lrnr_gts.R
@@ -59,12 +59,12 @@
 #'     are going to be used.}
 #' }
 #'
-#' @examples 
+#' @examples
 #' # Example adapted from hts package manual
 #' # The hierarchical structure looks like 2 child nodes associated with level 1,
 #' # which are followed by 3 and 2 sub-child nodes respectively at level 2.
 #' library(hts)
-#' 
+#'
 #' set.seed(3274)
 #' abc <- as.data.table(5 + matrix(sort(rnorm(200)), ncol = 4, nrow = 50))
 #' setnames(abc, paste("Series", 1:ncol(abc), sep = "_"))
@@ -72,14 +72,14 @@
 #' grps <- rbind(c(1, 1, 2, 2), c(1, 2, 1, 2))
 #' horizon <- 12
 #' suppressWarnings(abc_long <- melt(abc, id = "time", variable.name = "series"))
-#' 
+#'
 #' # create sl3 task (no outcome for hierarchical/grouped series)
 #' node_list <- list(outcome = "value", time = "time", id = "series")
 #' train_task <- sl3_Task$new(data = abc_long, nodes = node_list)
 #' test_data <- expand.grid(time = 51:55, series = unique(abc_long$series))
 #' test_data <- as.data.table(test_data)[, value := 0]
 #' test_task <- sl3_Task$new(data = test_data, nodes = node_list)
-#' 
+#'
 #' gts_learner <- Lrnr_gts$new()
 #' gts_learner_fit <- gts_learner$train(train_task)
 #' gts_learner_preds <- gts_learner_fit$predict(test_task)
diff --git a/R/Lrnr_h2o_glm.R b/R/Lrnr_h2o_glm.R
index 896a9f6c..6aec023c 100644
--- a/R/Lrnr_h2o_glm.R
+++ b/R/Lrnr_h2o_glm.R
@@ -69,19 +69,20 @@ define_h2o_X <- function(task, outcome_type = NULL) {
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' library(h2o)
 #' suppressWarnings(h2o.init())
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train h2o glm learner and make predictions
 #' lrnr_h2o <- Lrnr_h2o_glm$new()
 #' lrnr_h2o_fit <- lrnr_h2o$train(task)
diff --git a/R/Lrnr_h2o_grid.R b/R/Lrnr_h2o_grid.R
index 8ee03367..6c4b43d5 100644
--- a/R/Lrnr_h2o_grid.R
+++ b/R/Lrnr_h2o_grid.R
@@ -43,12 +43,12 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(h2o)
 #' suppressWarnings(h2o.init())
 #' set.seed(1)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' covars <- c(
@@ -57,13 +57,15 @@
 #' )
 #' outcome <- "haz"
 #' cpp_imputed <- cpp_imputed[1:150, ]
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
-#' 
+#'
 #' # h2o grid search hyperparameter alpha
-#' h2o_glm_grid <- Lrnr_h2o_grid$new(algorithm = "glm",
-#'                                   hyper_params = list(alpha = c(0, 0.5)))
+#' h2o_glm_grid <- Lrnr_h2o_grid$new(
+#'   algorithm = "glm",
+#'   hyper_params = list(alpha = c(0, 0.5))
+#' )
 #' h2o_glm_grid_fit <- h2o_glm_grid$train(task)
 #' pred <- h2o_glm_grid_fit$predict()
 Lrnr_h2o_grid <- R6Class(
diff --git a/R/Lrnr_independent_binomial.R b/R/Lrnr_independent_binomial.R
index 3ed59aa5..1a349465 100644
--- a/R/Lrnr_independent_binomial.R
+++ b/R/Lrnr_independent_binomial.R
@@ -27,21 +27,22 @@
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' library(dplyr)
-#' 
+#'
 #' # load example data
 #' data(cpp)
 #' cpp <- cpp %>%
 #'   select(c(bmi, agedays, feeding)) %>%
 #'   mutate(feeding = as.factor(feeding)) %>%
 #'   na.omit()
-#' 
+#'
 #' # create sl3 task
-#' task <- make_sl3_Task(cpp, 
-#'                       covariates = c("agedays", "bmi"), 
-#'                       outcome = "feeding")
-#' 
+#' task <- make_sl3_Task(cpp,
+#'   covariates = c("agedays", "bmi"),
+#'   outcome = "feeding"
+#' )
+#'
 #' # train independent binomial learner and make predictions
 #' lrnr_indbinomial <- make_learner(Lrnr_independent_binomial)
 #' fit <- lrnr_indbinomial$train(task)
diff --git a/R/Lrnr_multiple_ts.R b/R/Lrnr_multiple_ts.R
index 188a195b..92d7d82e 100644
--- a/R/Lrnr_multiple_ts.R
+++ b/R/Lrnr_multiple_ts.R
@@ -26,13 +26,13 @@
 #'    \code{learner$train}. See its documentation for details.
 #'   }
 #' }
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(origami)
 #' library(dplyr)
-#' 
+#'
 #' set.seed(123)
-#' 
+#'
 #' # Simulate simple AR(2) process
 #' data <- matrix(arima.sim(model = list(ar = c(.9, -.2)), n = 200))
 #' id <- c(rep("Series_1", 50), rep("Series_2", 50), rep("Series_3", 50), rep("Series_4", 50))
@@ -41,36 +41,36 @@
 #' data <- data %>%
 #'   group_by(id) %>%
 #'   dplyr::mutate(time = 1:n())
-#' 
+#'
 #' data$W1 <- rbinom(200, 1, 0.6)
 #' data$W2 <- rbinom(200, 1, 0.2)
-#' 
+#'
 #' data <- as.data.table(data)
-#' 
+#'
 #' folds <- origami::make_folds(data,
-#'                              t = max(data$time),
-#'                              id = data$id,
-#'                              time = data$time,
-#'                              fold_fun = folds_rolling_window_pooled,
-#'                              window_size = 20,
-#'                              validation_size = 15,
-#'                              gap = 0,
-#'                              batch = 10
+#'   t = max(data$time),
+#'   id = data$id,
+#'   time = data$time,
+#'   fold_fun = folds_rolling_window_pooled,
+#'   window_size = 20,
+#'   validation_size = 15,
+#'   gap = 0,
+#'   batch = 10
 #' )
-#' 
+#'
 #' task <- sl3_Task$new(
 #'   data = data, outcome = "data",
 #'   time = "time", id = "id",
 #'   covariates = c("W1", "W2"),
 #'   folds = folds
 #' )
-#' 
+#'
 #' train_task <- training(task, fold = task$folds[[1]])
 #' valid_task <- validation(task, fold = task$folds[[1]])
-#' 
+#'
 #' lrnr_arima <- Lrnr_arima$new()
 #' multiple_ts_arima <- Lrnr_multiple_ts$new(learner = lrnr_arima)
-#' 
+#'
 #' multiple_ts_arima_fit <- multiple_ts_arima$train(train_task)
 #' multiple_ts_arima_preds <- multiple_ts_arima_fit$predict(valid_task)
 Lrnr_multiple_ts <- R6Class(
diff --git a/R/Lrnr_multivariate.R b/R/Lrnr_multivariate.R
index 8c452128..e87c27be 100644
--- a/R/Lrnr_multivariate.R
+++ b/R/Lrnr_multivariate.R
@@ -25,10 +25,10 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(data.table)
-#' 
+#'
 #' # simulate data
 #' set.seed(123)
 #' n <- 1000
@@ -41,12 +41,13 @@
 #' data <- data.table(W, Y)
 #' covariates <- grep("W", names(data), value = TRUE)
 #' outcomes <- grep("Y", names(data), value = TRUE)
-#' 
+#'
 #' # make sl3 task
 #' task <- sl3_Task$new(data.table::copy(data),
-#'                      covariates = covariates,
-#'                      outcome = outcomes)
-#' 
+#'   covariates = covariates,
+#'   outcome = outcomes
+#' )
+#'
 #' # train multivariate learner and make predictions
 #' mv_learner <- make_learner(Lrnr_multivariate, make_learner(Lrnr_glm_fast))
 #' mv_fit <- mv_learner$train(task)
diff --git a/R/Lrnr_nnet.R b/R/Lrnr_nnet.R
index f42ffddd..271eb9d6 100644
--- a/R/Lrnr_nnet.R
+++ b/R/Lrnr_nnet.R
@@ -33,18 +33,18 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' set.seed(123)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' covars <- c("bmi", "parity", "mage", "sexn")
 #' outcome <- "haz"
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
-#' 
+#'
 #' # train neural networks and make predictions
 #' lrnr_nnet <- Lrnr_nnet$new(linout = TRUE, size = 10, maxit = 1000)
 #' fit <- lrnr_nnet$train(task)
diff --git a/R/Lrnr_pca.R b/R/Lrnr_pca.R
index cfe39377..be7b971c 100644
--- a/R/Lrnr_pca.R
+++ b/R/Lrnr_pca.R
@@ -40,9 +40,9 @@
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' set.seed(37912)
-#' 
+#'
 #' # load example data
 #' ncomp <- 3
 #' data(cpp_imputed)
@@ -51,15 +51,15 @@
 #'   "sexn"
 #' )
 #' outcome <- "haz"
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
-#' 
+#'
 #' # define learners
 #' glm_fast <- Lrnr_glm_fast$new(intercept = FALSE)
 #' pca_sl3 <- Lrnr_pca$new(n_comp = ncomp, center = TRUE, scale. = TRUE)
 #' pcr_pipe_sl3 <- Pipeline$new(pca_sl3, glm_fast)
-#' 
+#'
 #' # create stacks + train and predict
 #' pcr_pipe_sl3_fit <- pcr_pipe_sl3$train(task)
 #' pcr_pred <- pcr_pipe_sl3_fit$predict()
diff --git a/R/Lrnr_pooled_hazards.R b/R/Lrnr_pooled_hazards.R
index 63666c2f..84b7a396 100644
--- a/R/Lrnr_pooled_hazards.R
+++ b/R/Lrnr_pooled_hazards.R
@@ -25,18 +25,18 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(data.table)
 #' set.seed(74294)
-#' 
+#'
 #' n <- 500
 #' x <- rnorm(n)
 #' epsilon <- rnorm(n)
 #' y <- 3 * x + epsilon
 #' data <- data.table(x = x, y = y)
 #' task <- sl3_Task$new(data, covariates = c("x"), outcome = "y")
-#' 
+#'
 #' # instantiate learners
 #' hal <- Lrnr_hal9001$new(
 #'   lambda = exp(seq(-1, -13, length = 100)),
@@ -49,7 +49,7 @@
 #'   type = "equal_range",
 #'   n_bins = 5
 #' )
-#' 
+#'
 #' # fit discrete density model to pooled hazards data
 #' set.seed(74294)
 #' fit_density <- density_learner$train(task)
diff --git a/R/Lrnr_screener_augment.R b/R/Lrnr_screener_augment.R
index 39210de0..e3be1989 100644
--- a/R/Lrnr_screener_augment.R
+++ b/R/Lrnr_screener_augment.R
@@ -26,10 +26,10 @@
 #'   whether or not these covariates were selected by the screener.}
 #'   \item{\code{...}}{Other parameters passed to \code{screener}.}
 #' }
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(data.table)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' setDT(cpp_imputed)
@@ -39,13 +39,13 @@
 #'   "sexn"
 #' )
 #' outcome <- "haz"
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(data.table::copy(cpp_imputed),
-#'                      covariates = covars,
-#'                      outcome = outcome
+#'   covariates = covars,
+#'   outcome = outcome
 #' )
-#' 
+#'
 #' screener_cor <- make_learner(
 #'   Lrnr_screener_correlation,
 #'   type = "rank",
diff --git a/R/Lrnr_screener_coefs.R b/R/Lrnr_screener_coefs.R
index c0a716d6..f6071638 100644
--- a/R/Lrnr_screener_coefs.R
+++ b/R/Lrnr_screener_coefs.R
@@ -28,10 +28,10 @@
 #'     applicable when supplied \code{learner} is a \code{\link{Lrnr_glmnet}}.}
 #'   \item{\code{...}}{Other parameters passed to \code{learner}.}
 #' }
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(data.table)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' setDT(cpp_imputed)
@@ -41,18 +41,18 @@
 #'   "sexn"
 #' )
 #' outcome <- "haz"
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(data.table::copy(cpp_imputed),
-#'                      covariates = covars,
-#'                      outcome = outcome
+#'   covariates = covars,
+#'   outcome = outcome
 #' )
-#' 
+#'
 #' lrnr_glmnet <- make_learner(Lrnr_glmnet)
 #' lrnr_glm <- make_learner(Lrnr_glm)
 #' lrnr_mean <- make_learner(Lrnr_mean)
 #' lrnrs <- make_learner(Stack, lrnr_glm, lrnr_mean)
-#' 
+#'
 #' glm_screener <- make_learner(Lrnr_screener_coefs, lrnr_glm, max_screen = 2)
 #' glm_screener_pipeline <- make_learner(Pipeline, glm_screener, lrnrs)
 #' fit_glm_screener_pipeline <- glm_screener_pipeline$train(task)
diff --git a/R/Lrnr_screener_correlation.R b/R/Lrnr_screener_correlation.R
index ad4aa8c0..2d43fa9d 100644
--- a/R/Lrnr_screener_correlation.R
+++ b/R/Lrnr_screener_correlation.R
@@ -34,10 +34,10 @@
 #'   \item{\code{min_screen = 2}}{Minimum number of covariates to select. Used
 #'   in pvalue_threshold screening procedure.}
 #' }
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(data.table)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' setDT(cpp_imputed)
@@ -47,18 +47,18 @@
 #'   "sexn"
 #' )
 #' outcome <- "haz"
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(data.table::copy(cpp_imputed),
-#'                      covariates = covars,
-#'                      outcome = outcome
+#'   covariates = covars,
+#'   outcome = outcome
 #' )
-#' 
+#'
 #' lrnr_glmnet <- make_learner(Lrnr_glmnet)
 #' lrnr_glm <- make_learner(Lrnr_glm)
 #' lrnr_mean <- make_learner(Lrnr_mean)
 #' lrnrs <- make_learner(Stack, lrnr_glm, lrnr_mean)
-#' 
+#'
 #' screen_corP <- make_learner(Lrnr_screener_correlation, type = "threshold")
 #' corP_pipeline <- make_learner(Pipeline, screen_corP, lrnrs)
 #' fit_corP <- corP_pipeline$train(task)
diff --git a/R/Lrnr_stratified.R b/R/Lrnr_stratified.R
index 0e367952..fa44d6c3 100644
--- a/R/Lrnr_stratified.R
+++ b/R/Lrnr_stratified.R
@@ -26,24 +26,24 @@
 #'    \code{learner$train}. See its documentation for details.
 #'   }
 #' }
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(data.table)
-#' 
+#'
 #' # load example data set
 #' data(cpp_imputed)
 #' setDT(cpp_imputed)
-#' 
+#'
 #' # use covariates of intest and the outcome to build a task object
 #' covars <- c("apgar1", "apgar5", "sexn")
 #' task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = "haz")
-#' 
+#'
 #' hal_lrnr <- Lrnr_hal9001$new(fit_control = list(n_folds = 3))
 #' stratified_hal <- Lrnr_stratified$new(
 #'   learner = hal_lrnr,
 #'   variable_stratify = "sexn"
 #' )
-#' 
+#'
 #' # stratified learner
 #' set.seed(123)
 #' stratified_hal_fit <- stratified_hal$train(task)
diff --git a/R/Lrnr_subset_covariates.R b/R/Lrnr_subset_covariates.R
index 4ef4a800..3ec08814 100644
--- a/R/Lrnr_subset_covariates.R
+++ b/R/Lrnr_subset_covariates.R
@@ -25,25 +25,25 @@
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
 #' covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
 #' outcome <- "haz"
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(data.table::copy(cpp_imputed),
-#'                      covariates = covars,
-#'                      outcome = outcome,
-#'                      folds = origami::make_folds(cpp_imputed, V = 3)
+#'   covariates = covars,
+#'   outcome = outcome,
+#'   folds = origami::make_folds(cpp_imputed, V = 3)
 #' )
-#' 
+#'
 #' glm_learner <- Lrnr_glm$new()
 #' glmnet_learner <- Lrnr_glmnet$new()
 #' subset_apgar <- Lrnr_subset_covariates$new(covariates = c("apgar1", "apgar5"))
 #' learners <- list(glm_learner, glmnet_learner, subset_apgar)
 #' sl <- make_learner(Lrnr_sl, learners, glm_learner)
-#' 
+#'
 #' sl_fit <- sl$train(task)
 #' sl_pred <- sl_fit$predict()
 Lrnr_subset_covariates <- R6Class(
diff --git a/man/Lrnr_cv.Rd b/man/Lrnr_cv.Rd
index b6eeaa6c..a2866897 100644
--- a/man/Lrnr_cv.Rd
+++ b/man/Lrnr_cv.Rd
@@ -26,6 +26,25 @@ This can then be accessed with predict_fold(task, fold_number="full")
 }
 }
 
+\examples{
+library(origami)
+
+# load example data
+data(cpp_imputed)
+covars <- c(
+  "apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn"
+)
+outcome <- "haz"
+
+# create sl3 task
+task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
+glm_learner <- Lrnr_glm$new()
+cv_glm <- Lrnr_cv$new(glm_learner, folds = make_folds(cpp_imputed, V = 10))
+
+# train cv learner
+cv_glm_fit <- cv_glm$train(task)
+preds <- cv_glm_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_dbarts.Rd b/man/Lrnr_dbarts.Rd
index 6a521c30..27c57ef3 100644
--- a/man/Lrnr_dbarts.Rd
+++ b/man/Lrnr_dbarts.Rd
@@ -108,6 +108,21 @@ by all learners.
 }
 }
 
+\examples{
+set.seed(123)
+
+# load example data
+data(cpp_imputed)
+covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
+
+# create sl3 task
+task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
+dbart_learner <- make_learner(Lrnr_dbarts, ndpost = 200)
+
+# train dbart learner and make predictions
+dbart_fit <- dbart_learner$train(task)
+preds <- dbart_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_density_discretize.Rd b/man/Lrnr_density_discretize.Rd
index 105b76b0..53a4627a 100644
--- a/man/Lrnr_density_discretize.Rd
+++ b/man/Lrnr_density_discretize.Rd
@@ -34,6 +34,23 @@ by all learners.
 }
 }
 
+\examples{
+# load example data
+data(cpp_imputed)
+
+# create sl3 task
+task <- sl3_Task$new(
+  cpp_imputed, 
+  covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
+  outcome = "haz")
+
+# train density discretize learner and make predictions
+lrnr_discretize <- Lrnr_density_discretize$new(
+  categorical_learner = Lrnr_glmnet$new()
+)
+lrnr_discretize_fit <- lrnr_discretize$train(task)
+lrnr_discretize_pred <- lrnr_discretize_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_density_hse.Rd b/man/Lrnr_density_hse.Rd
index d0541bfa..3290c560 100644
--- a/man/Lrnr_density_hse.Rd
+++ b/man/Lrnr_density_hse.Rd
@@ -35,6 +35,21 @@ by all learners.
 }
 }
 
+\examples{
+# load example data
+data(cpp_imputed)
+
+# create sl3 task
+task <- sl3_Task$new(
+  cpp_imputed, 
+  covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
+  outcome = "haz")
+
+# train density hse learner and make predictions
+lrnr_density_hse <- Lrnr_density_hse$new(mean_learner = Lrnr_glm$new())
+fit_density_hse <- lrnr_density_hse$train(task)
+preds_density_hse <- fit_density_hse$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_density_semiparametric.Rd b/man/Lrnr_density_semiparametric.Rd
index acaca399..76bb28a9 100644
--- a/man/Lrnr_density_semiparametric.Rd
+++ b/man/Lrnr_density_semiparametric.Rd
@@ -35,6 +35,23 @@ by all learners.
 }
 }
 
+\examples{
+# load example data
+data(cpp_imputed)
+
+# create sl3 task
+task <- sl3_Task$new(
+  cpp_imputed, 
+  covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
+  outcome = "haz")
+
+# train density hse learner and make predictions
+lrnr_density_semi <- Lrnr_density_semiparametric$new(
+  mean_learner = Lrnr_glm$new()
+)
+lrnr_density_semi_fit <- lrnr_density_semi$train(task)
+lrnr_density_semi_pred <- lrnr_density_semi_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_grf.Rd b/man/Lrnr_grf.Rd
index 147460b4..d3ab470b 100644
--- a/man/Lrnr_grf.Rd
+++ b/man/Lrnr_grf.Rd
@@ -77,6 +77,21 @@ by all learners.
 }
 }
 
+\examples{
+# load example data
+data(cpp_imputed)
+
+# create sl3 task
+task <- sl3_Task$new(
+  cpp_imputed, 
+  covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
+  outcome = "haz")
+
+# train grf learner and make predictions
+lrnr_grf <- Lrnr_grf$new(seed = 123)
+lrnr_grf_fit <- lrnr_grf$train(task)
+lrnr_grf_pred <- lrnr_grf_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_gts.Rd b/man/Lrnr_gts.Rd
index 6d5cbd16..b610af1c 100644
--- a/man/Lrnr_gts.Rd
+++ b/man/Lrnr_gts.Rd
@@ -57,6 +57,31 @@ are going to be used.}
 }
 }
 
+\examples{
+# Example adapted from hts package manual
+# The hierarchical structure looks like 2 child nodes associated with level 1,
+# which are followed by 3 and 2 sub-child nodes respectively at level 2.
+library(hts)
+
+set.seed(3274)
+abc <- as.data.table(5 + matrix(sort(rnorm(200)), ncol = 4, nrow = 50))
+setnames(abc, paste("Series", 1:ncol(abc), sep = "_"))
+abc[, time := .I]
+grps <- rbind(c(1, 1, 2, 2), c(1, 2, 1, 2))
+horizon <- 12
+suppressWarnings(abc_long <- melt(abc, id = "time", variable.name = "series"))
+
+# create sl3 task (no outcome for hierarchical/grouped series)
+node_list <- list(outcome = "value", time = "time", id = "series")
+train_task <- sl3_Task$new(data = abc_long, nodes = node_list)
+test_data <- expand.grid(time = 51:55, series = unique(abc_long$series))
+test_data <- as.data.table(test_data)[, value := 0]
+test_task <- sl3_Task$new(data = test_data, nodes = node_list)
+
+gts_learner <- Lrnr_gts$new()
+gts_learner_fit <- gts_learner$train(train_task)
+gts_learner_preds <- gts_learner_fit$predict(test_task)
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_h2o_glm.Rd b/man/Lrnr_h2o_glm.Rd
index 9c626b98..f12906e3 100644
--- a/man/Lrnr_h2o_glm.Rd
+++ b/man/Lrnr_h2o_glm.Rd
@@ -61,6 +61,24 @@ by all learners.
 }
 }
 
+\examples{
+library(h2o)
+suppressWarnings(h2o.init())
+
+# load example data
+data(cpp_imputed)
+
+# create sl3 task
+task <- sl3_Task$new(
+  cpp_imputed, 
+  covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
+  outcome = "haz")
+
+# train h2o glm learner and make predictions
+lrnr_h2o <- Lrnr_h2o_glm$new()
+lrnr_h2o_fit <- lrnr_h2o$train(task)
+lrnr_h2o_pred <- lrnr_h2o_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_h2o_grid.Rd b/man/Lrnr_h2o_grid.Rd
index 38e65f02..6dde29d7 100644
--- a/man/Lrnr_h2o_grid.Rd
+++ b/man/Lrnr_h2o_grid.Rd
@@ -56,6 +56,29 @@ by all learners.
 }
 }
 
+\examples{
+library(h2o)
+suppressWarnings(h2o.init())
+set.seed(1)
+
+# load example data
+data(cpp_imputed)
+covars <- c(
+  "apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs",
+  "sexn"
+)
+outcome <- "haz"
+cpp_imputed <- cpp_imputed[1:150, ]
+
+# create sl3 task
+task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
+
+# h2o grid search hyperparameter alpha
+h2o_glm_grid <- Lrnr_h2o_grid$new(algorithm = "glm",
+                                  hyper_params = list(alpha = c(0, 0.5)))
+h2o_glm_grid_fit <- h2o_glm_grid$train(task)
+pred <- h2o_glm_grid_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_independent_binomial.Rd b/man/Lrnr_independent_binomial.Rd
index f4af7502..cea7a0d7 100644
--- a/man/Lrnr_independent_binomial.Rd
+++ b/man/Lrnr_independent_binomial.Rd
@@ -36,6 +36,26 @@ by all learners.
 }
 }
 
+\examples{
+library(dplyr)
+
+# load example data
+data(cpp)
+cpp <- cpp \%>\%
+  select(c(bmi, agedays, feeding)) \%>\%
+  mutate(feeding = as.factor(feeding)) \%>\%
+  na.omit()
+
+# create sl3 task
+task <- make_sl3_Task(cpp, 
+                      covariates = c("agedays", "bmi"), 
+                      outcome = "feeding")
+
+# train independent binomial learner and make predictions
+lrnr_indbinomial <- make_learner(Lrnr_independent_binomial)
+fit <- lrnr_indbinomial$train(task)
+preds <- fit$predict(task)
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_multiple_ts.Rd b/man/Lrnr_multiple_ts.Rd
index a7c6ec44..145002af 100644
--- a/man/Lrnr_multiple_ts.Rd
+++ b/man/Lrnr_multiple_ts.Rd
@@ -29,6 +29,53 @@ variables with discrete levels coded as \code{numeric}.
 }
 }
 
+\examples{
+library(origami)
+library(dplyr)
+
+set.seed(123)
+
+# Simulate simple AR(2) process
+data <- matrix(arima.sim(model = list(ar = c(.9, -.2)), n = 200))
+id <- c(rep("Series_1", 50), rep("Series_2", 50), rep("Series_3", 50), rep("Series_4", 50))
+data <- data.frame(data)
+data$id <- as.factor(id)
+data <- data \%>\%
+  group_by(id) \%>\%
+  dplyr::mutate(time = 1:n())
+
+data$W1 <- rbinom(200, 1, 0.6)
+data$W2 <- rbinom(200, 1, 0.2)
+
+data <- as.data.table(data)
+
+folds <- origami::make_folds(data,
+                             t = max(data$time),
+                             id = data$id,
+                             time = data$time,
+                             fold_fun = folds_rolling_window_pooled,
+                             window_size = 20,
+                             validation_size = 15,
+                             gap = 0,
+                             batch = 10
+)
+
+task <- sl3_Task$new(
+  data = data, outcome = "data",
+  time = "time", id = "id",
+  covariates = c("W1", "W2"),
+  folds = folds
+)
+
+train_task <- training(task, fold = task$folds[[1]])
+valid_task <- validation(task, fold = task$folds[[1]])
+
+lrnr_arima <- Lrnr_arima$new()
+multiple_ts_arima <- Lrnr_multiple_ts$new(learner = lrnr_arima)
+
+multiple_ts_arima_fit <- multiple_ts_arima$train(train_task)
+multiple_ts_arima_preds <- multiple_ts_arima_fit$predict(valid_task)
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_multivariate.Rd b/man/Lrnr_multivariate.Rd
index 13823074..f5319908 100644
--- a/man/Lrnr_multivariate.Rd
+++ b/man/Lrnr_multivariate.Rd
@@ -35,6 +35,33 @@ by all learners.
 }
 }
 
+\examples{
+library(data.table)
+
+# simulate data
+set.seed(123)
+n <- 1000
+p <- 5
+pY <- 3
+W <- matrix(rnorm(n * p), nrow = n)
+colnames(W) <- sprintf("W\%d", seq_len(p))
+Y <- matrix(rnorm(n * pY, 0, 0.2) + W[, 1], nrow = n)
+colnames(Y) <- sprintf("Y\%d", seq_len(pY))
+data <- data.table(W, Y)
+covariates <- grep("W", names(data), value = TRUE)
+outcomes <- grep("Y", names(data), value = TRUE)
+
+# make sl3 task
+task <- sl3_Task$new(data.table::copy(data),
+                     covariates = covariates,
+                     outcome = outcomes)
+
+# train multivariate learner and make predictions
+mv_learner <- make_learner(Lrnr_multivariate, make_learner(Lrnr_glm_fast))
+mv_fit <- mv_learner$train(task)
+mv_pred <- mv_fit$predict(task)
+mv_pred <- unpack_predictions(mv_pred)
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_nnet.Rd b/man/Lrnr_nnet.Rd
index 832e1cf1..5e8d61cc 100644
--- a/man/Lrnr_nnet.Rd
+++ b/man/Lrnr_nnet.Rd
@@ -43,6 +43,22 @@ by all learners.
 }
 }
 
+\examples{
+set.seed(123)
+
+# load example data
+data(cpp_imputed)
+covars <- c("bmi", "parity", "mage", "sexn")
+outcome <- "haz"
+
+# create sl3 task
+task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
+
+# train neural networks and make predictions
+lrnr_nnet <- Lrnr_nnet$new(linout = TRUE, size = 10, maxit = 1000)
+fit <- lrnr_nnet$train(task)
+preds <- fit$predict(task)
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_pca.Rd b/man/Lrnr_pca.Rd
index b3fd5c86..ec89caec 100644
--- a/man/Lrnr_pca.Rd
+++ b/man/Lrnr_pca.Rd
@@ -49,6 +49,30 @@ by all learners.
 }
 }
 
+\examples{
+set.seed(37912)
+
+# load example data
+ncomp <- 3
+data(cpp_imputed)
+covars <- c(
+  "apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs",
+  "sexn"
+)
+outcome <- "haz"
+
+# create sl3 task
+task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
+
+# define learners
+glm_fast <- Lrnr_glm_fast$new(intercept = FALSE)
+pca_sl3 <- Lrnr_pca$new(n_comp = ncomp, center = TRUE, scale. = TRUE)
+pcr_pipe_sl3 <- Pipeline$new(pca_sl3, glm_fast)
+
+# create stacks + train and predict
+pcr_pipe_sl3_fit <- pcr_pipe_sl3$train(task)
+pcr_pred <- pcr_pipe_sl3_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_pooled_hazards.Rd b/man/Lrnr_pooled_hazards.Rd
index 292c1dce..48f4e03f 100644
--- a/man/Lrnr_pooled_hazards.Rd
+++ b/man/Lrnr_pooled_hazards.Rd
@@ -35,6 +35,35 @@ by all learners.
 }
 }
 
+\examples{
+library(data.table)
+set.seed(74294)
+
+n <- 500
+x <- rnorm(n)
+epsilon <- rnorm(n)
+y <- 3 * x + epsilon
+data <- data.table(x = x, y = y)
+task <- sl3_Task$new(data, covariates = c("x"), outcome = "y")
+
+# instantiate learners
+hal <- Lrnr_hal9001$new(
+  lambda = exp(seq(-1, -13, length = 100)),
+  max_degree = 6,
+  smoothness_orders = 0
+)
+hazard_learner <- Lrnr_pooled_hazards$new(hal)
+density_learner <- Lrnr_density_discretize$new(
+  hazard_learner,
+  type = "equal_range",
+  n_bins = 5
+)
+
+# fit discrete density model to pooled hazards data
+set.seed(74294)
+fit_density <- density_learner$train(task)
+pred_density <- fit_density$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_screener_augment.Rd b/man/Lrnr_screener_augment.Rd
index a84db27b..a0af7b63 100644
--- a/man/Lrnr_screener_augment.Rd
+++ b/man/Lrnr_screener_augment.Rd
@@ -26,6 +26,35 @@ whether or not these covariates were selected by the screener.}
 }
 }
 
+\examples{
+library(data.table)
+
+# load example data
+data(cpp_imputed)
+setDT(cpp_imputed)
+cpp_imputed[, parity_cat := factor(ifelse(parity < 4, parity, 4))]
+covars <- c(
+  "apgar1", "apgar5", "parity_cat", "gagebrth", "mage", "meducyrs",
+  "sexn"
+)
+outcome <- "haz"
+
+# create sl3 task
+task <- sl3_Task$new(data.table::copy(cpp_imputed),
+                     covariates = covars,
+                     outcome = outcome
+)
+
+screener_cor <- make_learner(
+  Lrnr_screener_correlation,
+  type = "rank",
+  num_screen = 2
+)
+screener_augment <- Lrnr_screener_augment$new(screener_cor, covars)
+screener_fit <- screener_augment$train(task)
+selected <- screener_fit$fit_object$selected
+screener_selected <- screener_fit$fit_object$screener_selected
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_screener_coefs.Rd b/man/Lrnr_screener_coefs.Rd
index f55f6ca2..40b8dbe1 100644
--- a/man/Lrnr_screener_coefs.Rd
+++ b/man/Lrnr_screener_coefs.Rd
@@ -28,6 +28,35 @@ applicable when supplied \code{learner} is a \code{\link{Lrnr_glmnet}}.}
 }
 }
 
+\examples{
+library(data.table)
+
+# load example data
+data(cpp_imputed)
+setDT(cpp_imputed)
+cpp_imputed[, parity_cat := factor(ifelse(parity < 4, parity, 4))]
+covars <- c(
+  "apgar1", "apgar5", "parity_cat", "gagebrth", "mage", "meducyrs",
+  "sexn"
+)
+outcome <- "haz"
+
+# create sl3 task
+task <- sl3_Task$new(data.table::copy(cpp_imputed),
+                     covariates = covars,
+                     outcome = outcome
+)
+
+lrnr_glmnet <- make_learner(Lrnr_glmnet)
+lrnr_glm <- make_learner(Lrnr_glm)
+lrnr_mean <- make_learner(Lrnr_mean)
+lrnrs <- make_learner(Stack, lrnr_glm, lrnr_mean)
+
+glm_screener <- make_learner(Lrnr_screener_coefs, lrnr_glm, max_screen = 2)
+glm_screener_pipeline <- make_learner(Pipeline, glm_screener, lrnrs)
+fit_glm_screener_pipeline <- glm_screener_pipeline$train(task)
+preds_glm_screener_pipeline <- fit_glm_screener_pipeline$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_screener_correlation.Rd b/man/Lrnr_screener_correlation.Rd
index 8d621145..5c93d11c 100644
--- a/man/Lrnr_screener_correlation.Rd
+++ b/man/Lrnr_screener_correlation.Rd
@@ -34,6 +34,35 @@ in pvalue_threshold screening procedure.}
 }
 }
 
+\examples{
+library(data.table)
+
+# load example data
+data(cpp_imputed)
+setDT(cpp_imputed)
+cpp_imputed[, parity_cat := factor(ifelse(parity < 4, parity, 4))]
+covars <- c(
+  "apgar1", "apgar5", "parity_cat", "gagebrth", "mage", "meducyrs",
+  "sexn"
+)
+outcome <- "haz"
+
+# create sl3 task
+task <- sl3_Task$new(data.table::copy(cpp_imputed),
+                     covariates = covars,
+                     outcome = outcome
+)
+
+lrnr_glmnet <- make_learner(Lrnr_glmnet)
+lrnr_glm <- make_learner(Lrnr_glm)
+lrnr_mean <- make_learner(Lrnr_mean)
+lrnrs <- make_learner(Stack, lrnr_glm, lrnr_mean)
+
+screen_corP <- make_learner(Lrnr_screener_correlation, type = "threshold")
+corP_pipeline <- make_learner(Pipeline, screen_corP, lrnrs)
+fit_corP <- corP_pipeline$train(task)
+preds_corP_screener <- fit_corP$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_stratified.Rd b/man/Lrnr_stratified.Rd
index d938e666..457a68ce 100644
--- a/man/Lrnr_stratified.Rd
+++ b/man/Lrnr_stratified.Rd
@@ -29,6 +29,28 @@ variables with discrete levels coded as \code{numeric}.
 }
 }
 
+\examples{
+library(data.table)
+
+# load example data set
+data(cpp_imputed)
+setDT(cpp_imputed)
+
+# use covariates of intest and the outcome to build a task object
+covars <- c("apgar1", "apgar5", "sexn")
+task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = "haz")
+
+hal_lrnr <- Lrnr_hal9001$new(fit_control = list(n_folds = 3))
+stratified_hal <- Lrnr_stratified$new(
+  learner = hal_lrnr,
+  variable_stratify = "sexn"
+)
+
+# stratified learner
+set.seed(123)
+stratified_hal_fit <- stratified_hal$train(task)
+stratified_prediction <- stratified_hal_fit$predict(task = task)
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/man/Lrnr_subset_covariates.Rd b/man/Lrnr_subset_covariates.Rd
index 82dc2269..549072e8 100644
--- a/man/Lrnr_subset_covariates.Rd
+++ b/man/Lrnr_subset_covariates.Rd
@@ -35,6 +35,28 @@ by all learners.
 }
 }
 
+\examples{
+# load example data
+data(cpp_imputed)
+covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
+outcome <- "haz"
+
+# create sl3 task
+task <- sl3_Task$new(data.table::copy(cpp_imputed),
+                     covariates = covars,
+                     outcome = outcome,
+                     folds = origami::make_folds(cpp_imputed, V = 3)
+)
+
+glm_learner <- Lrnr_glm$new()
+glmnet_learner <- Lrnr_glmnet$new()
+subset_apgar <- Lrnr_subset_covariates$new(covariates = c("apgar1", "apgar5"))
+learners <- list(glm_learner, glmnet_learner, subset_apgar)
+sl <- make_learner(Lrnr_sl, learners, glm_learner)
+
+sl_fit <- sl$train(task)
+sl_pred <- sl_fit$predict()
+}
 \seealso{
 Other Learners: 
 \code{\link{Custom_chain}},
diff --git a/tests/testthat/test-add_many_columns.R b/tests/testthat/test-add_many_columns.R
index 47956467..655f3628 100644
--- a/tests/testthat/test-add_many_columns.R
+++ b/tests/testthat/test-add_many_columns.R
@@ -8,9 +8,9 @@ test_that("columns were added successful", {
   data(cpp_imputed)
   covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
   outcome <- "haz"
-  
+
   task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
-  
+
   for (i in 1:1e4) {
     new_data <- data.table(A = rnorm(task$nrow))
     z <- task$add_columns(new_data)
diff --git a/tests/testthat/test-bartMachine.R b/tests/testthat/test-bartMachine.R
index cbc4fd44..65f65409 100644
--- a/tests/testthat/test-bartMachine.R
+++ b/tests/testthat/test-bartMachine.R
@@ -19,14 +19,14 @@ test_that("Lrnr_bartMachine produces results matching those of bartMachine::bart
   fit_sl3 <- lrnr_bartMachine$train(task)
   preds_sl3 <- fit_sl3$predict(task)
   rmse_sl3 <- sqrt(mean((preds_sl3 - task$Y)^2))
-  
+
   # classic fit
   fit_classic <- bartMachine::bartMachine(
     X = data.frame(task$X), y = task$Y, seed = 196, verbose = FALSE
   )
   preds_classic <- as.numeric(predict(fit_classic, new_data = task$X))
   rmse_classic <- sqrt(mean((preds_classic - task$Y)^2))
-  
+
   # check equality
   expect_equal(rmse_sl3, rmse_classic, tolerance = 0.1)
 })
@@ -36,10 +36,10 @@ test_that("Lrnr_bartMachine does not fail when cross-validated", {
     Lrnr_bartMachine,
     verbose = FALSE
   ))
-  
+
   cv_lrnr_bartMachine <- Lrnr_cv$new(lrnr_bartMachine)
   fit_cv <- cv_lrnr_bartMachine$train(task)
   preds_cv <- fit_cv$predict(task)
-  
+
   expect_equal(length(preds_cv), nrow(task$data))
 })
diff --git a/tests/testthat/test-binomial_learners.R b/tests/testthat/test-binomial_learners.R
index abbd68a8..7ce52ae1 100644
--- a/tests/testthat/test-binomial_learners.R
+++ b/tests/testthat/test-binomial_learners.R
@@ -68,6 +68,6 @@ test_that("Lrnr_sl binomial integration test", {
   coefs <- coef(sl_fit$fit_object$cv_meta_fit)
   preds <- sl_fit$predict()
   loss <- sl_fit$cv_risk(loss_loglik_binomial)
-  
+
   expect_equal(length(preds), nrow(task$data))
 })
diff --git a/tests/testthat/test-caret.R b/tests/testthat/test-caret.R
index bd487024..2aeab689 100644
--- a/tests/testthat/test-caret.R
+++ b/tests/testthat/test-caret.R
@@ -94,7 +94,7 @@ test_that("Lrnr_caret RF match caret RF preds for binary classification", {
   fit_lrnr_caret_rf <- lrnr_caret_rf$train(task_binaryY)
   prd_lrnr_caret_rf <- fit_lrnr_caret_rf$predict()
   prd_lrnr_caret_rf <- as.numeric(prd_lrnr_caret_rf > 0.5)
-  
+
   ## fit caret RF using the data from the task
   set.seed(1530)
   fit_caret_rf <- suppressWarnings(caret::train(
@@ -108,7 +108,7 @@ test_that("Lrnr_caret RF match caret RF preds for binary classification", {
     predict(fit_caret_rf, newdata = task$X, type = "prob")[, 2]
   )
   prd_caret_rf <- as.numeric(prd_caret_rf > 0.5)
-  
+
   expect_equal(sum(abs(prd_lrnr_caret_rf - prd_caret_rf)), 0, tolerance = 1)
 })
 
@@ -161,6 +161,6 @@ test_that("Lrnr_caret RF preds match caret RF preds for binary regression", {
   ))
   prd_caret_rf <- as.numeric(predict(fit_caret_rf, newdata = task$X))
   rmse_classic <- sqrt(mean((prd_caret_rf - task_binaryY$Y)^2))
-  
+
   expect_equal(rmse_sl3, rmse_classic, tolerance = 0.1)
 })
diff --git a/tests/testthat/test-character_covariates.R b/tests/testthat/test-character_covariates.R
index f64f1509..b4e1914f 100644
--- a/tests/testthat/test-character_covariates.R
+++ b/tests/testthat/test-character_covariates.R
@@ -5,11 +5,11 @@ test_that("character covariates are cast to factors", {
   covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
   outcome <- "haz"
   cpp_imputed$sexn <- as.character(cpp_imputed$sexn)
-  
+
   expect_warning(
     task_character_to_factor <- make_sl3_Task(cpp_imputed, covars, outcome),
     "Character variables found: sexn;\nConverting these to factors"
   )
-  
+
   expect_equal(class(task_character_to_factor$get_node("covariates")$sexn), "factor")
 })
diff --git a/tests/testthat/test-cv_sl.R b/tests/testthat/test-cv_sl.R
index f23495f2..4f6ff5a6 100644
--- a/tests/testthat/test-cv_sl.R
+++ b/tests/testthat/test-cv_sl.R
@@ -9,9 +9,9 @@ test_that("cross-validated super learner works", {
   covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
   outcome <- "haz"
   task <- sl3_Task$new(data.table::copy(cpp_imputed),
-                       covariates = covars,
-                       outcome = outcome,
-                       folds = origami::make_folds(cpp_imputed, V = 3)
+    covariates = covars,
+    outcome = outcome,
+    folds = origami::make_folds(cpp_imputed, V = 3)
   )
   glm_learner <- Lrnr_glm$new()
   glmnet_learner <- Lrnr_pkg_SuperLearner$new("SL.glmnet")
@@ -20,6 +20,6 @@ test_that("cross-validated super learner works", {
   sl <- make_learner(Lrnr_sl, learners, glm_learner)
   sl_fit <- sl$train(task)
   cv_sl_fit <- cv_sl(sl_fit, task, loss_squared_error)
-  
+
   expect_false(any(is.na(cv_sl_fit)))
 })
diff --git a/tests/testthat/test-dbarts.R b/tests/testthat/test-dbarts.R
index c10197ff..ccd33cac 100644
--- a/tests/testthat/test-dbarts.R
+++ b/tests/testthat/test-dbarts.R
@@ -36,7 +36,7 @@ test_that("Lrnr_dbarts produces results matching those of dbarts::barts", {
   # get predictions from classic implementation
   set.seed(123)
   fit_classic <- dbarts::bart(
-    x.train = data.frame(task$X), y.train = task$Y, keeptrees = TRUE, 
+    x.train = data.frame(task$X), y.train = task$Y, keeptrees = TRUE,
     ndpost = 500, verbose = FALSE
   )
 
diff --git a/tests/testthat/test-delayed_sl3.R b/tests/testthat/test-delayed_sl3.R
index 84c5baeb..dfd7fb2a 100644
--- a/tests/testthat/test-delayed_sl3.R
+++ b/tests/testthat/test-delayed_sl3.R
@@ -5,9 +5,10 @@ plan(sequential)
 
 data(cpp_imputed)
 task <- sl3_Task$new(
-  cpp_imputed, 
+  cpp_imputed,
   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn"),
-  outcome = "haz")
+  outcome = "haz"
+)
 
 lrnr_rf <- Lrnr_randomForest$new()
 
@@ -17,7 +18,7 @@ test_that("FutureJob using delayed is reproducible", {
   sched <- Scheduler$new(test_delayed, FutureJob)
   fit_delayed <- sched$compute()
   preds_delayed <- fit_delayed$predict()
-  
+
   set.seed(123)
   options(sl3.enable.future = TRUE) # the default
   fit <- lrnr_rf$train(task)
@@ -31,7 +32,7 @@ test_that("SequentialJob is reproducible", {
   sched <- Scheduler$new(test_delayed, SequentialJob)
   fit_delayed <- sched$compute()
   preds_delayed <- fit_delayed$predict()
-  
+
   set.seed(123)
   options(sl3.enable.future = FALSE)
   fit <- lrnr_rf$train(task)
diff --git a/tests/testthat/test-density-pooled_hazards.R b/tests/testthat/test-density-pooled_hazards.R
index 0413950f..d135b97a 100644
--- a/tests/testthat/test-density-pooled_hazards.R
+++ b/tests/testthat/test-density-pooled_hazards.R
@@ -9,7 +9,7 @@ test_that("Negative log likelihood for pooled_hazards and haldensify match", {
   y <- 3 * x + epsilon
   data <- data.table(x = x, y = y)
   task <- sl3_Task$new(data, covariates = c("x"), outcome = "y")
-  
+
   # instantiate learners
   hal <- Lrnr_hal9001$new(
     lambda = exp(seq(-1, -13, length = 100)),
@@ -30,21 +30,20 @@ test_that("Negative log likelihood for pooled_hazards and haldensify match", {
     type = "equal_range",
     n_bins = 5
   )
-  
+
   # fit discrete density model to pooled hazards data
   set.seed(74294)
   fit_density <- density_learner$train(task)
   pred_density <- fit_density$predict()
-  
+
   # fit haldensify for comparison
   set.seed(74294)
   fit_haldensify <- haldensify$train(task)
   pred_haldensify <- fit_haldensify$predict()
-  
+
   # compare density estimates
   true_density <- dnorm(x = y, mean = 3 * x)
   nll_ph <- sum(-1 * true_density * log(pred_density))
   nll_haldensify <- sum(-1 * true_density * log(pred_haldensify))
   expect_equal(nll_ph, nll_haldensify, scale = nll_ph, tol = 2)
-  
 })
diff --git a/tests/testthat/test-density-semiparametric.R b/tests/testthat/test-density-semiparametric.R
index f0f373da..de81182d 100644
--- a/tests/testthat/test-density-semiparametric.R
+++ b/tests/testthat/test-density-semiparametric.R
@@ -2,54 +2,54 @@ context("test-density-semiparametric.R -- Lrnr_density_semiparametric")
 
 test_that("Lrnr_density_semiparametric works", {
   set.seed(1234)
-  
+
   # define test dataset
   n <- 1e6
   x <- runif(n, 0, 3)
   epsilon_x <- rnorm(n, 0, 0.5 + sqrt(x))
   # epsilon_x <- rnorm(n)
   y <- 3 * x + epsilon_x
-  
+
   data <- data.table(x = x, x2 = x^2, y = y)
   covariates <- c("x")
   task <- make_sl3_Task(data, covariates = covariates, outcome = "y")
-  
+
   # train
   hse_learner <- make_learner(Lrnr_density_semiparametric,
-                              mean_learner = make_learner(Lrnr_glm)
+    mean_learner = make_learner(Lrnr_glm)
   )
-  
+
   mvd_learner <- make_learner(Lrnr_density_semiparametric,
-                              mean_learner = make_learner(Lrnr_glm),
-                              var_learner = make_learner(Lrnr_glm)
+    mean_learner = make_learner(Lrnr_glm),
+    var_learner = make_learner(Lrnr_glm)
   )
-  
+
   hse_fit <- hse_learner$train(task)
   mvd_fit <- mvd_learner$train(task)
-  
+
   # test sampling
   y_samp <- mvd_fit$sample(task[1:10], 100)
-  
+
   x_grid <- seq(from = min(data$x), to = max(data$x), length = 100)
   y_grid <- seq(from = min(data$y), to = 1.5 * max(data$y), length = 100)
   pred_data <- as.data.table(expand.grid(x = x_grid, y = y_grid))
   pred_data$x2 <- pred_data$x^2
   pred_task <- make_sl3_Task(pred_data, covariates = covariates, outcome = "y")
-  
+
   pred_data$hse_preds <- hse_fit$predict(pred_task)
   pred_data$mvd_preds <- mvd_fit$predict(pred_task)
   pred_data[, true_dens := dnorm(x = y, mean = 3 * x, sd = abs(x))]
-  
+
   nll <- function(observed, pred) {
     res <- -1 * observed * log(pred)
     res[observed < .Machine$double.eps] <- 0
-    
+
     return(res)
   }
-  
+
   hse_nll <- sum(nll(pred_data$true_dens, pred_data$hse_preds))
   mvd_nll <- sum(nll(pred_data$true_dens, pred_data$mvd_preds))
-  
+
   expect_lt(hse_nll, n)
   expect_lt(mvd_nll, hse_nll)
   # long <- melt(pred_data, id = c("x", "y", "true_dens"), measure = c("hse_preds", "mvd_preds", "true_dens"))
diff --git a/tests/testthat/test-density_hse.R b/tests/testthat/test-density_hse.R
index 69e857a8..e1211772 100644
--- a/tests/testthat/test-density_hse.R
+++ b/tests/testthat/test-density_hse.R
@@ -3,24 +3,25 @@ context("test-density_hse.R -- Lrnr_density_hse")
 test_that("density_hse produces same results as density estimates from glm", {
   data(cpp_imputed)
   task <- sl3_Task$new(
-    cpp_imputed, 
-    covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-    outcome = "haz")
-  
+    cpp_imputed,
+    covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+    outcome = "haz"
+  )
+
   lrnr_density_hse <- Lrnr_density_hse$new(mean_learner = Lrnr_glm$new())
   lrnr_glm <- Lrnr_glm$new()
-  
+
   fit_density_hse <- lrnr_density_hse$train(task)
   fit_glm <- lrnr_glm$train(task)
-  
+
   # density hse
   preds_density_hse <- fit_density_hse$predict()
-  
+
   # density from glm
   mean_preds <- fit_glm$predict()
   errors <- task$Y - mean_preds
   dens_fit <- density(errors)
   preds_glm_density <- approx(dens_fit$x, dens_fit$y, errors, rule = 2)$y
-  
+
   expect_equal(preds_density_hse, preds_glm_density)
 })
diff --git a/tests/testthat/test-density_utils.R b/tests/testthat/test-density_utils.R
index 592196ce..0c9e4f4b 100644
--- a/tests/testthat/test-density_utils.R
+++ b/tests/testthat/test-density_utils.R
@@ -4,11 +4,11 @@ test_that("make_bins() works with equal range binning", {
   x <- c(0, 1, 9)
   bins <- make_bins(x, type = "equal_range", n_bins = 2)
   expect_equal(bins, c(0, 4.5, 9))
-  
+
   x <- c(-10, 1, 2, 3, 10)
   bins <- make_bins(x, type = "equal_range", n_bins = 5)
   expect_equal(bins, c(-10, -6, -2, 2, 6, 10))
-  
+
   x <- c(-10, 1, 2, 3, 9)
   bins <- make_bins(x, type = "equal_range", n_bins = 5)
   expect_equal(bins, c(-10, -6.2, -2.4, 1.4, 5.2, 9.0))
@@ -18,11 +18,11 @@ test_that("make_bins() works with equal mass binning", {
   x <- c(0, 1, 9)
   bins <- make_bins(x, type = "equal_mass", n_bins = 2)
   expect_equal(bins, c(0, 1, 9))
-  
+
   x <- c(-10, 0, 0, 10)
   bins <- make_bins(x, type = "equal_mass", n_bins = 2)
   expect_equal(bins, c(-10, 0, 10))
-  
+
   x <- c(-10, -2, -2, 1, 1, 1, 10)
   bins <- make_bins(x, type = "equal_mass", n_bins = 3)
   expect_equal(bins, c(-10, -2, 1, 10))
@@ -30,7 +30,7 @@ test_that("make_bins() works with equal mass binning", {
 
 test_that("discretize_variable() works with equal range binning", {
   x <- 1:10
-  
+
   expected_res <- list(
     x_discrete = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2),
     x_in_bin = c(0, 1, 2, 3, 4, 0.5, 1.5, 2.5, 3.5, 4.5),
@@ -43,13 +43,13 @@ test_that("discretize_variable() works with equal range binning", {
 
 test_that("discretize_variable() works with equal mass binning", {
   x <- 1:10
-  
+
   expected_res <- list(
     x_discrete = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3),
     x_in_bin = c(0, 1, 2, 0, 1, 2, 0, 1, 2, 3),
     breaks = c(1, 4, 7, 10)
   )
-  
+
   res <- discretize_variable(x, type = "equal_mass", n_bins = 3)
   expect_equal(res, expected_res)
 })
diff --git a/tests/testthat/test-ga.R b/tests/testthat/test-ga.R
index 80587e8e..178518d7 100644
--- a/tests/testthat/test-ga.R
+++ b/tests/testthat/test-ga.R
@@ -4,17 +4,17 @@ test_that("GA works as a metalearner", {
   data(cpp_imputed)
   covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
   task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
-  
+
   lasso_lrnr <- Lrnr_glmnet$new()
   glm_lrnr <- Lrnr_glm$new()
   ranger_lrnr <- Lrnr_ranger$new()
   lrnrs <- c(lasso_lrnr, glm_lrnr, ranger_lrnr)
   names(lrnrs) <- c("lasso", "glm", "ranger")
   lrnr_stack <- make_learner(Stack, lrnrs)
-  
+
   ga <- Lrnr_ga$new()
   sl <- Lrnr_sl$new(lrnr_stack, ga)
   sl_fit <- sl$train(task)
-  
+
   expect_equal(sum(sl_fit$coefficients), 1)
 })
diff --git a/tests/testthat/test-lightgbm.R b/tests/testthat/test-lightgbm.R
index a6254593..98848ea8 100644
--- a/tests/testthat/test-lightgbm.R
+++ b/tests/testthat/test-lightgbm.R
@@ -122,7 +122,8 @@ test_that("Lrnr_lightgbm predictions match lightgbm's: categorical outcome", {
   set.seed(73964)
   lrnr_lightgbm <- Lrnr_lightgbm$new(
     num_leaves = 40L, verbose = -1,
-    num_class = as.integer(length(unique(task$Y))))
+    num_class = as.integer(length(unique(task$Y)))
+  )
   fit_lrnr_lightgbm <- lrnr_lightgbm$train(task)
   prd_lrnr_lightgbm <- unpack_predictions(fit_lrnr_lightgbm$predict())
 
diff --git a/tests/testthat/test-mean.R b/tests/testthat/test-mean.R
index 5375935f..c40f4c0f 100644
--- a/tests/testthat/test-mean.R
+++ b/tests/testthat/test-mean.R
@@ -4,10 +4,10 @@ test_that("Lrnr_mean predictions are the same as simple means", {
   data(cpp_imputed)
   covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
   task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
-  
+
   lrnr_mean <- make_learner(Lrnr_mean)
   mean_fit <- lrnr_mean$train(task)
   mean_preds <- mean_fit$predict()
-  
+
   expect_equal(mean_preds, rep(mean(task$Y), nrow(cpp_imputed)))
 })
diff --git a/tests/testthat/test-nnet.R b/tests/testthat/test-nnet.R
index e30487f3..e22ce5ec 100644
--- a/tests/testthat/test-nnet.R
+++ b/tests/testthat/test-nnet.R
@@ -8,8 +8,10 @@ outcome <- "haz"
 task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
 
 test_that("Lrnr_nnet gives the correct output for non-binomial outcome", {
-  lrnr_nnet <- Lrnr_nnet$new(linout = TRUE, size = 10, maxit = 1000, 
-                             trace = FALSE)
+  lrnr_nnet <- Lrnr_nnet$new(
+    linout = TRUE, size = 10, maxit = 1000,
+    trace = FALSE
+  )
   fit <- lrnr_nnet$train(task)
   preds <- fit$predict(task)
   expect_equal(task$nrow, length(preds))
@@ -19,8 +21,10 @@ test_that("Lrnr_nnet gives the correct output for non-binomial outcome", {
 test_that("Lrnr_nnet gives the correct output for binomial outcome", {
   covars <- c("bmi", "parity", "mage")
   task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = "sexn")
-  lrnr_nnet <- Lrnr_nnet$new(linout = TRUE, size = 10, maxit = 1000, 
-                             trace = FALSE)
+  lrnr_nnet <- Lrnr_nnet$new(
+    linout = TRUE, size = 10, maxit = 1000,
+    trace = FALSE
+  )
   fit <- lrnr_nnet$train(task)
   preds <- fit$predict(task)
   expect_equal(task$nrow, length(preds))
diff --git a/tests/testthat/test-offset.R b/tests/testthat/test-offset.R
index f4761234..e805a679 100644
--- a/tests/testthat/test-offset.R
+++ b/tests/testthat/test-offset.R
@@ -48,17 +48,17 @@ newoffset_task <- sl3_Task$new(
 # specifically test lrnr_glm against base glm
 test_that("Lrnr_glm_fast produces same results as base glm with offset", {
   lrnr_glm <- make_learner(Lrnr_glm_fast)
-  
+
   fit <- lrnr_glm$train(task)
   offset_fit <- lrnr_glm$train(offset_task)
-  
+
   preds <- fit$predict()
   offset_preds <- offset_fit$predict()
   expect_false(isTRUE(all.equal(preds, offset_preds)))
-  
+
   glm_fit <- glm(A ~ W1 + W2 + W3 + W4, data, family = binomial())
   expect_equivalent(coef(glm_fit), coef(fit))
-  
+
   glm_offset_fit <- glm(
     A ~ W1 + W2 + W3 + W4, data,
     family = binomial(),
@@ -106,7 +106,7 @@ test_that("Offset works for learners that support it", {
     "Lrnr_glm", "Lrnr_glm_fast",
     "Lrnr_mean", "Lrnr_xgboost"
   )
-  
+
   offset_learners <- offset_learner_stack$params$learners
   lapply(
     offset_learners, test_learner_offset_support,
diff --git a/tests/testthat/test-pipeline.R b/tests/testthat/test-pipeline.R
index a52b324a..dffa13f4 100644
--- a/tests/testthat/test-pipeline.R
+++ b/tests/testthat/test-pipeline.R
@@ -15,13 +15,17 @@ test_screen_pipe <- function(screen_name_SuperLearner) {
   screen_learner <- Lrnr_pkg_SuperLearner_screener$new(screen_name_SuperLearner)
   screen_glm <- make_learner(Pipeline, screen_learner, glm_learner)
   fit <- screen_glm$train(task)
-  
-  expect_equal(fit$fit_object$learner_fits[[1]]$fit_object$selected,
-               names(fit$fit_object$learner_fits$Lrnr_glm_TRUE$coefficients)[-1])
+
+  expect_equal(
+    fit$fit_object$learner_fits[[1]]$fit_object$selected,
+    names(fit$fit_object$learner_fits$Lrnr_glm_TRUE$coefficients)[-1]
+  )
 }
 
 test_that("Pipeline pipes selected covariates from screening algorithms", {
-  screens <- c("screen.glmnet", "screen.corP", "screen.corRank", 
-               "screen.randomForest", "screen.SIS")
+  screens <- c(
+    "screen.glmnet", "screen.corP", "screen.corRank",
+    "screen.randomForest", "screen.SIS"
+  )
   lapply(screens, test_screen_pipe)
 })
diff --git a/tests/testthat/test-randomForest.R b/tests/testthat/test-randomForest.R
index e71a988c..eaaf2ab5 100644
--- a/tests/testthat/test-randomForest.R
+++ b/tests/testthat/test-randomForest.R
@@ -4,21 +4,23 @@ test_that("Lrnr_randomForest predictions are the same as original package", {
   data(cpp_imputed)
   covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
   task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
-  
+
   lrnr_rf <- make_learner(Lrnr_randomForest)
   set.seed(123)
   lrnr_rf_fit <- lrnr_rf$train(task)
   sl3_preds <- as.numeric(lrnr_rf_fit$predict())
   sl3_mse <- mean((task$Y - sl3_preds)^2)
-  
+
   set.seed(123)
-  rf_fit <- randomForest(x = task$X, y = task$Y, 
-                         ntree = lrnr_rf$params$ntree,
-                         keep.forest = lrnr_rf$params$keep.forest,
-                         nodesize = lrnr_rf$params$keep.forest,
-                         mtry = floor(ncol(task$X)))
+  rf_fit <- randomForest(
+    x = task$X, y = task$Y,
+    ntree = lrnr_rf$params$ntree,
+    keep.forest = lrnr_rf$params$keep.forest,
+    nodesize = lrnr_rf$params$keep.forest,
+    mtry = floor(ncol(task$X))
+  )
   rf_preds <- as.numeric(predict(rf_fit, task$data))
   classic_mse <- mean((task$Y - rf_preds)^2)
-  
+
   expect_equal(sl3_mse, classic_mse, tolerance = 0.05)
 })
diff --git a/tests/testthat/test-ranger.R b/tests/testthat/test-ranger.R
index 485fa1b8..dd80d224 100644
--- a/tests/testthat/test-ranger.R
+++ b/tests/testthat/test-ranger.R
@@ -62,13 +62,14 @@ test_that("Lrnr_ranger predictions match those from ranger", {
   prd_lrnr_ranger <- fit_lrnr_ranger$predict()
 
   ## fit ranger using the data from the task
-  fit_ranger <- ranger(mpg ~ ., 
-                       num.trees = lrnr_ranger$params$num.trees,
-                       write.forest = lrnr_ranger$params$write.forest,
-                       importance = lrnr_ranger$params$importance,
-                       num.threads = lrnr_ranger$params$num.threads,
-                       seed = 123,
-                       data = task$data)
+  fit_ranger <- ranger(mpg ~ .,
+    num.trees = lrnr_ranger$params$num.trees,
+    write.forest = lrnr_ranger$params$write.forest,
+    importance = lrnr_ranger$params$importance,
+    num.threads = lrnr_ranger$params$num.threads,
+    seed = 123,
+    data = task$data
+  )
   prd_ranger <- predict(fit_ranger, data = task$data)[[1]]
 
   ## test equivalence of prediction from Lrnr_ranger and ranger::ranger
diff --git a/tests/testthat/test-rugarch.R b/tests/testthat/test-rugarch.R
index 0fd9dbde..b597aa24 100644
--- a/tests/testthat/test-rugarch.R
+++ b/tests/testthat/test-rugarch.R
@@ -4,13 +4,13 @@ library(rugarch)
 
 test_that("Lrnr_rugarch predictions are the same as classic implementation", {
   data(bsds)
-  
+
   # make folds appropriate for time-series cross-validation
   folds <- make_folds(bsds,
-                      fold_fun = folds_rolling_window, window_size = 500,
-                      validation_size = 100, gap = 0, batch = 50
+    fold_fun = folds_rolling_window, window_size = 500,
+    validation_size = 100, gap = 0, batch = 50
   )
-  
+
   # build task by passing in external folds structure
   task <- sl3_Task$new(
     data = bsds,
@@ -20,22 +20,26 @@ test_that("Lrnr_rugarch predictions are the same as classic implementation", {
     ),
     outcome = "cnt"
   )
-  
+
   # create tasks for training and validation
   train_task <- training(task, fold = task$folds[[1]])
   valid_task <- validation(task, fold = task$folds[[1]])
-  
+
   lrnr_rugarch <- Lrnr_rugarch$new()
-  
+
   # sl3 implementation
   fit_sl3 <- lrnr_rugarch$train(train_task)
   pred_sl3 <- fit_sl3$predict(valid_task)
-  
+
   # classical implementation
   fit_classic <- rugarch::ugarchfit(ugarchspec(), train_task$X)
-  pred_classic <- ugarchforecast(fit_classic, data = valid_task$X, 
-                                 n.ahead = ts_get_pred_horizon(train_task, 
-                                                               valid_task))
+  pred_classic <- ugarchforecast(fit_classic,
+    data = valid_task$X,
+    n.ahead = ts_get_pred_horizon(
+      train_task,
+      valid_task
+    )
+  )
   pred_classic <- as.numeric(pred_classic@forecast$seriesFor)
   expect_equal(pred_sl3, pred_classic)
 })
diff --git a/tests/testthat/test-sl-timeseries.R b/tests/testthat/test-sl-timeseries.R
index bc5bd9bf..1469da28 100644
--- a/tests/testthat/test-sl-timeseries.R
+++ b/tests/testthat/test-sl-timeseries.R
@@ -12,35 +12,32 @@ test_that("validation set for time-series is as expected", {
   covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
   outcome <- "haz"
   data <- cpp_imputed[1:100, ]
-  
+
   folds <- origami::make_folds(data,
-                               fold_fun = folds_rolling_window, window_size = 25,
-                               validation_size = 25, gap = 0, batch = 10
+    fold_fun = folds_rolling_window, window_size = 25,
+    validation_size = 25, gap = 0, batch = 10
   )
   task <- make_sl3_Task(
     data = data,
     covariates = covars,
     outcome = outcome, folds = folds
   )
-  
+
   lrnr_glm <- make_learner(Lrnr_glm)
   lrnr_mean <- make_learner(Lrnr_mean)
   lrnr_glmnet <- make_learner(Lrnr_glmnet)
-  
+
   stack <- make_learner(
     Stack,
     lrnr_glm, lrnr_mean, lrnr_glmnet
   )
   metalearner <- make_learner(Lrnr_nnls)
-  
+
   sl <- make_learner(Lrnr_sl,
-                     learners = stack,
-                     metalearner = metalearner
+    learners = stack,
+    metalearner = metalearner
   )
   sl_fit <- sl$train(task)
   preds <- sl_fit$predict_fold(task, "validation")
   expect_equal(length(preds), 150)
 })
-          
-          
-         
diff --git a/tests/testthat/test-sl_fold.R b/tests/testthat/test-sl_fold.R
index 5e15814d..29adaa03 100644
--- a/tests/testthat/test-sl_fold.R
+++ b/tests/testthat/test-sl_fold.R
@@ -10,20 +10,20 @@ test_that("sl prediction for each fold works", {
   covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
   outcome <- "haz"
   task <- sl3_Task$new(data.table::copy(cpp_imputed), covariates = covars, outcome = outcome)
-  
+
   glm_learner <- Lrnr_glm$new()
   glmnet_learner <- Lrnr_pkg_SuperLearner$new("SL.glmnet")
   subset_apgar <- Lrnr_subset_covariates$new(covariates = c("apgar1", "apgar5"))
   learners <- list(glm_learner, glmnet_learner, subset_apgar)
   sl1 <- make_learner(Lrnr_sl, learners, glm_learner)
-  
+
   sl_fit <- sl1$train(task)
-  
+
   fold1_predict <- sl_fit$predict_fold(task, 1)
   validation_predict <- sl_fit$predict_fold(task, "validation")
   expect_false(all(fold1_predict == validation_predict))
   expect_true(any(fold1_predict == validation_predict))
-  
+
   glm_fit <- glm_learner$train(task)
   expect_warning(glm_fold1_predict <- glm_fit$predict_fold(task, 1))
 })