diff --git a/.Rbuildignore b/.Rbuildignore
index 7791a8d..65fea1a 100755
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -2,4 +2,6 @@
 ^\.Rproj\.user$
 ^\.travis
 ^README\.Rmd$
-^\.git$
\ No newline at end of file
+^\.git$
+^\.github$
+^cran-comments\.md$
diff --git a/.github/.gitignore b/.github/.gitignore
new file mode 100644
index 0000000..2d19fc7
--- /dev/null
+++ b/.github/.gitignore
@@ -0,0 +1 @@
+*.html
diff --git a/.github/workflows/R-CMD-check.yml b/.github/workflows/R-CMD-check.yml
new file mode 100644
index 0000000..ce702bd
--- /dev/null
+++ b/.github/workflows/R-CMD-check.yml
@@ -0,0 +1,83 @@
+on:
+  push:
+    branches:
+      - master
+      - devel
+  pull_request:
+    branches:
+      - master
+  schedule:
+    - cron: "0 0 * * 1"
+
+name: R-CMD-check
+
+jobs:
+  R-CMD-check:
+    runs-on: ${{ matrix.config.os }}
+    if: "!contains(github.event.head_commit.message, 'ci skip')"
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          #- {os: windows-latest, r: 'release'}
+          #- {os: macOS-latest, r: 'release'}
+          - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04, r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+
+    env:
+      R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
+      RSPM: ${{ matrix.config.rspm }}
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - uses: r-lib/actions/setup-r@master
+        with:
+          r-version: ${{ matrix.config.r }}
+
+      - uses: r-lib/actions/setup-pandoc@master
+
+      - name: Query dependencies
+        run: |
+          install.packages('remotes')
+          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
+          writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
+        shell: Rscript {0}
+
+      - name: Restore R package cache
+        if: runner.os != 'Windows'
+        uses: actions/cache@v2
+        with:
+          path: ${{ env.R_LIBS_USER }}
+          key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
+          restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-
+
+      - name: Install system dependencies
+        if: runner.os == 'Linux'
+        run: |
+          while read -r cmd
+          do
+            eval sudo $cmd
+          done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))')
+
+      - name: Install dependencies
+        run: |
+          remotes::install_deps(dependencies = TRUE)
+          remotes::install_cran("rcmdcheck")
+        shell: Rscript {0}
+
+      - name: Check
+        env:
+          _R_CHECK_CRAN_INCOMING_REMOTE_: false
+        run: rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check")
+        shell: Rscript {0}
+
+      - name: Upload check results
+        if: failure()
+        uses: actions/upload-artifact@master
+        with:
+          name: ${{ runner.os }}-r${{ matrix.config.r }}-results
+          path: check
+
diff --git a/.github/workflows/test_coverage.yml b/.github/workflows/test_coverage.yml
new file mode 100644
index 0000000..6ee47ce
--- /dev/null
+++ b/.github/workflows/test_coverage.yml
@@ -0,0 +1,54 @@
+on:
+  push:
+    branches:
+      - master
+      - devel
+  pull_request:
+    branches:
+      - master
+
+name: test-coverage
+
+jobs:
+  test-coverage:
+    runs-on: ubuntu-latest
+    if: "!contains(github.event.head_commit.message, 'ci skip')"
+    steps:
+      - uses: actions/checkout@v2
+
+      - uses: r-lib/actions/setup-r@master
+
+      - uses: r-lib/actions/setup-pandoc@master
+
+      - name: Query dependencies
+        run: |
+          install.packages('remotes')
+          saveRDS(remotes::dev_package_deps(dependencies = TRUE), "depends.Rds", version = 2)
+        shell: Rscript {0}
+
+      - name: Cache R packages
+        uses: actions/cache@v1
+        with:
+          path: ${{ env.R_LIBS_USER }}
+          key: ubuntu-r-release-${{ hashFiles('depends.Rds') }}
+          restore-keys: ubuntu-r-release-
+
+      - name: Install system dependencies
+        if: runner.os == 'Linux'
+        run: |
+          while read -r cmd
+          do
+            eval sudo $cmd
+          done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))')
+
+      - name: Install dependencies
+        run: |
+          install.packages(c("remotes"))
+          remotes::install_deps(dependencies = TRUE)
+          remotes::install_cran("covr")
+        shell: Rscript {0}
+
+      - name: Test coverage
+        run: covr::codecov()
+        shell: Rscript {0}
+
diff --git a/DESCRIPTION b/DESCRIPTION
index d89ff55..d554431 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,14 +1,22 @@
 Package: dynfeature
 Type: Package
-Title: Dynamic feature importance
-Version: 1.0.0.9000
-Authors@R: c(
-  person("Robrecht", "Cannoodt", email = "rcannood@gmail.com", role = c("aut")),
-  person("Wouter", "Saelens", email = "wouter.saelens@ugent.be", role = c("aut", "cre")))
-Description: Calculating feature importance scores from trajectories
+Title: Feature Importance for Dynamic Processes
+Version: 1.0.0
+Authors@R: 
+    c(person(given = "Robrecht",
+             family = "Cannoodt",
+             role = c("aut", "cre"),
+             email = "rcannood@gmail.com",
+             comment = c(ORCID = "0000-0003-3641-729X", github = "rcannood")),
+      person(given = "Wouter",
+             family = "Saelens",
+             role = "aut",
+             email = "wouter.saelens@gmail.com",
+             comment = c(ORCID = "0000-0002-7114-6248", github = "zouter")))
+Description: Calculating feature importance scores from trajectories using the random forests algorithm and more. Saelens and Cannoodt et
+    al. (2019) <doi:10.1038/s41587-019-0071-9>.
 License: GPL-3
 Encoding: UTF-8
-LazyData: true
 Imports:
   dplyr,
   dynutils (>= 1.0.2),
@@ -22,19 +30,7 @@ Imports:
   tidyr,
   tibble
 Suggests:
-  caret
-RoxygenNote: 6.1.1
+  caret,
+  covr
+RoxygenNote: 7.1.1
 Roxygen: list(markdown = TRUE)
-Remotes: 
-    dynverse/dynutils@devel,
-    dynverse/dynwrap@devel
-Collate: 
-    'calculate_feature_importances.R'
-    'calculate_branch_feature_importance.R'
-    'calculate_branching_point_feature_importance.R'
-    'calculate_cell_feature_importance.R'
-    'calculate_milestone_feature_importance.R'
-    'calculate_overall_feature_importance.R'
-    'calculate_waypoint_feature_importance.R'
-    'fi_methods.R'
-    'package.R'
diff --git a/NAMESPACE b/NAMESPACE
index dbba772..8d78b31 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -9,11 +9,11 @@ export(calculate_waypoint_feature_importance)
 export(fi_caret)
 export(fi_ranger_rf)
 export(fi_ranger_rf_lite)
+export(fi_ranger_rf_tiny)
 import(dplyr)
 import(dynutils)
 import(dynwrap)
 import(methods)
-import(tibble)
 import(tidyr)
 importFrom(purrr,"%>%")
 importFrom(purrr,invoke)
@@ -28,3 +28,6 @@ importFrom(purrr,map_lgl)
 importFrom(purrr,set_names)
 importFrom(ranger,ranger)
 importFrom(reshape2,acast)
+importFrom(tibble,as_tibble)
+importFrom(tibble,tibble)
+importFrom(tibble,tribble)
diff --git a/inst/NEWS.md b/NEWS.md
similarity index 89%
rename from inst/NEWS.md
rename to NEWS.md
index 5c5858a..f7f58e8 100644
--- a/inst/NEWS.md
+++ b/NEWS.md
@@ -1,8 +1,12 @@
-# dynfeature 1.0.0 (28-03-2019)
+# dynfeature 1.0.0
+
+Initial release of dynfeature on CRAN.
 
 * MINOR CHANGE: Use only one core by default.
 
-* MINOR CHANGE: Support sparse matrices
+* MINOR CHANGE: Support sparse matrices.
+
+* DOCUMENTATION: Add examples and returns.
 
 # dynfeature 0.2.0 (25-10-2018)
 
diff --git a/R/calculate_branch_feature_importance.R b/R/calculate_branch_feature_importance.R
index 0a07c6f..008e21d 100644
--- a/R/calculate_branch_feature_importance.R
+++ b/R/calculate_branch_feature_importance.R
@@ -1,52 +1,46 @@
 #' @rdname calculate_overall_feature_importance
 #'
 #' @export
-#'
-#' @include calculate_feature_importances.R
-calculate_branch_feature_importance <- inherit_default_params(
-  super_functions = list(calculate_feature_importances),
-  fun = function(
-    trajectory,
-    expression_source = "expression",
-    fi_method,
-    verbose
-  ) {
-    # assign name to each edge
-    milestone_network <-
-      trajectory$milestone_network %>%
-      mutate(edge_id = as.character(row_number())) %>%
-      select(from, to, edge_id)
+calculate_branch_feature_importance <- function(
+  trajectory,
+  expression_source = "expression",
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+) {
+  # assign name to each edge
+  milestone_network <-
+    trajectory$milestone_network %>%
+    mutate(edge_id = as.character(row_number())) %>%
+    select(.data$from, .data$to, .data$edge_id)
 
-    # determine which cell is part of which edge
-    edge_membership <-
-      trajectory$progressions %>%
-      group_by(cell_id) %>%
-      top_n(1, percentage) %>%
-      ungroup() %>%
-      left_join(milestone_network, c("from", "to")) %>%
-      reshape2::acast(cell_id ~ edge_id, value.var = "percentage") %>%
-      {!is.na(.)}
+  # determine which cell is part of which edge
+  edge_membership <-
+    trajectory$progressions %>%
+    group_by(.data$cell_id) %>%
+    top_n(1, .data$percentage) %>%
+    ungroup() %>%
+    left_join(milestone_network, c("from", "to")) %>%
+    mutate(contains = TRUE) %>%
+    reshape2::acast(cell_id ~ edge_id, value.var = "contains", fill = FALSE)
 
-    expression <- get_expression(trajectory, expression_source)
+  expression <- get_expression(trajectory, expression_source)
 
+  out <- calculate_feature_importances(
+    X = expression,
+    Y = edge_membership,
+    fi_method = fi_method,
+    verbose = verbose
+  )
+  suppressWarnings({
+    out <- out %>%
+      left_join(milestone_network, c("predictor_id" = "edge_id"))
+  })
 
-    out <- calculate_feature_importances(
-      X = expression,
-      Y = edge_membership,
-      fi_method = fi_method,
-      verbose = verbose
+  out %>%
+    transmute(
+      .data$feature_id,
+      from = factor(.data$from, trajectory$milestone_ids),
+      to = factor(.data$to, trajectory$milestone_ids),
+      .data$importance
     )
-    suppressWarnings({
-      out <- out %>%
-        left_join(milestone_network, c("predictor_id" = "edge_id"))
-    })
-
-    out %>%
-      transmute(
-        feature_id,
-        from = factor(from, trajectory$milestone_ids),
-        to = factor(to, trajectory$milestone_ids),
-        importance
-      )
-  }
-)
\ No newline at end of file
+}
\ No newline at end of file
diff --git a/R/calculate_branching_point_feature_importance.R b/R/calculate_branching_point_feature_importance.R
index 06833cc..b55b152 100644
--- a/R/calculate_branching_point_feature_importance.R
+++ b/R/calculate_branching_point_feature_importance.R
@@ -1,70 +1,65 @@
 #' @rdname calculate_overall_feature_importance
 #'
 #' @export
-#'
-#' @include calculate_feature_importances.R
-calculate_branching_point_feature_importance <- inherit_default_params(
-  super_functions = list(calculate_feature_importances),
-  fun = function(
-    trajectory,
-    expression_source = "expression",
-    milestones_oi = trajectory$milestone_ids,
-    fi_method,
-    verbose
-  ) {
-    # assign name to each edge
-    milestone_network <-
-      trajectory$milestone_network %>%
-      mutate(edge_id = as.character(row_number())) %>%
-      select(from, to, edge_id)
+calculate_branching_point_feature_importance <- function(
+  trajectory,
+  expression_source = "expression",
+  milestones_oi = trajectory$milestone_ids,
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+) {
+  # assign name to each edge
+  milestone_network <-
+    trajectory$milestone_network %>%
+    mutate(edge_id = as.character(row_number())) %>%
+    select(.data$from, .data$to, .data$edge_id)
 
-    # determine which cell is part of which edge
-    edge_membership <- trajectory$progressions %>%
-      group_by(cell_id) %>%
-      top_n(1, percentage) %>%
-      ungroup() %>%
-      left_join(milestone_network, c("from", "to")) %>%
-      reshape2::acast(cell_id~edge_id, value.var="percentage") %>%
-      {!is.na(.)}
+  # determine which cell is part of which edge
+  edge_membership <- trajectory$progressions %>%
+    group_by(.data$cell_id) %>%
+    top_n(1, .data$percentage) %>%
+    ungroup() %>%
+    left_join(milestone_network, c("from", "to")) %>%
+    mutate(contains = TRUE) %>%
+    reshape2::acast(cell_id~edge_id, value.var = "contains", fill = FALSE)
 
-    expression <- get_expression(trajectory, expression_source)
+  expression <- get_expression(trajectory, expression_source)
 
-    map_df(
-      seq_along(milestones_oi),
-      function(i) {
-        if (verbose) cat("Processing milestone ", i , "/", length(milestones_oi), "\n", sep = "")
+  map_df(
+    seq_along(milestones_oi),
+    function(i) {
+      if (verbose) cat("Processing milestone ", i , "/", length(milestones_oi), "\n", sep = "")
 
-        milestone_oi <- milestones_oi[[i]]
+      milestone_oi <- milestones_oi[[i]]
 
-        # select the cells which are close to the milestone
-        prog <-
-          trajectory$progressions %>%
-          filter(from == milestone_oi | to == milestone_oi) %>%
-          mutate(milestone_other = ifelse(from == milestone_oi, to, from)) %>%
-          group_by(cell_id) %>%
-          top_n(1, percentage) %>%
-          ungroup()
+      # select the cells which are close to the milestone
+      prog <-
+        trajectory$progressions %>%
+        filter(.data$from == milestone_oi | .data$to == milestone_oi) %>%
+        mutate(milestone_other = ifelse(.data$from == milestone_oi, .data$to, .data$from)) %>%
+        group_by(.data$cell_id) %>%
+        top_n(1, .data$percentage) %>%
+        ungroup()
 
-        expression_oi <- expression[prog$cell_id, ]
-        outcome <- as.character(prog$milestone_other)
+      expression_oi <- expression[prog$cell_id, ]
+      outcome <- as.character(prog$milestone_other)
 
-        if(length(unique(prog$milestone_other)) < 2) {
-          warning("Could not find features specific for milestone ", milestone_oi)
-          tibble()
-        } else {
-          calculate_feature_importances(
-            X = expression_oi,
-            Y = outcome,
-            fi_method = fi_method,
-            verbose = verbose
-          ) %>%
-            transmute(
-              milestone_id = factor(milestone_oi, levels = trajectory$milestone_ids),
-              feature_id,
-              importance
-            )
-        }
+      if(length(unique(prog$milestone_other)) < 2) {
+        warning("Could not find features specific for milestone ", milestone_oi)
+        tibble()
+      } else {
+        calculate_feature_importances(
+          X = expression_oi,
+          Y = outcome,
+          fi_method = fi_method,
+          verbose = verbose
+        ) %>%
+          transmute(
+            milestone_id = factor(milestone_oi, levels = trajectory$milestone_ids),
+            .data$feature_id,
+            .data$importance
+          )
       }
-    )
-  }
-)
\ No newline at end of file
+    }
+  )
+}
\ No newline at end of file
diff --git a/R/calculate_cell_feature_importance.R b/R/calculate_cell_feature_importance.R
index 10ceefc..3679029 100644
--- a/R/calculate_cell_feature_importance.R
+++ b/R/calculate_cell_feature_importance.R
@@ -1,44 +1,38 @@
 #' @rdname calculate_overall_feature_importance
 #'
 #' @export
-#'
-#' @include calculate_feature_importances.R
-calculate_cell_feature_importance <- inherit_default_params(
-  super_functions = list(calculate_feature_importances),
-  fun = function(
-    trajectory,
-    expression_source = "expression",
-    fi_method,
-    verbose
-  ) {
-    if (!is_wrapper_with_waypoints(trajectory)) {
-      trajectory <- trajectory %>% dynwrap::add_waypoints()
-    }
+calculate_cell_feature_importance <- function(
+  trajectory,
+  expression_source = "expression",
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+) {
+  if (!is_wrapper_with_waypoints(trajectory)) {
+    trajectory <- trajectory %>% dynwrap::add_waypoints()
+  }
 
-    waypoints <- trajectory$waypoints
+  waypoints <- trajectory$waypoints
 
-    waypoint_feature_importances <-
-      calculate_waypoint_feature_importance(
-        trajectory = trajectory,
-        expression_source = expression_source,
-        waypoints = waypoints,
-        fi_method = fi_method,
-        verbose = verbose
-      )
+  waypoint_feature_importances <-
+    calculate_waypoint_feature_importance(
+      trajectory = trajectory,
+      expression_source = expression_source,
+      waypoints = waypoints,
+      fi_method = fi_method,
+      verbose = verbose
+    )
 
-    closest_waypoints <-
-      trajectory$waypoints$geodesic_distances %>% {
-        tibble(
-          cell_id = factor(colnames(.), levels = colnames(.)),
-          waypoint_id = factor(rownames(.)[apply(., 2, which.min)], levels = waypoints$waypoints$waypoint_id %>% sort)
-        )
-      }
+  geo <- trajectory$waypoints$geodesic_distances
 
-    full_join(
-      closest_waypoints,
-      waypoint_feature_importances,
-      by = "waypoint_id"
-    ) %>%
-      select(-waypoint_id)
-  }
-)
\ No newline at end of file
+  closest_waypoints <- tibble(
+    cell_id = factor(colnames(geo), levels = colnames(geo)),
+    waypoint_id = factor(rownames(geo)[apply(geo, 2, which.min)], levels = waypoints$waypoints$waypoint_id %>% sort)
+  )
+
+  full_join(
+    closest_waypoints,
+    waypoint_feature_importances,
+    by = "waypoint_id"
+  ) %>%
+    select(-.data$waypoint_id)
+}
\ No newline at end of file
diff --git a/R/calculate_feature_importances.R b/R/calculate_feature_importances.R
index 9836c56..5123a7c 100644
--- a/R/calculate_feature_importances.R
+++ b/R/calculate_feature_importances.R
@@ -4,14 +4,28 @@
 #' @param Y A data frame of predictor variables, with `nrow(Y) == nrow(X)`.
 #' @param fi_method A feature importance method. Default: `fi_ranger_rf_lite()`. Check `?fi_methods` for a full list of available feature importance methods.
 #' @param verbose Whether to print out extra information.
-calculate_feature_importances <- function(X, Y, fi_method = fi_ranger_rf_lite(), verbose = FALSE) {
+#'
+#' @returns A data frame with three columns, `predictor_id`, `feature_id`, and `importance`. `predictor_id` is a column in `Y`, while `feature_id` is a column in `X`.
+#'
+#' @examples
+#' X <- data.frame(matrix(runif(25*10), ncol = 10))
+#' Y <- data.frame(matrix(runif(25*2), ncol = 2))
+#'
+#' # don't run since this function is not exported
+#' # calculate_feature_importances(X, Y)
+calculate_feature_importances <- function(
+  X,
+  Y,
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+) {
   # if Y is a vector or a matrix, turn it into a data frame
   if (!is.data.frame(Y)) {
     # convert to regular matrix if sparse
     if (dynutils::is_sparse(Y)) {
       Y <- as.matrix(Y)
     }
-    Y <- as_data_frame(Y)
+    Y <- as_tibble(Y, .name_repair = "minimal")
   }
 
   # convert expression to regular matrix if sparse
@@ -36,7 +50,7 @@ calculate_feature_importances <- function(X, Y, fi_method = fi_ranger_rf_lite(),
       importance <- fi_method$fun(X, y, verbose = verbose)
     }
 
-    data_frame(
+    tibble(
       predictor_id = factor(colnames(Y)[[i]], levels = colnames(Y)),
       feature_id = factor(names(importance), levels = names(importance)),
       importance
@@ -45,5 +59,5 @@ calculate_feature_importances <- function(X, Y, fi_method = fi_ranger_rf_lite(),
 
   # return importances ordered by value
   importances %>%
-    arrange(desc(importance))
+    arrange(desc(.data$importance))
 }
\ No newline at end of file
diff --git a/R/calculate_milestone_feature_importance.R b/R/calculate_milestone_feature_importance.R
index d362bdb..8c2f178 100644
--- a/R/calculate_milestone_feature_importance.R
+++ b/R/calculate_milestone_feature_importance.R
@@ -1,50 +1,45 @@
 #' @rdname calculate_overall_feature_importance
 #'
 #' @export
-#'
-#' @include calculate_feature_importances.R
-calculate_milestone_feature_importance <- inherit_default_params(
-  super_functions = list(calculate_feature_importances),
-  fun = function(
-    trajectory,
-    expression_source = "expression",
-    milestones_oi = NULL,
-    fi_method,
-    verbose
-  ) {
-    # get expression from trajectory source or expression source
-    expression <- get_expression(trajectory, expression_source)
+calculate_milestone_feature_importance <- function(
+  trajectory,
+  expression_source = "expression",
+  milestones_oi = NULL,
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+) {
+  # get expression from trajectory source or expression source
+  expression <- get_expression(trajectory, expression_source)
 
-    # check trajectory and extract some variables
-    testthat::expect_true(dynwrap::is_wrapper_with_trajectory(trajectory))
-    milestone_percentages <- trajectory$milestone_percentages
-    cell_ids <- trajectory$cell_ids
+  # check trajectory and extract some variables
+  testthat::expect_true(dynwrap::is_wrapper_with_trajectory(trajectory))
+  milestone_percentages <- trajectory$milestone_percentages
+  cell_ids <- trajectory$cell_ids
 
-    testthat::expect_true(all(cell_ids %in% rownames(expression)))
-    testthat::expect_true(length(cell_ids) >= 3, info = "Need 3 or more cells in a trajectory to determine important features")
+  testthat::expect_true(all(cell_ids %in% rownames(expression)))
+  testthat::expect_true(length(cell_ids) >= 3, info = "Need 3 or more cells in a trajectory to determine important features")
 
-    # process milestones
-    if (is.null(milestones_oi)) {
-      milestones_oi <- trajectory$milestone_ids
-    }
+  # process milestones
+  if (is.null(milestones_oi)) {
+    milestones_oi <- trajectory$milestone_ids
+  }
 
-    # construct milestone percentages matrix:
-    # * only retain relevant milestones
-    # * transform from long format to matrix
-    # * if cells were filtered out, add them back to the matrix
-    milenet_m <-
-      milestone_percentages %>%
-      filter(milestone_id %in% milestones_oi) %>%
-      reshape2::acast(cell_id ~ milestone_id, value.var = "percentage", fill = 0) %>%
-      expand_matrix(rownames = cell_ids)
+  # construct milestone percentages matrix:
+  # * only retain relevant milestones
+  # * transform from long format to matrix
+  # * if cells were filtered out, add them back to the matrix
+  milenet_m <-
+    milestone_percentages %>%
+    filter(.data$milestone_id %in% milestones_oi) %>%
+    reshape2::acast(cell_id ~ milestone_id, value.var = "percentage", fill = 0) %>%
+    expand_matrix(rownames = cell_ids)
 
-    # calculate feature importance scores
-    calculate_feature_importances(
-      X = expression,
-      Y = milenet_m,
-      fi_method = fi_method,
-      verbose = verbose
-    ) %>%
-      rename(milestone_id = predictor_id)
-  }
-)
\ No newline at end of file
+  # calculate feature importance scores
+  calculate_feature_importances(
+    X = expression,
+    Y = milenet_m,
+    fi_method = fi_method,
+    verbose = verbose
+  ) %>%
+    rename(milestone_id = .data$predictor_id)
+}
\ No newline at end of file
diff --git a/R/calculate_overall_feature_importance.R b/R/calculate_overall_feature_importance.R
index baa0e90..77f2fb4 100644
--- a/R/calculate_overall_feature_importance.R
+++ b/R/calculate_overall_feature_importance.R
@@ -12,6 +12,8 @@
 #' @param milestones_oi The milestone(s) for which to calculate feature importance
 #' @param waypoints The waypoints, optional
 #'
+#' @returns A data frame with two or more columns, `feature_id`, and `importance`. `feature_id` is a column in the trajectory expression matrix. Additional columns may be available depending on the function called.
+#'
 #' @inheritParams calculate_feature_importances
 #'
 #' @importFrom reshape2 acast
@@ -19,23 +21,24 @@
 #'
 #' @export
 #'
-#' @include calculate_feature_importances.R
-calculate_overall_feature_importance <- inherit_default_params(
-  super_functions = list(calculate_feature_importances),
-  fun = function(
-    trajectory,
-    expression_source = "expression",
-    fi_method,
-    verbose
-  ) {
-    calculate_milestone_feature_importance(
-      trajectory = trajectory,
-      expression_source = expression_source,
-      fi_method = fi_method,
-      verbose = verbose
-    ) %>%
-      group_by(feature_id) %>%
-      summarise(importance = mean(importance)) %>%
-      arrange(desc(importance))
-  }
-)
\ No newline at end of file
+#' @examples
+#' library(dynwrap)
+#' data(example_trajectory)
+#'
+#' calculate_overall_feature_importance(example_trajectory)
+calculate_overall_feature_importance <- function(
+  trajectory,
+  expression_source = "expression",
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+) {
+  calculate_milestone_feature_importance(
+    trajectory = trajectory,
+    expression_source = expression_source,
+    fi_method = fi_method,
+    verbose = verbose
+  ) %>%
+    group_by(.data$feature_id) %>%
+    summarise(importance = mean(.data$importance)) %>%
+    arrange(desc(.data$importance))
+}
\ No newline at end of file
diff --git a/R/calculate_waypoint_feature_importance.R b/R/calculate_waypoint_feature_importance.R
index 02d321f..a5463a5 100644
--- a/R/calculate_waypoint_feature_importance.R
+++ b/R/calculate_waypoint_feature_importance.R
@@ -1,34 +1,29 @@
 #' @rdname calculate_overall_feature_importance
 #'
 #' @export
-#'
-#' @include calculate_feature_importances.R
-calculate_waypoint_feature_importance <- inherit_default_params(
-  super_functions = list(calculate_feature_importances),
-  fun = function(
-    trajectory,
-    expression_source = "expression",
-    waypoints = NULL,
-    fi_method,
-    verbose
-  ) {
-    if (is.null(waypoints)) {
-      if (!is_wrapper_with_waypoints(trajectory)) {
-        message("Adding waypoints to prediction")
-        trajectory <- trajectory %>% dynwrap::add_waypoints()
-      }
-
-      waypoints <- trajectory$waypoints
+calculate_waypoint_feature_importance <- function(
+  trajectory,
+  expression_source = "expression",
+  waypoints = NULL,
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+) {
+  if (is.null(waypoints)) {
+    if (!is_wrapper_with_waypoints(trajectory)) {
+      message("Adding waypoints to prediction")
+      trajectory <- trajectory %>% dynwrap::add_waypoints()
     }
 
-    expression <- get_expression(trajectory, expression_source)
-
-    calculate_feature_importances(
-      X = expression,
-      Y = t(waypoints$geodesic_distances)[rownames(expression),],
-      fi_method = fi_method,
-      verbose = verbose
-    ) %>%
-      rename(waypoint_id = predictor_id)
+    waypoints <- trajectory$waypoints
   }
-)
\ No newline at end of file
+
+  expression <- get_expression(trajectory, expression_source)
+
+  calculate_feature_importances(
+    X = expression,
+    Y = t(waypoints$geodesic_distances)[rownames(expression),unique(rownames(waypoints$geodesic_distances))],
+    fi_method = fi_method,
+    verbose = verbose
+  ) %>%
+    rename(waypoint_id = .data$predictor_id)
+}
\ No newline at end of file
diff --git a/R/fi_methods.R b/R/fi_methods.R
index ad56084..c3a6701 100644
--- a/R/fi_methods.R
+++ b/R/fi_methods.R
@@ -14,11 +14,19 @@ apply_function_params <- function(params, nrow, ncol) {
 #' @param num_variables_per_split (fi_ranger_rf_lite) The number of variables to sample per split
 #' @param num_samples_per_tree (fi_ranger_rf_lite) The number of samples to bootstrap per split
 #' @param min_node_size (fi_ranger_rf_lite) The minimum node size, no split will be made if the node size is less than this value.
-#' @param ... Extra parameters to pass onto the underlying feature importnce function.
+#' @param ... Extra parameters to pass onto the underlying feature importance function.
+#'
+#' @returns A list containing a helper function for calling a feature importance function.
 #'
 #' @rdname fi_methods
 #'
 #' @export
+#'
+#' @examples
+#' library(dynwrap)
+#' data(example_trajectory)
+#'
+#' calculate_overall_feature_importance(example_trajectory, fi_method = fi_ranger_rf())
 fi_ranger_rf_lite <- function(
   num_trees = 2000,
   num_variables_per_split = 50,
@@ -116,7 +124,9 @@ fi_caret <- function(
 
       model <- do.call(caret::train, method_params)
 
-      caret::varImp(model)[[1]] %>% {set_names(.[, 1], rownames(.))}
+      vi <- caret::varImp(model)[[1]]
+
+      set_names(vi[, 1], rownames(vi))
     }
   )
 }
diff --git a/R/package.R b/R/package.R
index be0c517..2958831 100755
--- a/R/package.R
+++ b/R/package.R
@@ -1,9 +1,11 @@
-#' Dynfeature feature importance
+#' Feature Importance for Dynamic Processes
+#'
+#' Calculating feature importance scores from trajectories using the random forests algorithm.
 #'
 #' @import dplyr
 #' @import tidyr
 #' @import methods
-#' @import tibble
+#' @importFrom tibble tibble tribble as_tibble
 #' @import dynutils
 #' @import dynwrap
 #' @importFrom purrr %>% map map_df map_chr map_lgl map_int map_dbl keep set_names list_modify invoke
diff --git a/README.Rmd b/README.Rmd
index d5d9f5b..c302767 100755
--- a/README.Rmd
+++ b/README.Rmd
@@ -23,12 +23,11 @@ The plotting of the top features is nicely intergrated into [dynplot](https://gi
 
 ## Latest changes
 
-Check out `news(package = "dynwrap")` or [NEWS.md](inst/NEWS.md) for a full list of changes.
+Check out `news(package = "dynwrap")` or [NEWS.md](NEWS.md) for a full list of changes.
 
-<!-- This section gets automatically generated from inst/NEWS.md, and also generates inst/NEWS -->
+<!-- This section gets automatically generated from NEWS.md -->
 
 ```{r news, echo=FALSE, results="asis"}
-dynutils::update_news()
 cat(dynutils::recent_news())
 ```
 
diff --git a/README.md b/README.md
index a6b2a52..f21ae7e 100755
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@ heatmap](https://raw.githubusercontent.com/dynverse/dynplot/devel/.readme_files/
 
 ## Latest changes
 
-Check out `news(package = "dynwrap")` or [NEWS.md](inst/NEWS.md) for a
+Check out `news(package = "dynwrap")` or [NEWS.md](NEWS.md) for a
 full list of
 changes.
 
diff --git a/cran-comments.md b/cran-comments.md
new file mode 100644
index 0000000..093ddf6
--- /dev/null
+++ b/cran-comments.md
@@ -0,0 +1,26 @@
+# dynfeature 1.0.0
+
+Initial release of dynfeature on CRAN.
+
+* MINOR CHANGE: Use only one core by default.
+
+* MINOR CHANGE: Support sparse matrices.
+
+* DOCUMENTATION: Add examples and returns.
+
+## Test environments
+* local Fedora install, R 4.0
+* ubuntu 20.04, mac os x, windows (on github actions), R 4.0
+* win-builder (devel and release)
+
+## R CMD check results
+
+```
+── R CMD check results ─────────────────────────────────── dynfeature 1.0.0 ────
+Duration: 3m 0.1s
+
+0 errors ✓ | 0 warnings ✓ | 0 notes ✓
+
+R CMD check succeeded
+```
+
diff --git a/inst/NEWS b/inst/NEWS
deleted file mode 100644
index 5cd73e6..0000000
--- a/inst/NEWS
+++ /dev/null
@@ -1,31 +0,0 @@
-dynfeature 1.0.0 (28-03-2019)
-
-* MINOR CHANGE: Use only one core by default.
-
-* MINOR CHANGE: Support sparse matrices
-
-dynfeature 0.2.0 (25-10-2018)
-
-* SPEED UP: Added `fi_ranger_rf_lite()`, which scales much better w.r.t. the number of samples and features, 
-  at the cost of increasing loss of accuracy at higher dimension sizes.
-  
-* MAJOR CHANGES: Large cleanup of the code. Most notably,
-  - The format of feature importance method specification and its parameters, 
-    with format `fi_method = fi_example_method(param1 = 10, param2 = 4)`. 
-    Before, it had to be specified as `method = "example_method", method_params = list(param1 = 10, param2 = 4)`.
-
-* MINOR CHANGE: Whenever possible, output columns are now factors instead of characters.
-
-* MINOR CHANGE: Add NEWS, and add news section to README.
-
-* DOCUMENTATION: Turned on markdown for Roxygen.
-
-* DOCUMENTATION: Improved documentation on expression_source.
-
-* TESTING: Improved testing with a larger dataset, and will check whether the overall feature importance produces decent results.
-
-* MINOR CHANGE: Feature importance functions will always return factors instead of characters.
-
-dynfeature 0.1.0 (26-04-2018)
-
-* INITIAL RELEASE: dynfeature, calculating feature importance scores from trajectories.
diff --git a/man/calculate_feature_importances.Rd b/man/calculate_feature_importances.Rd
index a6f984d..ad201ed 100644
--- a/man/calculate_feature_importances.Rd
+++ b/man/calculate_feature_importances.Rd
@@ -4,8 +4,12 @@
 \alias{calculate_feature_importances}
 \title{Calculate feature importance scores}
 \usage{
-calculate_feature_importances(X, Y, fi_method = fi_ranger_rf_lite(),
-  verbose = FALSE)
+calculate_feature_importances(
+  X,
+  Y,
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+)
 }
 \arguments{
 \item{X}{A data frame containing the features as columns.}
@@ -16,6 +20,16 @@ calculate_feature_importances(X, Y, fi_method = fi_ranger_rf_lite(),
 
 \item{verbose}{Whether to print out extra information.}
 }
+\value{
+A data frame with three columns, \code{predictor_id}, \code{feature_id}, and \code{importance}. \code{predictor_id} is a column in \code{Y}, while \code{feature_id} is a column in \code{X}.
+}
 \description{
 Calculate feature importance scores
 }
+\examples{
+X <- data.frame(matrix(runif(25*10), ncol = 10))
+Y <- data.frame(matrix(runif(25*2), ncol = 2))
+
+# don't run since this function is not exported
+# calculate_feature_importances(X, Y)
+}
diff --git a/man/calculate_overall_feature_importance.Rd b/man/calculate_overall_feature_importance.Rd
index 42fc9d6..f5c548b 100755
--- a/man/calculate_overall_feature_importance.Rd
+++ b/man/calculate_overall_feature_importance.Rd
@@ -14,30 +14,50 @@
 \alias{calculate_waypoint_feature_importance}
 \title{Calculating feature importances across trajectories}
 \usage{
-calculate_branch_feature_importance(trajectory,
-  expression_source = "expression", fi_method = fi_ranger_rf_lite(),
-  verbose = FALSE)
+calculate_branch_feature_importance(
+  trajectory,
+  expression_source = "expression",
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+)
 
-calculate_branching_point_feature_importance(trajectory,
+calculate_branching_point_feature_importance(
+  trajectory,
   expression_source = "expression",
   milestones_oi = trajectory$milestone_ids,
-  fi_method = fi_ranger_rf_lite(), verbose = FALSE)
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+)
 
-calculate_cell_feature_importance(trajectory,
-  expression_source = "expression", fi_method = fi_ranger_rf_lite(),
-  verbose = FALSE)
+calculate_cell_feature_importance(
+  trajectory,
+  expression_source = "expression",
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+)
 
-calculate_milestone_feature_importance(trajectory,
-  expression_source = "expression", milestones_oi = NULL,
-  fi_method = fi_ranger_rf_lite(), verbose = FALSE)
+calculate_milestone_feature_importance(
+  trajectory,
+  expression_source = "expression",
+  milestones_oi = NULL,
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+)
 
-calculate_overall_feature_importance(trajectory,
-  expression_source = "expression", fi_method = fi_ranger_rf_lite(),
-  verbose = FALSE)
+calculate_overall_feature_importance(
+  trajectory,
+  expression_source = "expression",
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+)
 
-calculate_waypoint_feature_importance(trajectory,
-  expression_source = "expression", waypoints = NULL,
-  fi_method = fi_ranger_rf_lite(), verbose = FALSE)
+calculate_waypoint_feature_importance(
+  trajectory,
+  expression_source = "expression",
+  waypoints = NULL,
+  fi_method = fi_ranger_rf_lite(),
+  verbose = FALSE
+)
 }
 \arguments{
 \item{trajectory}{A trajectory object containing expression values and a trajectory.}
@@ -57,8 +77,17 @@ calculate_waypoint_feature_importance(trajectory,
 
 \item{waypoints}{The waypoints, optional}
 }
+\value{
+A data frame with two or more columns, \code{feature_id}, and \code{importance}. \code{feature_id} is a column in the trajectory expression matrix. Additional columns may be available depending on the function called.
+}
 \description{
 Uses the feature importance measures of \code{\link[ranger]{ranger}} or \code{caret}.
 \code{calculate_overall_feature_importance} calculates the importance for the whole trajectory,
 \code{calculate_milestone_feature_importance} calculates it for individual milestones (e.g. branching points)
 }
+\examples{
+library(dynwrap)
+data(example_trajectory)
+
+calculate_overall_feature_importance(example_trajectory)
+}
diff --git a/man/dynfeature.Rd b/man/dynfeature.Rd
index 6c23920..74d7cec 100755
--- a/man/dynfeature.Rd
+++ b/man/dynfeature.Rd
@@ -3,8 +3,7 @@
 \docType{package}
 \name{dynfeature}
 \alias{dynfeature}
-\alias{dynfeature-package}
-\title{Dynfeature feature importance}
+\title{Feature Importance for Dynamic Processes}
 \description{
-Dynfeature feature importance
+Calculating feature importance scores from trajectories using the random forests algorithm.
 }
diff --git a/man/fi_methods.Rd b/man/fi_methods.Rd
index aafac26..ce17fe7 100644
--- a/man/fi_methods.Rd
+++ b/man/fi_methods.Rd
@@ -4,14 +4,28 @@
 \alias{fi_ranger_rf_lite}
 \alias{fi_ranger_rf}
 \alias{fi_caret}
+\alias{fi_ranger_rf_tiny}
 \title{Feature Importance methods}
 \usage{
-fi_ranger_rf_lite(num_trees = 2000, num_variables_per_split = 50,
-  num_samples_per_tree = 250, min_node_size = 20, ...)
+fi_ranger_rf_lite(
+  num_trees = 2000,
+  num_variables_per_split = 50,
+  num_samples_per_tree = 250,
+  min_node_size = 20,
+  ...
+)
 
 fi_ranger_rf(...)
 
 fi_caret(caret_method, ...)
+
+fi_ranger_rf_tiny(
+  num_trees = 100,
+  num_variables_per_split = 50,
+  num_samples_per_tree = 250,
+  min_node_size = 20,
+  ...
+)
 }
 \arguments{
 \item{num_trees}{(fi_ranger_rf_lite) The number of trees to use}
@@ -22,10 +36,19 @@ fi_caret(caret_method, ...)
 
 \item{min_node_size}{(fi_ranger_rf_lite) The minimum node size, no split will be made if the node size is less than this value.}
 
-\item{...}{Extra parameters to pass onto the underlying feature importnce function.}
+\item{...}{Extra parameters to pass onto the underlying feature importance function.}
 
 \item{caret_method}{(fi_caret) Which caret method to use for feature importance.}
 }
+\value{
+A list containing a helper function for calling a feature importance function.
+}
 \description{
 Feature Importance methods
 }
+\examples{
+library(dynwrap)
+data(example_trajectory)
+
+calculate_overall_feature_importance(example_trajectory, fi_method = fi_ranger_rf())
+}
diff --git a/tests/testthat/test-calculate_feature_importance.R b/tests/testthat/test-calculate_feature_importance.R
index 4b7c898..94ef3e4 100755
--- a/tests/testthat/test-calculate_feature_importance.R
+++ b/tests/testthat/test-calculate_feature_importance.R
@@ -11,8 +11,8 @@ milestone_network <- tibble::tribble(
   "A",   "D", 1,       TRUE
 )
 
-num_cells <- 200
-num_features <- 300
+num_cells <- 100
+num_features <- 20
 
 cell_ids <- paste0("cell_", seq_len(num_cells))
 feature_ids <- paste0("feature_", seq_len(num_features))
@@ -39,7 +39,7 @@ trajectory <-
   )
 
 # generate module positions
-num_modules <- 20
+num_modules <- 10
 module_ids <- paste0("module_", seq_len(num_modules))
 module_progressions <-
   milestone_network %>%
@@ -73,7 +73,7 @@ suppressWarnings({
   module_expression <-
     distance_from_module %>%
     reshape2::melt(varnames = c("module_id", "cell_id"), value.name = "distance") %>%
-    as_data_frame() %>%
+    as_tibble(.name_repair = "minimal") %>%
     left_join(module_progressions %>% select(-from, -to, -percentage), by = "module_id") %>%
     mutate(
       expr = dnorm(distance, mean = 0, sd = sd) * mult + basal
@@ -83,7 +83,7 @@ suppressWarnings({
 module_expression <- module_expression[module_ids, cell_ids]
 
 # generate gene expression
-feature_info <- data_frame(
+feature_info <- tibble(
   feature_id = feature_ids,
   module = sample.int(num_modules, num_features, replace = TRUE),
   mean = rnorm(num_features, 10, 3) %>% pmax(0),
@@ -95,7 +95,7 @@ expression <-
   feature_info %>%
   group_by(feature_id) %>%
   purrr::pmap_df(function(feature_id, module, mean, sd, dropout) {
-    data_frame(
+    tibble(
       cell_id = colnames(module_expression),
       feature_id,
       expression = ifelse(runif(num_cells) < dropout, 0, module_expression[module, , drop = FALSE] + rnorm(num_cells, mean, sd))