---
title: "bioLeak: Leakage-Aware Biomedical Modeling"
author: "Selçuk Korkmaz"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_document:
    toc: true
    toc_float: true
    number_sections: true
    theme: flatly
    highlight: tango
vignette: >
  %\VignetteIndexEntry{bioLeak: Leakage-Aware Biomedical Modeling}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE,
  eval = TRUE
)
```

# Why bioLeak

bioLeak is a leakage-aware modeling toolkit for biomedical and machine-learning
analyses. Its purpose is to prevent and diagnose information leakage across
resampling workflows where training and evaluation data are not truly
independent because samples share subjects, batches, studies, or time.

Standard workflows are often insufficient. Random, row-wise cross-validation
assumes samples are independent. Global preprocessing (imputation, scaling,
feature selection) done before resampling lets test-fold information shape the
training process. These choices inflate performance and can lead to incorrect
biomarker discovery, misleading clinical signals, or models that fail in
deployment.

Data leakage means any direct or indirect use of evaluation data in training or
feature engineering. In biomedical data, leakage commonly appears as:

- repeated measurements from the same patient split across folds
- batch or study effects that align with the outcome
- duplicates or near-duplicates across train and test
- time-series lookahead or random splits that use future information
- preprocessing that uses all samples instead of training-only statistics

bioLeak addresses these issues with leakage-aware splitting, guarded
preprocessing that is fit only on training data, and audit diagnostics that
surface overlaps, confounding, and duplicates.

# Guided workflow

The sections below walk through a leakage-aware workflow from data setup to
audits. Each step includes a leaky failure mode and a corrected alternative.

## Example data

```{r example-data}
library(bioLeak)

set.seed(123)
n <- 160
subject <- rep(seq_len(40), each = 4)
batch <- sample(paste0("B", 1:6), n, replace = TRUE)
study <- sample(paste0("S", 1:4), n, replace = TRUE)
time <- seq_len(n)

x1 <- rnorm(n)
x2 <- rnorm(n)
x3 <- rnorm(n)
linpred <- 0.7 * x1 - 0.4 * x2 + 0.2 * x3 + rnorm(n, sd = 0.5)
p <- stats::plogis(linpred)
outcome <- factor(ifelse(runif(n) < p, "case", "control"),
                  levels = c("control", "case"))

df <- data.frame(
  subject = subject,
  batch = batch,
  study = study,
  time = time,
  outcome = outcome,
  x1 = x1,
  x2 = x2,
  x3 = x3
)

df_leaky <- within(df, {
  leak_subject <- ave(as.numeric(outcome == "case"), subject, FUN = mean)
  leak_batch <- ave(as.numeric(outcome == "case"), batch, FUN = mean)
  leak_global <- mean(as.numeric(outcome == "case"))
})

df_time <- df
df_time$leak_future <- c(as.numeric(df_time$outcome == "case")[-1], 0)
predictors <- c("x1", "x2", "x3")


# Example data (first 6 rows)
head(df)

# Outcome class counts
as.data.frame(table(df$outcome))
```

The table preview displays the metadata columns (`subject`, `batch`, `study`, `time`), the binary outcome, and three numeric predictors (`x1`, `x2`, `x3`).

The class count table shows the baseline prevalence. Stratified splits try to preserve these proportions (for grouped modes, stratification is applied at the group level).


## Create leakage-aware splits with `make_split_plan()`

`make_split_plan()` is the foundation. It returns a `LeakSplits` object with
explicit train/test indices (or compact fold assignments) and metadata. For
data.frame inputs, a unique `row_id` column is added automatically, so
`group = "row_id"` is the explicit way to request sample-wise CV. It assumes
that the grouping columns you provide are complete and that samples sharing a
group must not cross folds. Grouped stratification uses the majority class per
group and is ignored for `study_loocv`, `time_series`, and survival outcomes.
Misuse to avoid:

- using `group = "row_id"` (sample-wise CV) when subjects repeat
- using the wrong grouping column (for example batch instead of subject)
- using time-series CV with unsorted or missing time values
- relying on stratification when the outcome is missing, single-class at the
  group level, or a time/event outcome
- assuming `time_series` will always return exactly `v` folds

**Leaky example: row-wise CV when subjects repeat**

```{r leaky-splits}
leaky_splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "subject_grouped",
  group = "row_id",
  v = 5,
  repeats = 1,
  stratify = TRUE
)

cat("Leaky splits summary (sample-wise CV):\n")
leaky_splits
```

The printed `LeakSplits` summary reports the split mode, number of folds, and fold-level training and test set sizes.

Because `group = "row_id"`, each sample is treated as its own group. As a result, repeated samples from the same subject may appear in both the training and test sets, which introduces information leakage.


**Leakage-safe alternative: group by subject**

```{r safe-splits}
safe_splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "subject_grouped",
  group = "subject",
  v = 5,
  repeats = 1,
  stratify = TRUE,
  seed = 10
)

cat("Leakage-safe splits summary (subject-grouped CV):\n")
safe_splits
```

Here, each subject is confined to a single fold. Test set sizes are multiples
of the number of samples per subject, confirming that subjects were not split
across folds.

The fold sizes remain comparable because `stratify = TRUE` balances outcome
proportions across folds while respecting subject-level boundaries.


**Other leakage-aware modes**

```{r splits-other-modes}
batch_splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "batch_blocked",
  batch = "batch",
  v = 4,
  stratify = TRUE
)

cat("Batch-blocked splits summary:\n")
batch_splits

study_splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "study_loocv",
  study = "study"
)

cat("Study leave-one-out splits summary:\n")
study_splits

time_splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "time_series",
  time = "time",
  v = 4,
  horizon = 2
)

cat("Time-series splits summary:\n")
time_splits

nested_splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "subject_grouped",
  group = "subject",
  v = 3,
  nested = TRUE,
  stratify = TRUE
)

cat("Nested CV splits summary:\n")
nested_splits
```

Each summary reports the number of folds and the fold sizes for the corresponding split strategy.

- **Batch & Study:** The test set sizes vary because batches/studies are not evenly sized. `study_loocv` uses one study per fold, so `v` and `repeats` are ignored.

- **Time series:** The training set size increases across folds while the test set size follows the block size. Early blocks with no prior training data (or too-small test windows) are skipped, so you may see fewer than `v` folds.

**Assumptions and intent by mode**

- `subject_grouped`: keeps all samples from a subject together  
- `batch_blocked`: keeps batches/centers together (leave-one-group-out when `v`
  is at least the number of batches; otherwise multiple batches per fold)  
- `study_loocv`: holds out each study in turn for external validation  
- `time_series`: rolling-origin splits with an optional horizon to prevent lookahead

Use `repeats` for repeated CV in grouped/batch modes (it is ignored for
`study_loocv` and `time_series`), `stratify = TRUE` to balance outcome
proportions at the group level when applicable, and `nested = TRUE` to attach
inner folds (one repeat). For large datasets, `progress = TRUE` reports
progress; storing explicit indices can be memory intensive.


## Scalability

**Handling Large Datasets (Compact Mode)**

For large datasets (e.g., $N > 50,000$) with many repeats, storing explicit
integer indices for every fold can consume gigabytes of memory. Use `compact = TRUE`
to store a lightweight vector of fold assignments instead. `fit_resample()` will
automatically reconstruct the indices on the fly.
Compact mode is not supported when `nested = TRUE` (it falls back to full
indices). For `time_series` compact splits, the time column must be present in
the stored split metadata so folds can be reconstructed.

```{r compact-splits}
# Efficient storage for large N
big_splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "subject_grouped",
  group = "subject",
  v = 5,
  compact = TRUE  # <--- Saves memory
)

cat("Compact-mode splits summary:\n")
big_splits
cat(sprintf("Compact storage enabled: %s\n", big_splits@info$compact))
```

The summary is identical to a regular split, but the underlying storage is a
compact fold-assignment vector. Use the `compact` flag to confirm the memory-
saving mode is active.

## Guarded preprocessing and imputation

`bioLeak` uses guarded preprocessing to prevent leakage from global imputation,
scaling, and feature selection. The low-level API is:

- `.guard_fit()` to fit preprocessing on training data only
- `predict_guard()` to apply the trained guard to new data
- `.guard_ensure_levels()` to align factor levels across train/test
- `impute_guarded()` as a convenience wrapper for leakage-safe imputation

`.guard_fit()` fits a pipeline that can winsorize, impute, normalize, filter,
and select features. It one-hot encodes non-numeric columns and carries factor
levels forward to new data. Assumptions and misuse to avoid:

- `fs = "ttest"` requires a binary outcome with enough samples per class
- `fs = "lasso"` requires `glmnet`
- `impute = "mice"` is not supported for guarded prediction
- `impute = "none"` with missing values will trigger a median fallback and
  missingness indicators to avoid errors

Supported preprocessing options include imputation (`median`, `knn`,
`missForest`, `none`), normalization (`zscore`, `robust`, `none`), filtering by
variance or IQR (optionally `min_keep`), and feature selection (`ttest`,
`lasso`, `pca`). Winsorization is controlled via `impute$winsor` and
`impute$winsor_k` in guarded steps; in `impute_guarded()`, use `winsor` and
`winsor_thresh`. `impute_guarded()` returns a `LeakImpute` object with guarded
data, diagnostics, and the fitted guard state.

**Leaky example: global scaling before CV**

```{r leaky-scaling}
df_leaky_scaled <- df
df_leaky_scaled[predictors] <- scale(df_leaky_scaled[predictors])
scaled_summary <- data.frame(
  feature = predictors,
  mean = colMeans(df_leaky_scaled[predictors]),
  sd = apply(df_leaky_scaled[predictors], 2, stats::sd)
)
scaled_summary$mean <- round(scaled_summary$mean, 3)
scaled_summary$sd <- round(scaled_summary$sd, 3)

# Leaky global scaling: means ~0 and SDs ~1 computed on all samples
scaled_summary
```

The summary shows that scaling used the full dataset, so test-fold statistics
influenced the training transformation. This violates the train/test separation
and can bias performance estimates.

**Leakage-safe alternative: fit preprocessing on training only**

```{r guarded-preprocess}
fold1 <- safe_splits@indices[[1]]
train_x <- df[fold1$train, predictors]
test_x <- df[fold1$test, predictors]

guard <- .guard_fit(
  X = train_x,
  y = df$outcome[fold1$train],
  steps = list(
    impute = list(method = "median", winsor = TRUE),
    normalize = list(method = "zscore"),
    filter = list(var_thresh = 0, iqr_thresh = 0),
    fs = list(method = "none")
  ),
  task = "binomial"
)

train_x_guarded <- predict_guard(guard, train_x)
test_x_guarded <- predict_guard(guard, test_x)

cat("GuardFit object:\n")
guard
cat("\nGuardFit summary:\n")
summary(guard)

# Guarded training data (first 6 rows)
head(train_x_guarded)

# Guarded test data (first 6 rows)
head(test_x_guarded)
```

The `GuardFit` object records the preprocessing steps and the number of
features retained after filtering. The summary reports how many features were
removed and the preprocessing audit trail. The guarded train/test previews show
that missing values were imputed and (if requested) scaled using training-only
statistics; the test data never influences these values.

**Factor level guard (advanced)**

```{r guard-ensure-levels}
raw_levels <- data.frame(
  site = c("A", "B", "B"),
  status = c("yes", "no", "yes"),
  stringsAsFactors = FALSE
)

level_state <- .guard_ensure_levels(raw_levels)

# Aligned factor data with consistent levels
level_state$data

# Levels map
level_state$levels
```

The returned `data` keeps factor levels consistent across folds, while the
`levels` list records the training-time levels (including any dummy levels added
to preserve one-hot columns). Use these when transforming new data to avoid
misaligned model matrices.

**Leaky example: imputation using train and test together**

```{r leaky-impute}
train <- data.frame(a = c(1, 2, NA, 4), b = c(NA, 1, 1, 0))
test <- data.frame(a = c(NA, 5), b = c(1, NA))

all_median <- vapply(rbind(train, test),
                     function(col) median(col, na.rm = TRUE),
                     numeric(1))
train_leaky <- as.data.frame(Map(function(col, m) { col[is.na(col)] <- m; col },
                                 train, all_median))
test_leaky <- as.data.frame(Map(function(col, m) { col[is.na(col)] <- m; col },
                                test, all_median))

# Leaky medians computed on train + test
data.frame(feature = names(all_median), median = all_median)

# Leaky-imputed training data
train_leaky

# Leaky-imputed test data
test_leaky
```

The medians above were computed using both train and test data, so the test set
influences the imputation values. This is a classic leakage pathway because
test information directly alters the training features.

**Leakage-safe alternative: `impute_guarded()`**

```{r safe-impute}
imp <- impute_guarded(
  train = train,
  test = test,
  method = "median",
  winsor = FALSE
)

# Guarded-imputed training data
imp$train

# Guarded-imputed test data
imp$test
```

Here, the imputation statistics are computed from the training set only. The missing value in the test set (column `a`) is replaced by 2, which is the training median. In contrast, in the leaky example above, it was replaced by 3, the global median.

This confirms that the test set is transformed using fixed values learned from the training data, preserving a clean separation between training and evaluation.
The `LeakImpute` object also contains missingness diagnostics in
`imp$summary$diagnostics`, and guarded outputs use the same encoding as
`.guard_fit()` (categorical variables are one-hot encoded). Use `vars` to
impute only a subset of columns when needed.

## Fit and resample with `fit_resample()`

`fit_resample()` combines leakage-aware splits with guarded preprocessing and
model fitting. It supports:

- built-in learners (`glmnet`, `ranger`)
- `parsnip` model specifications or `workflows::workflow` objects (recommended)
- `recipes::recipe` preprocessing (prepped on training folds only)
- `rsample` resamples (`rset`/`rsplit`) via `splits`, with `split_cols` to map metadata
- custom learners via `custom_learners` (legacy/advanced)
- multiple metrics, class weights, positive class override, and yardstick metrics
- fold-level run diagnostics in `fit@info$fold_status` (`success`/`skipped`/`failed`)

Assumptions and misuse to avoid:

- outcome must be binary or multiclass (factor) for classification, numeric for
  regression, or a survival outcome (a `Surv` column or time/event columns)
- `positive_class` must be a single value that exists in the outcome levels
- `class_weights` only applies to binomial/multiclass tasks; workflows must
  handle weights internally
- if you supply a matrix without metadata, you must remove leakage columns
  yourself (group, batch, study, time)

Use `learner_args` to pass model-specific arguments. For custom learners
(advanced), you can supply separate `fit` and `predict` argument lists. Set
`parallel = TRUE` to use future.apply for fold-level parallelism when available.
When `learner` is a parsnip specification, `learner_args` and
`custom_learners` are ignored. When `learner` is a workflow, `preprocess` and
`learner_args` are ignored.
When a recipe or workflow is used, the built-in guarded preprocessing list is
bypassed, so ensure the recipe/workflow itself is leakage-safe.

Metrics used in this vignette:

- **AUC**: area under the ROC curve (0.5 = random, 1.0 = perfect; higher is better)
- **PR AUC**: area under the precision-recall curve (0 to 1; higher is better)
- **Accuracy**: fraction of correct predictions (0 to 1; higher is better)
- **RMSE**: root mean squared error (0 to infinity; lower is better)
- **Macro F1**: average F1 across classes (multiclass; higher is better)
- **Log loss**: cross-entropy for probabilities (lower is better)
- **C-index**: concordance for regression/survival (higher is better)

Always report the mean and variability across folds, not a single fold value.
When using yardstick metrics, the positive class is the second factor level;
set `positive_class` to control this.

**Parsnip model specification (recommended)**

```{r parsnip-spec}
spec <- parsnip::logistic_reg(mode = "classification") |>
  parsnip::set_engine("glm")
```

This spec uses base R `glm` under the hood and does not require extra model
packages. Use it in the examples below; custom learners are covered in the
Advanced section.

**Leaky example: leaky features and row-wise splits**

```{r fit-leaky}
fit_leaky <- fit_resample(
  df_leaky,
  outcome = "outcome",
  splits = leaky_splits,
  learner = spec,
  metrics = c("auc", "accuracy"),
  preprocess = list(
    impute = list(method = "median"),
    normalize = list(method = "zscore"),
    filter = list(var_thresh = 0),
    fs = list(method = "none")
  )
)

cat("Leaky fit summary:\n")
summary(fit_leaky)
metrics_leaky <- as.data.frame(fit_leaky@metric_summary)
num_cols <- vapply(metrics_leaky, is.numeric, logical(1))
metrics_leaky[num_cols] <- lapply(metrics_leaky[num_cols], round, digits = 3)

# Leaky fit: mean and SD of metrics across folds
metrics_leaky
```

The summary reports cross-validated performance. AUC ranges from 0.5 (random)
to 1.0 (perfect), while accuracy is the fraction of correct predictions.
Because the splits are leaky, these metrics can be artificially high and should
not be used for reporting.

**Leakage-safe alternative: grouped splits and clean predictors**

```{r fit-safe}
fit_safe <- fit_resample(
  df,
  outcome = "outcome",
  splits = safe_splits,
  learner = spec,
  metrics = c("auc", "accuracy"),
  preprocess = list(
    impute = list(method = "median"),
    normalize = list(method = "zscore"),
    filter = list(var_thresh = 0),
    fs = list(method = "none")
  ),
  positive_class = "case",
  class_weights = c(control = 1, case = 1),
  refit = TRUE
)

cat("Leakage-safe fit summary:\n")
summary(fit_safe)
metrics_safe <- as.data.frame(fit_safe@metric_summary)
num_cols <- vapply(metrics_safe, is.numeric, logical(1))
metrics_safe[num_cols] <- lapply(metrics_safe[num_cols], round, digits = 3)

# Leakage-safe fit: mean and SD of metrics across folds
metrics_safe

# Per-fold metrics (first 6 rows)
head(fit_safe@metrics)
```

`fit_resample()` returns a `LeakFit` object. Use `summary(fit_safe)` for a
compact report and inspect `fit_safe@metrics` and `fit_safe@predictions` for
details. When `refit = TRUE`, the final model and preprocessing state are stored
in `fit_safe@info$final_model` and `fit_safe@info$final_preprocess`.
Fold-level status diagnostics are available in `fit_safe@info$fold_status`.

The mean +/- SD table is the primary performance summary to report, while the
per-fold metrics reveal variability and potential instability across folds.

By default, `fit_resample()` stores refit inputs in `fit_safe@info$perm_refit_spec`
to enable `audit_leakage(perm_refit = "auto")`. Set `store_refit_data = FALSE`
to reduce memory and provide `perm_refit_spec` manually when you want refit-based
permutations. When multiple learners are passed, `refit = TRUE` refits only the
first learner; set `refit = FALSE` if you do not need a final model.

```{r fit-fold-status}
# Fold-level diagnostics for reproducible troubleshooting
head(fit_safe@info$fold_status, 5)
```

Interpretation of `fold_status`:

- `status = "success"` means at least one learner produced valid metrics on that fold.
- `status = "skipped"` typically means a data-structure issue in that fold (for example, single-class training data in classification).
- `status = "failed"` indicates a runtime failure (for example, model fitting or preprocessing error).

Use `reason` and `notes` to decide whether to:

- adjust split design (for example, stronger grouping, stratification, or fewer folds),
- adjust preprocessing/model complexity,
- or exclude unstable configurations before formal comparison.

If many folds are skipped/failed, do **not** trust aggregate metric means until the instability is resolved.

The examples above use a parsnip model specification. To swap in other models,
replace `spec` with another parsnip spec (see the gradient boosting example
below).

**Multiclass classification (optional)**

```{r multiclass-example}
if (requireNamespace("ranger", quietly = TRUE)) {
  set.seed(11)
  df_multi <- df
  df_multi$outcome3 <- factor(sample(c("A", "B", "C"),
                                     nrow(df_multi), replace = TRUE))

  multi_splits <- make_split_plan(
    df_multi,
    outcome = "outcome3",
    mode = "subject_grouped",
    group = "subject",
    v = 4,
    stratify = TRUE,
    seed = 11
  )

  fit_multi <- fit_resample(
    df_multi,
    outcome = "outcome3",
    splits = multi_splits,
    learner = "ranger",
    metrics = c("accuracy", "macro_f1", "log_loss"),
    refit = FALSE
  )

  cat("Multiclass fit summary:\n")
  summary(fit_multi)
} else {
  cat("ranger not installed; skipping multiclass example.\n")
}
```

Multiclass fits compute accuracy, macro-F1, and log loss when class
probabilities are available. `positive_class` is ignored for multiclass tasks.

**Survival outcomes (optional)**

```{r survival-example}
if (requireNamespace("survival", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  set.seed(12)
  df_surv <- df
  df_surv$time_to_event <- rexp(nrow(df_surv), rate = 0.1)
  df_surv$event <- rbinom(nrow(df_surv), 1, 0.7)

  surv_splits <- make_split_plan(
    df_surv,
    outcome = c("time_to_event", "event"),
    mode = "subject_grouped",
    group = "subject",
    v = 4,
    stratify = FALSE,
    seed = 12
  )

  fit_surv <- fit_resample(
    df_surv,
    outcome = c("time_to_event", "event"),
    splits = surv_splits,
    learner = "glmnet",
    metrics = "cindex",
    refit = FALSE
  )

  cat("Survival fit summary:\n")
  summary(fit_surv)
} else {
  cat("survival or glmnet not installed; skipping survival example.\n")
}
```

For survival tasks, supply `outcome = c(time_col, event_col)` or a `Surv`
column; `stratify` is ignored for time/event outcomes and `class_weights` are
not used.

**Passing learner-specific arguments (optional)**

```{r learner-args}
if (requireNamespace("glmnet", quietly = TRUE)) {
  fit_glmnet <- fit_resample(
    df,
    outcome = "outcome",
    splits = safe_splits,
    learner = "glmnet",
    metrics = "auc",
    learner_args = list(glmnet = list(alpha = 0.5)),
    preprocess = list(
      impute = list(method = "median"),
      normalize = list(method = "zscore"),
      filter = list(var_thresh = 0),
      fs = list(method = "none")
    )
  )
  cat("GLMNET summary with learner-specific arguments:\n")
  summary(fit_glmnet)
} else {
  cat("glmnet not installed; skipping learner_args example.\n")
}
```

This summary reflects the same guarded preprocessing but a different model
configuration (here, `alpha = 0.5`). Use the mean +/- SD metrics to compare
learner settings under identical splits.

**SummarizedExperiment inputs (optional)**

```{r se-example}
if (requireNamespace("SummarizedExperiment", quietly = TRUE)) {
  se <- SummarizedExperiment::SummarizedExperiment(
    assays = list(counts = t(as.matrix(df[, predictors]))),
    colData = df[, c("subject", "batch", "study", "time", "outcome"), drop = FALSE]
  )

  se_splits <- make_split_plan(
    se,
    outcome = "outcome",
    mode = "subject_grouped",
    group = "subject",
    v = 3
  )

  se_fit <- fit_resample(
    se,
    outcome = "outcome",
    splits = se_splits,
    learner = spec,
    metrics = "auc"
  )
  cat("SummarizedExperiment fit summary:\n")
  summary(se_fit)
} else {
  cat("SummarizedExperiment not installed; skipping SE example.\n")
}
```

The summary is identical in structure to the `data.frame` case because `fit_resample()` extracts predictors and metadata from the `SummarizedExperiment` object.

Note that `features_final` here reflects only the assay predictors (`x1`, `x2`,
`x3`), because the assay was constructed without metadata columns.


## Tidymodels interoperability

`bioLeak` integrates with `rsample`, `recipes`, `workflows`, and `yardstick`:

- `splits` can be an `rsample` rset/rsplit; use `split_cols` (default `"auto"`)
  or `bioLeak_mode` / `bioLeak_perm_mode` attributes to map
  group/batch/study/time metadata.
- `preprocess` can be a `recipes::recipe` (prepped on training folds only).
- `learner` can be a `workflows::workflow`.
- `metrics` can be a `yardstick::metric_set`.
- `as_rsample()` converts `LeakSplits` to an `rsample` object.

```{r tidymodels-interop}
library(bioLeak)
library(parsnip)
library(recipes)
library(yardstick)

set.seed(123)
N <- 60
df <- data.frame(
  subject = factor(rep(paste0("S", 1:20), length.out = N)), 
  outcome = factor(rep(c("ClassA", "ClassB"), length.out = N)),
  x1 = rnorm(N),
  x2 = rnorm(N),
  x3 = rnorm(N)
)

spec <- logistic_reg() |> set_engine("glm")

# Use bioLeak's native split planner to avoid conversion errors
set.seed(13)

# Use make_split_plan instead of rsample::group_vfold_cv
# This creates a subject-grouped CV directly compatible with fit_resample
splits <- make_split_plan(
  df, 
  outcome = "outcome", 
  mode = "subject_grouped", 
  group = "subject", 
  v = 3
)

rec <- recipes::recipe(outcome ~ x1 + x2 + x3, data = df) |>
  recipes::step_impute_median(recipes::all_numeric_predictors()) |>
  recipes::step_normalize(recipes::all_numeric_predictors())

metrics_set <- yardstick::metric_set(yardstick::roc_auc, yardstick::accuracy)

fit_rs <- fit_resample(
  df,
  outcome = "outcome",
  splits = splits,
  learner = spec,
  preprocess = rec,
  metrics = metrics_set,
  refit = FALSE
)

if (exists("as_rsample", where = asNamespace("bioLeak"), mode = "function")) {
    rs_export <- as_rsample(fit_rs@splits, data = df)
    print(rs_export)
}
```

Use `split_cols` to ensure split-defining metadata are dropped from predictors
when using rsample inputs.

```{r workflow-example}
if (requireNamespace("workflows", quietly = TRUE)) {
  wf <- workflows::workflow() |>
    workflows::add_model(spec) |>
    workflows::add_formula(outcome ~ x1 + x2 + x3)

  fit_wf <- fit_resample(
    df,
    outcome = "outcome",
    splits = safe_splits,
    learner = wf,
    metrics = "auc",
    refit = FALSE
  )

  cat("Workflow fit summary:\n")
  summary(fit_wf)
} else {
  cat("workflows not installed; skipping workflow example.\n")
}
```

When auditing rsample-based fits, pass `perm_mode = "subject_grouped"` (or set
`attr(rs, "bioLeak_perm_mode") <- "subject_grouped"`) so restricted permutations
respect the intended split design.


## Nested tuning with `tune_resample()`

`tune_resample()` runs leakage-aware nested tuning with tidymodels. It accepts a
parsnip model specification or workflow, and supports two inner-CV modes:

- precomputed inner folds (for example from `make_split_plan(..., nested = TRUE)`)
- on-the-fly inner split generation for `LeakSplits` using `inner_v`,
  `inner_repeats`, and `inner_seed`

For rsample inputs, inner folds must already be present. Survival tasks are not
yet supported.

New in the development version (planned for v0.2.0):

- deterministic `selection = "one_std_err"` with an explicit simplicity tie-break
- fold-level tuning diagnostics in `tuned$fold_status`
- optional final refit on full data via `refit = TRUE`

```{r tune-example}
if (requireNamespace("parsnip", quietly = TRUE) &&
    requireNamespace("recipes", quietly = TRUE) &&
    requireNamespace("tune", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  # --- 1. Create Data ---
  set.seed(123)
  N <- 60
  df <- data.frame(
    subject = factor(rep(paste0("S", 1:20), length.out = N)),
    outcome = factor(sample(c("ClassA", "ClassB"), N, replace = TRUE)),
    x1 = rnorm(N),
    x2 = rnorm(N),
    x3 = rnorm(N)
  )

  # --- 2. Generate Nested Splits ---
  set.seed(1)
  nested_splits <- make_split_plan(
    df,
    outcome = "outcome",
    mode = "subject_grouped",
    group = "subject",
    v = 3,
    nested = TRUE
  )

  # --- 3. Define Recipe & Model ---
  rec <- recipes::recipe(outcome ~ x1 + x2 + x3, data = df) |>
    recipes::step_impute_median(recipes::all_numeric_predictors()) |>
    recipes::step_normalize(recipes::all_numeric_predictors())

  spec_tune <- parsnip::logistic_reg(
    penalty = tune::tune(),
    mixture = 1,
    mode = "classification"
  ) |>
    parsnip::set_engine("glmnet")

  # --- 4. Run Tuning ---
  tuned <- tune_resample(
    df,
    outcome = "outcome",
    splits = nested_splits,
    learner = spec_tune,
    preprocess = rec,
    inner_v = 2,
    grid = 3,
    metrics = c("auc", "accuracy"),
    selection = "one_std_err",
    refit = TRUE,
    seed = 14
  )

  summary(tuned)
} else {
  cat("parsnip/recipes/tune/glmnet not installed; skipping nested tuning example.\n")
}
```

```{r tune-diagnostics}
if (exists("tuned")) {
  # Fold-level status from the outer loop
  head(tuned$fold_status, 5)

  # Final tuned model is available when refit = TRUE
  is.null(tuned$final_model)
} else {
  cat("Nested tuning object not available (dependencies missing).\n")
}
```

How to interpret this tuning output:

- `selection = "one_std_err"` chooses a model within one standard error of the best inner-CV score, then applies a deterministic simplicity tie-break. This usually favors more regularized/simpler models with similar expected performance.
- `tuned$metrics` and `tuned$metric_summary` remain the primary unbiased performance estimates (outer folds only).
- `tuned$final_model` is the deployable model fit on full data using the selected hyperparameters; it is for downstream prediction, not for estimating generalization.
- `tuned$fold_status` should be reviewed before reporting results. A large number of skipped/failed folds indicates unstable tuning and should trigger split/parameter redesign.


## Advanced: Using Gradient Boosting with Parsnip

`bioLeak` natively supports `tidymodels` specifications. You can pass a `parsnip`
model specification directly to `fit_resample()`. This allows you to use
state-of-the-art algorithms like XGBoost, LightGBM, or SVMs while ensuring all
preprocessing remains guarded.

```{r fit-xgboost}
if (requireNamespace("parsnip", quietly = TRUE) &&
    requireNamespace("xgboost", quietly = TRUE) &&
    requireNamespace("recipes", quietly = TRUE)) {
  
  # 1. Define the model spec
  xgb_spec <- parsnip::boost_tree(
    mode = "classification",
    trees = 100,
    tree_depth = 6,
    learn_rate = 0.01
  ) |>
    parsnip::set_engine("xgboost")
  
  # 2. Define a recipe on data without 'subject' because split metadata
  # columns are excluded from predictors by fit_resample().
  df_for_rec <- df[, !names(df) %in% "subject"]
  
  rec_xgb <- recipes::recipe(outcome ~ ., data = df_for_rec) |>
    recipes::step_dummy(recipes::all_nominal_predictors()) |>
    recipes::step_impute_median(recipes::all_numeric_predictors())
  # Note: No need for step_rm(subject) because it's already gone!
  
  # 3. Fit
  fit_xgb <- fit_resample(
    df,
    outcome = "outcome",
    splits = nested_splits,
    learner = xgb_spec,
    metrics = "auc",
    preprocess = rec_xgb 
  )
  
  cat("XGBoost parsnip fit summary:\n")
  print(summary(fit_xgb))
} else {
  cat("parsnip/xgboost/recipes not installed.\n")
}
```

The summary reports cross-validated AUC for a non-linear gradient boosting
model. Use the mean $\pm$ SD table to compare against baseline models, and
confirm that any gains do not coincide with leakage signals in the audit
diagnostics.


## Advanced: Custom learners

Custom learners are used when a model is not available as a *parsnip* specification or when a lightweight wrapper around base R is needed.

Each custom learner must define `fit` and `predict` components. The `fit` function must accept `x`, `y`, `task`, and `weights` as inputs, while the `predict` function must accept `object` and `newdata`.

```{r custom-learners}
custom_learners <- list(
  glm = list(
    fit = function(x, y, task, weights, ...) {
      df_fit <- data.frame(y = y, x, check.names = FALSE)
      stats::glm(y ~ ., data = df_fit,
                 family = stats::binomial(), weights = weights)
    },
    predict = function(object, newdata, task, ...) {
      as.numeric(stats::predict(object, newdata = as.data.frame(newdata),
                                type = "response"))
    }
  )
)

cat("Custom learner names:\n")
names(custom_learners)
cat("Custom learner components (fit/predict):\n")
lapply(custom_learners, names)
```

This output confirms that each custom learner provides both a `fit` and a `predict` function.

Custom learners can be used with `fit_resample()` as follows:

```r
fit_resample(
  ...,
  learner = "glm",
  custom_learners = custom_learners
)
```

## Visual diagnostics

**bioLeak** includes plotting helpers for quick diagnostic checks:

- `plot_fold_balance()` shows class balance per fold.  
- `plot_overlap_checks()` highlights train/test overlap for a metadata column.  
- `plot_time_acf()` checks autocorrelation in time-series predictions.
- `plot_calibration()` shows reliability of binomial probabilities.  
- `plot_confounder_sensitivity()` summarizes performance by batch/study strata.

Tabular helpers are also available:

- `calibration_summary()` returns calibration curve data and ECE/MCE/Brier metrics.
- `confounder_sensitivity()` returns per-stratum performance summaries.

**Misuse to avoid**

- Plotting without predictions (fits with no successful folds).  
- Using `plot_overlap_checks()` when the column is not present in the split metadata.  
- Using `plot_time_acf()` without a time column or for non-temporal data.
- Using `plot_calibration()` outside binomial tasks.
- Using `plot_confounder_sensitivity()` with a metric unsupported by the task.

For classification, `plot_fold_balance()` uses the positive class recorded in
`fit@info$positive_class`, or defaults to the second factor level if this is not
set. For multiclass tasks, it shows per-class counts without a proportion line.

```{r plot-fold-balance}
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot_fold_balance(fit_safe)
} else {
  cat("ggplot2 not installed; skipping fold balance plot.\n")
}
```

The bar chart shows the counts of Positives (blue) and Negatives (tan) in each
fold. The dashed blue line represents the proportion of the positive class.

In a well-stratified fit, this line remains relatively stable across folds.
Large fluctuations indicate poor stratification, which can lead to unstable
fold-level performance estimates.

```{r plot-calibration}
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot_calibration(fit_safe, bins = 10)
} else {
  cat("ggplot2 not installed; skipping calibration plot.\n")
}
```

Calibration curves compare predicted probabilities to observed event rates. Large deviations from the diagonal indicate miscalibration.
Use `min_bin_n` to suppress tiny bins and `learner =` to select a model when
multiple learners are stored in the fit.

```{r plot-confounder-sensitivity}
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot_confounder_sensitivity(fit_safe, confounders = c("batch", "study"),
                              metric = "auc", min_n = 3)
} else {
  cat("ggplot2 not installed; skipping confounder sensitivity plot.\n")
}
```

Confounder sensitivity plots highlight whether performance varies substantially across batch or study strata.

```{r diagnostics-tables}
cal <- calibration_summary(fit_safe, bins = 10, min_bin_n = 5)
conf_tbl <- confounder_sensitivity(fit_safe,
                                   confounders = c("batch", "study"),
                                   metric = "auc",
                                   min_n = 3)

# Calibration metrics
cal$metrics

# Confounder sensitivity table (first 6 rows)
head(conf_tbl)
```


```{r plot-overlap}
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot_overlap_checks(fit_leaky, column = "subject")
  plot_overlap_checks(fit_safe, column = "subject")
} else {
  cat("ggplot2 not installed; skipping overlap plots.\n")
}
```

The overlap plots compare the number of unique subjects appearing in the training and test sets for each fold.

**Top plot (`fit_leaky`)**  
The red line shows high overlap counts, accompanied by the explicit *“WARNING: Overlaps detected!”* annotation. This confirms that the same subjects appear in both the training and test sets, indicating information leakage.

**Bottom plot (`fit_safe`)**  
The overlap line remains flat at zero across all folds. This confirms that `subject_grouped` splitting successfully keeps subjects isolated, ensuring that no subject appears in both sets simultaneously.


## Audit leakage with `audit_leakage()`

`audit_leakage()` combines four diagnostics in one object:

- permutation gap: tests whether model signal is non-random
- batch or study association with folds (chi-square and Cramers V)
- target leakage scan: feature-wise outcome association to flag proxy columns
- duplicate and near-duplicate detection across train/test (by default) using feature similarity

Assumptions and misuse to avoid:

- `coldata` (or stored split metadata) must align with prediction IDs; rownames
  or a `row_id` column help alignment
- `X_ref` must align to prediction IDs (rownames, `row_id`, or matching order)
- `plot_perm_distribution()` requires `return_perm = TRUE`
- `target_threshold` should be set high enough to flag only strong proxies
- target leakage scan includes univariate associations plus an optional
  multivariate/interaction check (`target_scan_multivariate`, `*_components`,
  `*_interactions`, `*_B`); proxies outside `X_ref` can still pass undetected
- for rsample splits, set `perm_mode` (or `bioLeak_perm_mode`) so restricted
  permutations respect the intended design
- for time-series audits, ensure a valid time column and set `time_block` /
  `block_len` as needed

**Interpretation Note:** The label-permutation test refits models by default when
refit inputs are available (`perm_refit = "auto"` with `store_refit_data = TRUE`)
and `B <= perm_refit_auto_max`. Otherwise it keeps predictions fixed, so the
p-value quantifies prediction-label association rather than a full refit null.
A large gap indicates a non-random association. To determine if that signal is
*real* or *leaked*, check the **Batch Association** (confounding),
**Target Leakage Scan** (proxy features), and **Duplicate Detection**
(memorization) tables. Use `perm_refit = FALSE` to force fixed-prediction
shuffles or `perm_refit = TRUE` with `perm_refit_spec` to always refit.

Use `feature_space = "rank"` to compare samples by rank profiles, and
`sim_method` (`cosine` or `pearson`) to control similarity. For large `n`, `nn_k`
and `max_pairs` limit duplicate searches. Use `duplicate_scope = "all"` to
include within-fold duplicates (data-quality checks). Use `ci_method = "if"`
(influence-function) or `ci_method = "bootstrap"` with `boot_B` to obtain a
confidence interval for the permutation gap; `include_z` controls whether a
z-score is reported. Set `perm_stratify = "auto"` to stratify permutations only
when outcomes support it.

**Leakage-aware audit**

```{r audit-leakage}
X_ref <- df[, predictors]
X_ref[c(1, 5), ] <- X_ref[1, ]

audit <- audit_leakage(
  fit_safe,
  metric = "auc",
  B = 20,
  perm_stratify = TRUE,
  batch_cols = c("batch", "study"),
  X_ref = X_ref,
  sim_method = "cosine",
  sim_threshold = 0.995,
  return_perm = TRUE
)

cat("Leakage audit summary:\n")
summary(audit)
if (!is.null(audit@permutation_gap) && nrow(audit@permutation_gap) > 0) {
  # Permutation significance results
  audit@permutation_gap
}
if (!is.null(audit@batch_assoc) && nrow(audit@batch_assoc) > 0) {
  # Batch/study association with folds (Cramer's V)
  audit@batch_assoc
} else {
  cat("No batch or study associations detected.\n")
}
if (!is.null(audit@target_assoc) && nrow(audit@target_assoc) > 0) {
  # Top features by target association score
  head(audit@target_assoc)
} else {
  cat("No target leakage scan results available.\n")
}
if (!is.null(audit@duplicates) && nrow(audit@duplicates) > 0) {
  # Top duplicate/near-duplicate pairs by similarity
  head(audit@duplicates)
} else {
  cat("No near-duplicates detected.\n")
}
```

The permutation table reports the observed metric, the mean under random label permutation, the gap (difference), and a permutation *p*-value. For metrics where higher values indicate better performance, larger gaps reflect stronger non-random signal.

The batch association table reports chi-square statistics and Cramer's V. Large
p-values and small V values indicate that folds are not aligned with batch or
study labels (which is the desired outcome when these should be independent).

The target scan table lists features with the strongest associations with the
outcome. For numeric features, the score is \(|\mathrm{AUC} - 0.5| \times 2\) for
classification or the absolute correlation for regression. For categorical
features, the score is Cramér’s V or eta-squared. Scores closer to 1 indicate
stronger outcome association.

The duplicate table lists pairs of samples with near-identical profiles that
cross train/test folds (by default). In this setup, the artificially duplicated
pair (`X_ref[c(1, 5), ] <- X_ref[1, ]`) should appear near the top of the list.
Use `duplicate_scope = "all"` to include within-fold duplicates.

```{r plot-perm}
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot_perm_distribution(audit)
} else {
  cat("ggplot2 not installed; skipping permutation plot.\n")
}
```

The histogram shows the null distribution (gray bars) of the performance metric under random label permutation.

The blue dashed line represents the average performance of a random model (the permuted mean). The red solid line represents the observed performance of the fitted model.

A genuine signal is indicated when the red line lies well to the right of the
gray distribution; overlap suggests weak or unstable signal.


**Audit per learner**

```{r audit-by-learner}
if (requireNamespace("ranger", quietly = TRUE) && 
    requireNamespace("parsnip", quietly = TRUE)) {
    
    # 1. Define specs
    # Standard GLM (no tuning)
    spec_glm <- parsnip::logistic_reg() |> 
        parsnip::set_engine("glm")
    
    # Random Forest
    spec_rf <- parsnip::rand_forest(
        mode = "classification",
        trees = 100
    ) |>
        parsnip::set_engine("ranger")
    
    # 2. Fit using the current split object
    fit_multi <- fit_resample(
        df,
        outcome = "outcome",
        splits = nested_splits,
        learner = list(glm = spec_glm, rf = spec_rf),
        metrics = "auc"
    )
    
    # 3. Run the audit
    audits <- audit_leakage_by_learner(fit_multi, metric = "auc", B = 20)
    cat("Per-learner audit summary:\n")
    print(audits)
    
} else {
    cat("ranger/parsnip not installed.\n")
}
```

This example uses **ranger** for the random forest specification; if it is not installed, the code chunk is skipped.

Use `parallel_learners = TRUE` to audit learners concurrently when **future.apply** is available.

The printed table summarizes each learner’s observed metric, permutation gap,
p-value, and key batch/duplicate summaries. Use it to compare signal strength
across models while checking for leakage risks. Pass `learners =` to audit a
subset, or `mc.cores =` to control parallel workers.


**HTML audit report**

`audit_report()` accepts either a `LeakAudit` or a `LeakFit` object. When a
`LeakFit` is provided, it first runs `audit_leakage()` and then forwards any
additional arguments to the audit step. If multiple learners were fit, pass
`learner =` via `...` to select one. Use `open = TRUE` to open the report in a
browser after rendering.

```{r audit-report, eval = FALSE}
if (requireNamespace("rmarkdown", quietly = TRUE) && rmarkdown::pandoc_available()) {
  report_path <- audit_report(audit, output_dir = ".")
  cat("HTML report written to:\n", report_path, "\n")
} else {
  cat("rmarkdown or pandoc not available; skipping audit report rendering.\n")
}
```

The report path points to a standalone HTML file containing the same audit tables and plots, suitable for sharing with collaborators or archiving as a quality control record.

## Time-series leakage checks

Time-series data require special handling. Random splits can leak information
from the future into the past. Use `mode = "time_series"` with a prediction
`horizon`, and audit with block permutations. Choose `time_block = "circular"`
or `"stationary"`; when `block_len` is NULL, the audit uses a default block
length (~10% of the test block size, minimum 5).

**Leaky example: lookahead feature**

```{r time-series-fit}
time_splits <- make_split_plan(
  df_time,
  outcome = "outcome",
  mode = "time_series",
  time = "time",
  v = 4,
  horizon = 1
)

cat("Time-series splits summary:\n")
time_splits

fit_time_leaky <- fit_resample(
  df_time,
  outcome = "outcome",
  splits = time_splits,
  learner = spec,
  metrics = "auc"
)

cat("Time-series leaky fit summary:\n")
summary(fit_time_leaky)
```

Time-series splitting trains on growing windows, so performance can differ from
standard CV simply because early folds have smaller training sets. Regardless of
the score, the presence of the `leak_future` feature makes this estimate
methodologically invalid.

**Leakage-safe alternative: remove lookahead and audit with blocks**

```{r time-series-safe}
df_time_safe <- df_time
df_time_safe$leak_future <- NULL

fit_time_safe <- fit_resample(
  df_time_safe,
  outcome = "outcome",
  splits = time_splits,
  learner = spec,
  metrics = "auc"
)

cat("Time-series safe fit summary:\n")
summary(fit_time_safe)

audit_time <- audit_leakage(
  fit_time_safe,
  metric = "auc",
  B = 20,
  time_block = "stationary",
  block_len = 5
)

cat("Time-series leakage audit summary:\n")
summary(audit_time)
if (!is.null(audit_time@permutation_gap) && nrow(audit_time@permutation_gap) > 0) {
  # Time-series permutation significance results
  audit_time@permutation_gap
}

if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot_time_acf(fit_time_safe, lag.max = 20)
} else {
  cat("ggplot2 not installed; skipping ACF plot.\n")
}
```

The safe fit summary provides the leakage-resistant performance estimate. Compare
leaky vs. safe fits to gauge inflation risk; `features_final` should drop when
the lookahead feature is removed.

The ACF plot shows the autocorrelation of out-of-fold predictions by lag. The
dashed red lines represent the 95% confidence interval for white noise; large
bars outside the bands indicate residual temporal dependence. `plot_time_acf()`
requires numeric predictions and time metadata aligned to the fit.

# Parallel Processing

`bioLeak` uses the `future` framework for parallelism (Windows, macOS, Linux).
`fit_resample()`, `audit_leakage()`, `audit_leakage_by_learner()`, and
`simulate_leakage_suite()` honor the active plan when `parallel = TRUE` (or
`parallel_learners = TRUE`).

```{r parallel-setup, eval=FALSE}
library(future)

# Use multiple cores (works on all OS)
plan(multisession, workers = 4)

# Run a heavy simulation
sim <- simulate_leakage_suite(..., parallel = TRUE)

# Parallel folds or audits
# fit_resample(..., parallel = TRUE)
# audit_leakage(..., parallel = TRUE)
# audit_leakage_by_learner(..., parallel_learners = TRUE)

# Return to sequential processing
plan(sequential)
```

This chunk only configures the parallel plan, so it does not produce a result
object. Use it as a template before running compute-heavy functions.

## Simulation suite

`simulate_leakage_suite()` runs Monte Carlo simulations that inject specific
leakage mechanisms and then evaluates detection with the audit pipeline. It is
useful for validating your leakage checks before applying them to real data.
Available leakage types include `none`, `subject_overlap`, `batch_confounded`,
`peek_norm`, and `lookahead`. Use `signal_strength`, `prevalence`, `rho`, `K`,
`repeats`, `horizon`, and `preprocess` to control the simulation. The output is
a `LeakSimResults` data frame with one row per seed. Metrics are selected
automatically based on outcome type (AUC for binary classification).

```{r simulate-suite}
if (requireNamespace("glmnet", quietly = TRUE)) {
  sim <- simulate_leakage_suite(
    n = 80,
    p = 6,
    mode = "subject_grouped",
    learner = "glmnet",
    leakage = "subject_overlap",
    seeds = 1:2,
    B = 20,
    parallel = FALSE,
    signal_strength = 1
  )
  
  # Simulation results (first 6 rows)
  head(sim)
} else {
  cat("glmnet not installed; skipping simulation suite example.\n")
}
```

Each row corresponds to one simulation seed.

- `metric_obs` is the observed performance (AUC for this simulation). Higher
  values suggest stronger apparent signal, which can be inflated by leakage.

- `gap` and `p_value` indicate that the leaked signal is statistically distinguishable from random noise.

Use `metric_obs` to gauge the magnitude of the leakage effect, and `gap` to assess detection sensitivity.


## Objects and summaries

bioLeak uses S4 classes and list-like results to capture provenance and diagnostics:

- `LeakSplits`: returned by `make_split_plan()`, printed with `show()`
- `LeakFit`: returned by `fit_resample()`, summarized with `summary()`
  includes fold diagnostics in `fit@info$fold_status`
- `LeakAudit`: returned by `audit_leakage()`, summarized with `summary()`
- `LeakAuditList`: returned by `audit_leakage_by_learner()`, printed directly
- `GuardFit`: returned by `.guard_fit()`, printed and summarized
- `LeakImpute`: returned by `impute_guarded()` (guarded data + diagnostics)
- `LeakTune`: returned by `tune_resample()` (nested tuning summaries)
  includes `fold_status`, selected parameters, and optional `final_model` /
  `final_workflow` when `refit = TRUE`
- `LeakSimResults`: returned by `simulate_leakage_suite()` (per-seed results)

These objects store hashes and metadata that help reproduce and audit the
workflow. Inspect their slots (`@metrics`, `@predictions`, `@permutation_gap`,
`@duplicates`) to drill into specific leakage signals.
