# Comparison with Alternatives

#### Gilles Colling

#### 2026-01-08

## Overview

This vignette compares corrselect’s graph-theoretic approach to four established methods for multicollinearity management and variable selection:

1. caret::findCorrelation() - Greedy correlation-based pruning
2. Boruta - Random forest permutation importance
3. glmnet - L1/L2 regularization (LASSO/Ridge)
4. Manual VIF removal - Iterative variance inflation factor thresholding

Each comparison examines algorithmic differences, performance characteristics, and appropriate use cases. Evaluations use the `bioclim_example` dataset (19 bioclimatic variables, \(n = 100\)).

See `vignette("theory")` for mathematical foundations. See `vignette("quickstart")` for usage examples.

## Evaluation Dataset

    data(bioclim_example)
    predictors <- bioclim_example[, -1]  # Exclude response
    response <- bioclim_example[, 1]

    cat("Variables:", ncol(predictors), "\n")
    #> Variables: 19
    cat("Observations:", nrow(predictors), "\n")
    #> Observations: 100
    cat("Response: species_richness (continuous)\n")
    #> Response: species_richness (continuous)

    cor_matrix <- cor(predictors)

    # Correlation heatmap
    col_pal <- colorRampPalette(c("#3B4992", "white", "#EE0000"))(100)

    par(mar = c(1, 1, 3, 1))
    nc <- ncol(cor_matrix)
    nr <- nrow(cor_matrix)
    image(seq_len(nc), seq_len(nr), t(cor_matrix[nr:1, ]),
          col = col_pal,
          xlab = "", ylab = "", axes = FALSE,
          main = "Bioclimatic Variable Correlations (p = 19)",
          zlim = c(-1, 1))
    axis(1, at = seq_len(nc), labels = colnames(cor_matrix), las = 2, cex.axis = 0.7)
    axis(2, at = nc:1, labels = colnames(cor_matrix), las = 2, cex.axis = 0.7)

    for (i in seq_len(nc)) {
      for (j in seq_len(nr)) {
        text_col <- if (abs(cor_matrix[j, i]) > 0.6) "white" else "black"
        text(i, nr - j + 1, sprintf("%.2f", cor_matrix[j, i]),
             cex = 0.5, col = text_col)
      }
    }

Block structure present: correlations range from -0.15 to 0.97.

## Comparison 1: caret::findCorrelation()

### Method

caret’s `findCorrelation()` applies greedy iterative removal:

1. Identify pair with maximum \(|r_{ij}|\)
2. Remove variable with larger mean absolute correlation
3. Repeat until all \(|r_{ij}| < \tau\)

Non-deterministic: results depend on internal ordering. Typically removes more variables than graph-theoretic methods.

### Execution

    if (requireNamespace("caret", quietly = TRUE)) {
      # Apply caret's greedy algorithm
      to_remove_caret <- caret::findCorrelation(cor_matrix, cutoff = 0.7)
      result_caret <- predictors[, -to_remove_caret]

      cat("caret results:\n")
      cat("  Variables retained:", ncol(result_caret), "\n")
      cat("  Variables removed:", length(to_remove_caret), "\n")
      cat("  Removed:", paste(colnames(predictors)[to_remove_caret], collapse = ", "), "\n")
    }
    #> caret results:
    #>   Variables retained: 10
    #>   Variables removed: 9
    #>   Removed: BIO8, BIO7, BIO5, BIO4, BIO3, BIO9, BIO1, BIO11, BIO15

    # Apply corrselect (exact mode)
    result_corrselect <- corrPrune(predictors, threshold = 0.7, mode = "exact")

    cat("\ncorrselect results:\n")
    #>
    #> corrselect results:
    cat("  Variables retained:", ncol(result_corrselect), "\n")
    #>   Variables retained: 12
    cat("  Variables removed:", length(attr(result_corrselect, "removed_vars")), "\n")
    #>   Variables removed: 7
    cat("  Removed:", paste(attr(result_corrselect, "removed_vars"), collapse = ", "), "\n")
    #>   Removed: BIO2, BIO4, BIO5, BIO7, BIO8, BIO10, BIO15

corrselect retains more variables (\(|S_{\text{corrselect}}| \ge |S_{\text{caret}}|\)) via maximal clique enumeration while satisfying identical threshold constraint.

### Distribution Comparison

    # Extract correlations
    cor_orig <- cor(predictors)
    cor_corrselect <- cor(result_corrselect)

    if (requireNamespace("caret", quietly = TRUE)) {
      cor_caret <- cor(result_caret)

      # Overlaid histogram comparing all three
      hist(abs(cor_orig[upper.tri(cor_orig)]),
           breaks = 30,
           main = "Distribution of Absolute Correlations",
           xlab = "Absolute Correlation",
           col = rgb(0.5, 0.5, 0.5, 0.4),
           xlim = c(0, 1))

      hist(abs(cor_caret[upper.tri(cor_caret)]),
           breaks = 30,
           col = rgb(0.8, 0.2, 0.2, 0.4),
           add = TRUE)

      hist(abs(cor_corrselect[upper.tri(cor_corrselect)]),
           breaks = 30,
           col = rgb(0.2, 0.5, 0.8, 0.4),
           add = TRUE)

      abline(v = 0.7, col = "black", lwd = 2, lty = 2)

      legend("topright",
             legend = c(
               paste0("Original (", ncol(predictors), " vars)"),
               paste0("caret (", ncol(result_caret), " vars)"),
               paste0("corrselect (", ncol(result_corrselect), " vars)"),
               "Threshold"
             ),
             fill   = c(
               rgb(0.5, 0.5, 0.5, 0.4),
               rgb(0.8, 0.2, 0.2, 0.4),
               rgb(0.2, 0.5, 0.8, 0.4),
               NA
             ),
             border = c("white", "white", "white", NA),
             lty    = c(NA, NA, NA, 2),
             lwd    = c(NA, NA, NA, 2),
             col    = c(NA, NA, NA, "black"),
             bty    = "o",
             bg = "white")
    }

### Comparison

Feature | caret | corrselect
--- | --- | ---
Algorithm | Greedy iterative removal | Maximal clique enumeration
Optimality | Heuristic | Exact (mode = “exact”)
Reproducibility | Non-deterministic | Deterministic
Variables retained | \(\le\) optimal | Maximal
Forced variables | No | Yes (`force_in`)
Mixed data | No | Yes (`assocSelect`)
Complexity | \(O(p^2)\) | \(O(p^2)\) greedy, \(O(3^{p/3})\) exact

### Applications

caret: Exploratory analysis, non-critical reproducibility requirements.

corrselect: Reproducible research, maximal variable retention, forced variable constraints, mixed data types.

## Comparison 2: Boruta

### Method

Boruta tests variable importance via random forest permutation:

1. Create shadow features (permuted copies)
2. Fit random forest on original + shadow features
3. Test: \(\text{importance}(X_i) > \max(\text{importance}(\text{shadow}))\)
4. Iteratively confirm/reject until convergence

Orthogonal objective: Boruta selects predictive variables (supervised). corrselect removes redundant variables (unsupervised).

### Execution

    if (requireNamespace("Boruta", quietly = TRUE)) {
      # Boruta: "Which variables predict species_richness?"
      set.seed(123)
      boruta_result <- Boruta::Boruta(
        species_richness ~ .,
        data    = bioclim_example,
        maxRuns = 100
      )

      cat("Boruta variable importance screening:\n")
      print(table(boruta_result$finalDecision))

      important_vars <- names(boruta_result$finalDecision[
        boruta_result$finalDecision == "Confirmed"
      ])

      cat("\n  Confirmed predictors:", length(important_vars), "\n")
      cat(" ", paste(important_vars, collapse = ", "), "\n")
    }
    #> Boruta variable importance screening:
    #>
    #> Tentative Confirmed  Rejected
    #>         0         6        13
    #>
    #>   Confirmed predictors: 6
    #>   BIO12, BIO13, BIO14, BIO15, BIO16, BIO17

    # corrselect: "Which variables are redundant?"
    corrselect_result <- corrPrune(predictors, threshold = 0.7)

    cat("\ncorrselect multicollinearity pruning:\n")
    #>
    #> corrselect multicollinearity pruning:
    cat("  Non-redundant variables:", ncol(corrselect_result), "\n")
    #>   Non-redundant variables: 12
    cat(" ", paste(names(corrselect_result), collapse = ", "), "\n")
    #>   BIO1, BIO3, BIO6, BIO9, BIO11, BIO12, BIO13, BIO14, BIO16, BIO17, BIO18, BIO19

Different variable sets selected: Boruta optimizes prediction; corrselect minimizes redundancy.

### Comparison

Criterion | Boruta | corrselect
--- | --- | ---
Objective | Predictive power | Redundancy removal
Criterion | Permutation importance | \(\|r_{ij}\| < \tau\)
Response | Required | Not required
Multicollinearity | Indirect | Direct
Stochastic | Yes | No
Complexity | High (\(\ge 100\) forests) | Low (graph)

### Sequential Application

    # Stage 1: Correlation-based pruning
    data_pruned <- corrPrune(raw_data, threshold = 0.7)

    # Stage 2: Importance testing
    boruta_result <- Boruta::Boruta(response ~ ., data = cbind(response, data_pruned))
    final_vars <- names(boruta_result$finalDecision[
      boruta_result$finalDecision == "Confirmed"
    ])

    # Stage 3: Final model
    final_model <- lm(response ~ ., data = cbind(response, data_pruned)[, c("response", final_vars)])

Stage 1 removes redundancy (reproducible). Stage 2 tests importance (stochastic). Stage 3 fits model with non-redundant, predictive variables.

### Applications

Boruta: Prediction-focused analysis with response variable.

corrselect: Multicollinearity removal, exploratory analysis without response, reproducible selection.

Sequential: High-dimensional correlated data requiring both redundancy removal and importance testing.

## Comparison 3: glmnet (LASSO/Ridge)

### Method

glmnet minimizes regularized loss:

\[ \min_{\beta} \frac{1}{2n} \|y - X\beta\|_2^2 + \lambda \left[\alpha \|\beta\|_1 + (1-\alpha) \|\beta\|_2^2\right] \]

- \(\alpha = 1\): LASSO (L1 penalty, sparse \(\beta\))
- \(\alpha = 0\): Ridge (L2 penalty, shrinkage)
- \(\lambda\): Cross-validation selected

Difference: glmnet performs soft selection (shrinkage) optimizing prediction. corrselect performs hard selection (removal) based on correlation structure.

### Execution

    if (requireNamespace("glmnet", quietly = TRUE)) {
      # Fit LASSO with cross-validation
      X <- as.matrix(predictors)
      y <- response

      set.seed(123)
      cv_lasso <- glmnet::cv.glmnet(X, y, alpha = 1)

      # Extract non-zero coefficients at lambda.1se (conservative choice)
      coef_lasso <- stats::coef(cv_lasso, s = "lambda.1se")
      selected_lasso <- rownames(coef_lasso)[coef_lasso[, 1] != 0][-1]  # Remove intercept

      cat("glmnet (LASSO, λ = lambda.1se):\n")
      cat("  Variables retained:", length(selected_lasso), "\n")
      cat(" ", paste(selected_lasso, collapse = ", "), "\n")
    }
    #> glmnet (LASSO, λ = lambda.1se):
    #>   Variables retained: 4
    #>   BIO1, BIO12, BIO15, BIO16

    if (requireNamespace("glmnet", quietly = TRUE)) {
      # Compare model performance
      model_glmnet <- lm(species_richness ~ .,
                         data = bioclim_example[, c("species_richness", selected_lasso)])

      model_corrselect <- lm(species_richness ~ .,
                             data = cbind(species_richness = response, result_corrselect))

      cat("\nModel comparison (OLS on selected variables):\n")
      cat("  glmnet:     R² =", round(summary(model_glmnet)$r.squared, 3),
          "with", length(selected_lasso), "predictors\n")
      cat("  corrselect: R² =", round(summary(model_corrselect)$r.squared, 3),
          "with", ncol(result_corrselect), "predictors\n")
    }
    #>
    #> Model comparison (OLS on selected variables):
    #>   glmnet:     R² = 0.983 with 4 predictors
    #>   corrselect: R² = 0.909 with 12 predictors

glmnet selects fewer variables (\(|S_{\text{glmnet}}| \le |S_{\text{corrselect}}|\)) optimizing prediction. corrselect maximizes retention under correlation constraint.

### Coefficient Comparison

    if (requireNamespace("glmnet", quietly = TRUE)) {
      par(mfrow = c(1, 2), mar = c(8, 4, 3, 2))

      # glmnet coefficients (shrinkage)
      coef_vals <- coef_lasso[coef_lasso[, 1] != 0, ][-1]
      barplot(sort(abs(coef_vals), decreasing = TRUE),
              las = 2,
              main = "glmnet: Shrunk Coefficients",
              ylab = "Absolute Coefficient Value",
              col = "salmon",
              cex.names = 0.7)

      # corrselect: unbiased OLS coefficients
      coef_corrselect <- coef(model_corrselect)[-1]  # Remove intercept
      barplot(sort(abs(coef_corrselect), decreasing = TRUE),
              las = 2,
              main = "corrselect: Unbiased OLS Coefficients",
              ylab = "Absolute Coefficient Value",
              col = rgb(0.2, 0.5, 0.8, 0.7),
              cex.names = 0.7)
    }

Left: L1 penalty shrinks coefficients toward zero (biased). Right: OLS on pruned variables (unbiased). glmnet optimizes prediction with shrinkage. corrselect preserves effect sizes.

### Comparison

Criterion | glmnet | corrselect
--- | --- | ---
Objective | Prediction accuracy | Multicollinearity removal
Selection | Soft (shrinkage) | Hard (removal)
Coefficient bias | Yes (L1/L2) | No
Multicollinearity | Regularization | Pruning
Response | Required | Not required
Tuning | \(\lambda\) (cross-validation) | \(\tau\) (user-specified)
Interpretability | Shrunk effects | Direct effects

### Applications

glmnet: Prediction-focused, high-dimensional (\(p > n\)), accepts biased coefficients.

corrselect: Interpretable coefficients, exploratory analysis, explicit correlation constraint, unregularized modeling.

Sequential: Correlation pruning (corrselect) followed by sparse prediction (glmnet).

## Comparison 4: modelPrune() vs Manual VIF Removal

### Method

Variance Inflation Factor quantifies predictor multicollinearity:

\[ \text{VIF}_j = \frac{1}{1 - R^2_j} \]

where \(R^2_j\) results from regressing \(X_j\) on remaining predictors. Thresholds:

- VIF < 5: Low collinearity
- VIF < 10: Moderate (acceptable)
- VIF ≥ 10: High (problematic)

Manual approach: iteratively remove max(VIF) until all VIF < threshold.

### Manual Implementation

    # Manual iterative VIF removal
    manual_vif_removal <- function(formula, data, threshold = 5, max_iter = 10) {
      require(car)

      # Get response variable name
      response_var <- all.vars(formula)[1]

      # Get predictor names (handles ~ . notation)
      model <- lm(formula, data = data)
      current_vars <- names(coef(model))[-1]  # Exclude intercept

      removed_vars <- character(0)
      vif_vals <- car::vif(model)

      while (max(vif_vals) > threshold && length(current_vars) > 1 && length(removed_vars) < max_iter) {
        # Remove variable with highest VIF
        var_to_remove <- names(which.max(vif_vals))
        removed_vars <- c(removed_vars, var_to_remove)
        cat("Iteration", length(removed_vars), ": Removing", var_to_remove,
            "(VIF =", round(max(vif_vals), 2), ")\n")

        # Update variable list and refit
        current_vars <- setdiff(current_vars, var_to_remove)
        new_formula <- as.formula(paste(response_var, "~", paste(current_vars, collapse = " + ")))
        model <- lm(new_formula, data = data)
        vif_vals <- car::vif(model)
      }

      list(model = model, iterations = length(removed_vars),
           vif = vif_vals, removed = removed_vars, converged = max(vif_vals) <= threshold)
    }

    # Run manual VIF removal
    if (requireNamespace("car", quietly = TRUE)) {
      cat("Manual VIF removal (iterative):\n")
      manual_result <- manual_vif_removal(species_richness ~ ., data = bioclim_example, threshold = 5)
      cat("\nVariables kept:", length(manual_result$vif), "\n")
      if (!manual_result$converged) {
        cat("(Stopped at max_iter = 10; VIF threshold not yet reached)\n")
      }
    }
    #> Manual VIF removal (iterative):
    #> Loading required package: car
    #> Loading required package: carData
    #> Iteration 1 : Removing BIO2 (VIF = 5.83 )
    #> Iteration 2 : Removing BIO7 (VIF = 5.66 )
    #> Iteration 3 : Removing BIO5 (VIF = 5.03 )
    #>
    #> Variables kept: 16

## modelPrune() Comparison

    # Run modelPrune
    modelprune_result <- modelPrune(species_richness ~ ., data = bioclim_example, limit = 5)

    cat("\nmodelPrune results:\n")
    #>
    #> modelPrune results:
    cat("Variables removed:", attr(modelprune_result, "removed_vars"), "\n")
    #> Variables removed: BIO2 BIO7 BIO5
    cat("Variables kept:", length(attr(modelprune_result, "selected_vars")), "\n")
    #> Variables kept: 16

    # Extract final model
    final_model <- attr(modelprune_result, "final_model")
    if (requireNamespace("car", quietly = TRUE)) {
      cat("\nFinal VIF values:\n")
      print(round(car::vif(final_model), 2))
    }
    #>
    #> Final VIF values:
    #>  BIO1  BIO3  BIO4  BIO6  BIO8  BIO9 BIO10 BIO11 BIO12 BIO13 BIO14 BIO15 BIO16
    #>  2.09  3.68  3.81  2.57  4.03  4.27  4.96  3.06  1.76  2.51  3.11  3.01  2.43
    #> BIO17 BIO18 BIO19
    #>  2.88  2.82  1.70

## Visual: VIF Comparison

    if (requireNamespace("car", quietly = TRUE)) {
      # Compute VIF for original model
      model_full <- lm(species_richness ~ ., data = bioclim_example)
      vif_before <- car::vif(model_full)

      # VIF after modelPrune
      vif_after <- car::vif(final_model)

      # Combined barplot
      par(mar = c(8, 4, 4, 2))
      all_vars <- unique(c(names(vif_before), names(vif_after)))
      vif_combined <- data.frame(
        before = vif_before[match(all_vars, names(vif_before))],
        after = vif_after[match(all_vars, names(vif_after))]
      )
      vif_combined[is.na(vif_combined)] <- 0
      vif_combined <- vif_combined[order(vif_combined$before, decreasing = TRUE), ]

      # Show top 15
      n_show <- min(15, nrow(vif_combined))
      barplot(t(as.matrix(vif_combined[1:n_show, ])),
              beside = TRUE,
              las = 2,
              main = "VIF Before and After modelPrune()",
              ylab = "VIF",
              col = c(rgb(0.8, 0.2, 0.2, 0.7), rgb(0.2, 0.5, 0.8, 0.7)),
              cex.names = 0.6,
              names.arg = rownames(vif_combined)[1:n_show])
      abline(h = 5, col = "black", lwd = 2, lty = 2)
      legend("topright",
             legend = c("Before", "After", "Limit = 5"),
             fill   = c(rgb(0.8, 0.2, 0.2, 0.7), rgb(0.2, 0.5, 0.8, 0.7), NA),
             border = c("white", "white", NA),
             lty    = c(NA, NA, 2),
             lwd    = c(NA, NA, 2),
             col    = c(NA, NA, "black"),
             bty    = "o",
             bg = "white")
    }

### Comparison

Criterion | Manual VIF | modelPrune()
--- | --- | ---
Algorithm | Iterative greedy | Graph-based
Automation | Manual | Automated
Optimality | Heuristic | Maximal subset
Forced variables | Manual exclusion | `force_in`
Output | Verbose log | Summary

### Applications

Manual VIF: Educational use, diagnostic understanding, legacy workflows.

modelPrune(): Production pipelines, forced variable constraints, reproducible documentation.

## Summary

### Method Selection

Goal | Primary Method | Alternative
--- | --- | ---
Redundancy removal (unsupervised) | corrPrune() | caret::findCorrelation()
VIF reduction (regression) | modelPrune() | Manual VIF
Predictive variable selection | Boruta | RF importance
Prediction accuracy | glmnet | Elastic net
Mixed data types | assocSelect() | Manual metrics
Forced variable constraints | corrselect (`force_in`) | N/A
Exploratory (fast) | caret | corrPrune (greedy)

### corrselect Distinguishing Features

1. Maximal clique enumeration: Optimal retention under \(|r_{ij}| < \tau\) constraint
2. Deterministic: ELS and Bron-Kerbosch algorithms guarantee reproducibility
3. Flexible: Forced variables, mixed types, greedy/exact modes
4. Unbiased estimates: Hard removal preserves coefficient interpretability
5. Model-agnostic: Correlation-based preprocessing

### Integrated Workflow

    # Correlation pruning
    data_pruned <- corrPrune(raw_data, threshold = 0.7)

    # VIF refinement
    model_data <- modelPrune(response ~ ., data = data_pruned, limit = 5)

    # Importance testing (optional)
    if (requireNamespace("Boruta", quietly = TRUE)) {
      boruta_result <- Boruta::Boruta(response ~ ., data = model_data)
      important_vars <- names(boruta_result$finalDecision[
        boruta_result$finalDecision == "Confirmed"
      ])
    }

    # Final model: OLS (interpretable) or glmnet (prediction)
    final_model <- lm(response ~ ., data = model_data[, c("response", important_vars)])

## References

- caret: Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1-26. doi:10.18637/jss.v028.i05

- Boruta: Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. Journal of Statistical Software, 36(11), 1-13. doi:10.18637/jss.v036.i11

- glmnet: Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22. doi:10.18637/jss.v033.i01

- VIF: Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley. doi:10.1002/0471725153

## See Also

- `vignette("quickstart")` - Interface overview and usage examples
- `vignette("workflows")` - Domain-specific workflows (genomics, ecology, surveys)
- `vignette("advanced")` - Algorithm selection and performance tuning
- `vignette("theory")` - Graph-theoretic foundations and formal proofs

## Session Info

    sessionInfo()
    #> R version 4.5.2 (2025-10-31 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows 11 x64 (build 26200)
    #>
    #> Matrix products: default
    #>   LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] LC_COLLATE=C
    #> [2] LC_CTYPE=English_United States.utf8
    #> [3] LC_MONETARY=English_United States.utf8
    #> [4] LC_NUMERIC=C
    #> [5] LC_TIME=English_United States.utf8
    #>
    #> time zone: Europe/Luxembourg
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] car_3.1-3            carData_3.0-5        microbenchmark_1.5.0
    #> [4] corrselect_3.1.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] shape_1.4.6.1        gtable_0.3.6         xfun_0.53
    #>  [4] bslib_0.9.0          ggplot2_4.0.0        recipes_1.3.1
    #>  [7] lattice_0.22-7       vctrs_0.6.5          tools_4.5.2
    #> [10] generics_0.1.4       stats4_4.5.2         parallel_4.5.2
    #> [13] tibble_3.3.0         pkgconfig_2.0.3      ModelMetrics_1.2.2.2
    #> [16] Matrix_1.7-4         data.table_1.18.0    RColorBrewer_1.1-3
    #> [19] S7_0.2.0             lifecycle_1.0.4      compiler_4.5.2
    #> [22] farver_2.1.2         stringr_1.5.2        textshaping_1.0.3
    #> [25] codetools_0.2-20     Boruta_9.0.0         htmltools_0.5.8.1
    #> [28] class_7.3-23         sass_0.4.10          glmnet_4.1-10
    #> [31] yaml_2.3.10          Formula_1.2-5        prodlim_2025.04.28
    #> [34] pillar_1.11.1        jquerylib_0.1.4      MASS_7.3-65
    #> [37] cachem_1.1.0         gower_1.0.2          iterators_1.0.14
    #> [40] abind_1.4-8          rpart_4.1.24         foreach_1.5.2
    #> [43] nlme_3.1-168         parallelly_1.45.1    lava_1.8.2
    #> [46] tidyselect_1.2.1     digest_0.6.37        stringi_1.8.7
    #> [49] future_1.67.0        dplyr_1.1.4          reshape2_1.4.4
    #> [52] purrr_1.2.0          listenv_0.9.1        splines_4.5.2
    #> [55] fastmap_1.2.0        grid_4.5.2           cli_3.6.5
    #> [58] magrittr_2.0.4       survival_3.8-3       future.apply_1.20.0
    #> [61] withr_3.0.2          scales_1.4.0         lubridate_1.9.4
    #> [64] timechange_0.3.0     rmarkdown_2.30       globals_0.18.0
    #> [67] nnet_7.3-20          timeDate_4051.111    ranger_0.17.0
    #> [70] evaluate_1.0.5       knitr_1.50           hardhat_1.4.2
    #> [73] caret_7.0-1          rlang_1.1.6          Rcpp_1.1.0
    #> [76] glue_1.8.0           pROC_1.19.0.1        ipred_0.9-15
    #> [79] svglite_2.2.2        jsonlite_2.0.0       R6_2.6.1
    #> [82] plyr_1.8.9           systemfonts_1.3.1