# Sensitivity analysis

Sensitivity analysis quantifies how changes in an input affect the model output. In risk analysis, it can be used to identify which uncertain inputs are most important for a decision-relevant output. This helps to understand the model behaviour before running it with real data, to prioritise where to intervene to reduce risk, and to increase efforts to reduce uncertainty in the most sensitive inputs.

In `mcmodule`, you can perform sensitivity analysis using different methods:

- Scenario (“what-if”) analysis: Compares output changes under targeted input variations, practical for model runs using real data. It does not produce sensitivity indices, but it can be useful for complex models where the most important inputs depend on context.
- Correlation (tornado) analysis: Ranking of influential inputs by correlating each input uncertainty with output uncertainty across existing runs. Only captures inputs that are uncertain in those runs. It can be applied to multivariate inputs, but interpretation can be more complex when correlations vary across variates.
- Sample design based sensitivity analysis: Evaluates the model on a predefined input design (e.g., Morris/Sobol) to estimate sensitivity over a chosen input space using packages such as `sensitivity`(Iooss et al. 2025) or `sensobol` (Puy et al. 2022)and yield sensitivity indices. In `mcmodule`, this method is currently limited to one variate.

# Scenario analysis

Scenario analysis (or “what-if analysis”) is a non-statistical but still useful approach to assess input sensitivity. It does not produce sensitivity indices, but it will show how the output changes when you make specific changes to the inputs. This makes it a practical way to compare targeted input changes using real input data. It is especially helpful for complex models where the most important inputs depend on context (for example, when estimating disease-entry risk across countries, where certain pathways will be more important in some countries than in others). Scenario analysis can therefore help you identify the most sensitive inputs for a particular use case. This is covered in the working with what-if scenarios section.

# Correlation analysis

`mcmodule_corr()` ranks inputs by their correlation with a chosen output across the current model runs. It is an extension of `mc2d::tornado()` from `mc2d` (Pouillot and Delignette-Muller 2010) for `mcmodule` objects and multiple variates.

Unlike methods that require a sample design, this approach works directly on an `mcmodule` built from model input data. Note that `mcmodule_corr()` only computes correlations for uncertain inputs (stochastic nodes built with `func`) and, when `variates_as_nsv = TRUE`, also for multivariate inputs.

When we correlate specific model input data distributions with outputs, the particular input distributions used in each variate do not necessarily capture the full input sample space. As a result, these correlations indicate where output uncertainty “comes from”, rather than which inputs the output is most sensitive to. A weak correlation does not necessarily mean an input is unimportant (e.g., symmetric non-linear effects can cancel out), and a strong correlation may reflect confounding with other correlated inputs.

Here we compute inputs-output correlations using Spearman coefficient:

    # Calculate output (expected number of animals not detected)
    imports_mcmodule_corr <- trial_totals(
      imports_mcmodule,
      mc_names = c("no_detect"),
      trials_n = "animals_n",
      subsets_n = "farms_n",
      subsets_p = "h_prev",
      mctable = imports_mctable
    )

    # Calculate correlations using Spearman's method (default)
    corr_results <- mcmodule_corr(imports_mcmodule_corr, output = "no_detect_set_n", method = "spearman")
    #>
    #> === Correlation Analysis Summary ===
    #>
    #> Analysis Parameters:
    #> - Analysis type: Global output
    #> - Output node: no_detect_set_n
    #> - Correlation method(s): spearman
    #> - Missing value handling: all.obs
    #>
    #> Expression Information:
    #> - Module: mcmodule
    #>   - Expression: imports
    #>     - Input nodes: w_prev, test_sensi, animals_n, h_prev
    #>     - Variates analyzed: 6
    #>
    #> Results Summary:
    #> - Total correlations calculated: 24
    #> - Top 4 most correlated inputs (by absolute mean correlation):
    #>   1. h_prev: 0.7304
    #>   2. w_prev: 0.4559
    #>   3. animals_n: 0.2893
    #>   4. test_sensi: -0.1532
    #>
    #> Input Correlation Strength Distribution:
    #> - Very strong: 2 (8.3%)
    #> - Strong: 4 (16.7%)
    #> - Moderate: 7 (29.2%)
    #> - Weak: 5 (20.8%)
    #> - Very weak/None: 6 (25.0%)
    #>
    #> Inputs by Correlation Strength:
    #> - Very strong: h_prev
    #> - Strong: w_prev, h_prev, test_sensi
    #> - Moderate: animals_n, h_prev, w_prev
    #> - Weak: w_prev, animals_n, test_sensi
    #> - Very weak/None: test_sensi, animals_n

In this example, `h_prev` is the main driver of `no_detect_set_n` (very strong positive association). `w_prev` and `animals_n` also show positive correlation, while `test_sensi` is close to zero or slightly negative across variates.

    # View correlation results
    head(corr_results)
    #>       exp exp_n variate          output      input       value   method     use
    #> 1 imports     1       1 no_detect_set_n     w_prev  0.69460226 spearman all.obs
    #> 2 imports     1       1 no_detect_set_n test_sensi  0.01160619 spearman all.obs
    #> 3 imports     1       1 no_detect_set_n  animals_n  0.50959070 spearman all.obs
    #> 4 imports     1       1 no_detect_set_n     h_prev  0.55260115 spearman all.obs
    #> 5 imports     1       2 no_detect_set_n     w_prev  0.25395671 spearman all.obs
    #> 6 imports     1       2 no_detect_set_n test_sensi -0.03559191 spearman all.obs
    #>     module pathogen origin       strength
    #> 1 mcmodule        a   nord         Strong
    #> 2 mcmodule        a   nord Very weak/None
    #> 3 mcmodule        a   nord       Moderate
    #> 4 mcmodule        a   nord       Moderate
    #> 5 mcmodule        a  south           Weak
    #> 6 mcmodule        a  south Very weak/None

Then visualise the ranking with a tornado plot. It can be customised with standard `ggplot2` layers.

    library(ggplot2)
    #> Warning: package 'ggplot2' was built under R version 4.5.3

    # Plot tornado plot of correlations
    mcmodule_tornado(imports_mcmodule_corr, output = "no_detect_set_n", method = "spearman", print_summary = FALSE)

Each point in the plot represents one input variate. The coloured point marks the variate with the largest absolute correlation for that input, and that value is used to rank the inputs. The black point shows the median correlation across variates, which is often a useful summary when correlations vary across the model. The black horizontal line shows the full range of correlations across variates, so you can see whether an input is consistently influential or only influential in certain variates.

Note that `farms_n` and `test_origin` are not shown in this analysis because they are fixed in the original model data and therefore have zero correlation with the output. This is an important limitation of correlation-based methods using real input data: they can only identify influential inputs that vary in the original model runs. To explore uncertainty for fixed inputs, you need a method that evaluates a sample design.

# Sensitivity analysis with sample design

## Create a sample design

An input sample space is the allowable range of values it can take (or distribution definition). A sample design is a specific set of sampled input combinations used to run the model. The sample space defines where values can come from, while the sample design defines which values are actually used in model runs.

A `sample_design` in `mcmodule` can be either:

- a matrix/data frame, or
- a sensitivity object from the `sensitivity::sensitivity` package (for example, a Morris design object) that has an `X` element containing the sampling matrix.

The sampling matrix must have one column per input node and one row per sample. Each row is one scenario in the mcnode uncertainty dimension. When you provide a sample_design, that uncertainty dimension is no longer sampled from the original model inputs; it is mapped to the values in your design. Currently, sample_design supports one variate.

You can pass the sampling matrix (or sensitivity object) directly to `eval_module()`, or set it as a global default with `set_sample_design()`. Using a global design ensures the same sampling matrix is available across all model steps, which is convenient when you run sensitivity analysis after building a complex model. You can also pass the design to each function call, but that is more verbose, and you must remove it later to run the model with the original data inputs.

    # Create a sample design as a data frame
    X <- data.frame(
      w_prev = runif(10000, 0.15, 0.6),
      h_prev = runif(10000, 0.02, 0.7),
      test_sensi = runif(10000, 0.8, 0.91),
      animals_n = sample(5:10, 10000, replace = TRUE),
      farms_n = sample(82:176, 10000, replace = TRUE),
      test_origin = runif(10000, 0, 1)
    )

    dim(X)
    #> [1] 10000     6
    head(X)
    #>      w_prev    h_prev test_sensi animals_n farms_n test_origin
    #> 1 0.3340396 0.6117728  0.8477135         7     129  0.44193520
    #> 2 0.5473578 0.2434982  0.8176573         6     163  0.03386385
    #> 3 0.5732103 0.1054768  0.8905329         6     157  0.63026555
    #> 4 0.1705004 0.2622306  0.8228900        10     133  0.15496632
    #> 5 0.3876475 0.6528754  0.8313463         8     117  0.66649531
    #> 6 0.5515886 0.6151214  0.8600830         8     109  0.82409581

    # Set global sample design
    set_sample_design(X)
    #> sample_design set to X

    # Retrieve current global sample design
    current_design <- set_sample_design()

    # Evaluate the module with the global sample design
    imports_mcmodule_X <- eval_module(
      exp = imports_exp
    )
    #> imports_exp evaluated
    #> mcmodule created (expressions: imports_exp)

    # Calculate output (expected number of animals not detected)
    imports_mcmodule_X <- trial_totals(
      imports_mcmodule_X,
      mc_names = c("no_detect"),
      trials_n = "animals_n",
      subsets_n = "farms_n",
      subsets_p = "h_prev"
    )

    mcmodule_tornado(imports_mcmodule_X, output = "no_detect_set_n", method = "spearman")
    #>
    #> === Correlation Analysis Summary ===
    #>
    #> Analysis Parameters:
    #> - Analysis type: Global output
    #> - Output node: no_detect_set_n
    #> - Correlation method(s): spearman
    #> - Missing value handling: all.obs
    #>
    #> Expression Information:
    #> - Module: mcmodule
    #>   - Expression: imports_exp
    #>     - Input nodes: w_prev, test_origin, test_sensi, animals_n, farms_n, h_prev
    #>     - Variates analyzed: 1
    #>
    #> Results Summary:
    #> - Total correlations calculated: 6
    #> - Top 5 most correlated inputs (by absolute mean correlation):
    #>   1. h_prev: 0.6786
    #>   2. test_origin: -0.4723
    #>   3. w_prev: 0.3440
    #>   4. animals_n: 0.2187
    #>   5. farms_n: 0.2075
    #>
    #> Input Correlation Strength Distribution:
    #> - Very strong: 0 (0.0%)
    #> - Strong: 1 (16.7%)
    #> - Moderate: 1 (16.7%)
    #> - Weak: 3 (50.0%)
    #> - Very weak/None: 1 (16.7%)
    #>
    #> Inputs by Correlation Strength:
    #> - Strong: h_prev
    #> - Moderate: test_origin
    #> - Weak: w_prev, animals_n, farms_n
    #> - Very weak/None: test_sensi

    # Reset global sample design
    reset_sample_design()
    #> sample_design reset

This sample design produces a different ranking from the first tornado analysis, because we’re using the inputs sampling space rather than the original model inputs. Here, `h_prev` is the main positive driver of `no_detect_set_n`. `test_origin` is the next most influential input and is negatively associated with the output. `w_prev`, `animals_n` and `farms_n` have smaller positive effects, while `test_sensi` is weak or negligible within these bounds.

## Define sample space in `mctable`

For methods that use a sample design, `mctable` can be used to define how inputs are sampled. In particular, `mctable$sample_space` stores the bounds or distribution parameters used to generate sensitivity samples. To read more about `mctable` see the package vignette.

    imports_mctable[,c("mcnode","mc_func", "sample_space")]
    #>            mcnode mc_func                        sample_space
    #> 1          h_prev   runif               min = 0.02, max = 0.7
    #> 2          w_prev   runif               min = 0.15, max = 0.6
    #> 3      test_sensi   rpert min = 0.8, mode = 0.875, max = 0.91
    #> 4         farms_n    <NA>                   min = 5, max = 10
    #> 5       animals_n   rnorm                 min = 82, max = 176
    #> 6 test_origin_unk    <NA>                                <NA>
    #> 7     test_origin    <NA>                    min = 0, max = 1

Two helper functions support common sampling designs from `mctable`:

- `mctable_bounds()` for bound (min and max) extraction, for Morris and related approaches.
- `mctable_sobol_matrices()` for Sobol design matrices.

Currently, `mcmodule` only provides direct conversion from `mctable` to Sobol sampling designs. However, you can use `mctable_bounds()` to create many other sample designs using different approaches. The only requirement is that your sample design matrix contains columns for all the input nodes in your `mcmodule` model.

## Morris elementary effects

Morris (Morris 1991) is another screening method. It identifies influential inputs and potential nonlinearity or interactions at moderate computational cost

Start by getting parameter bounds from `mctable`.

    # Get bounds for Morris sampling design
    b <- mctable_bounds(imports_mctable, transformation = FALSE)

Build the Morris design with `sensitivity::morris()`. The resulting object can be passed directly as `sample_design`. The arguments `factors`, `r`, and `design` control the the factors (inputs) sampled, the number of repetitions of the design, and the step size between sampled values, respectively. The arguments `binf` and `bsup` provide the bounds for each input, and `scale = TRUE` scales the inputs to [0,1] for better comparability of Morris indices across inputs with different ranges.

To have stable Morris indices in large sample designs (with many inputs), you need to run the model with a large number of repetitions, which can be computationally intensive. Always check the sensitivity indices stability in diferent runs before interpreting them, increase r until rankings stabilise.

    library(sensitivity)
    #> Warning: package 'sensitivity' was built under R version 4.5.2
    #> Registered S3 method overwritten by 'sensitivity':
    #>   method    from
    #>   print.src dplyr
    #>
    #> Attaching package: 'sensitivity'
    #> The following object is masked from 'package:dplyr':
    #>
    #>     src

    # Create Morris design
    morris_sa <- sensitivity::morris(
      model = NULL,
      factors = b$factors,
      r = 2000,
      design = list(type = "oat", levels = 4, grid.jump = 2),
      binf = b$binf,
      bsup = b$bsup,
      scale = TRUE
    )
    #> Warning in sensitivity::morris(model = NULL, factors = b$factors, r = 2000, :
    #> keeping 1999 repetitions out of 2000

    # Evaluate the module with that design.
    imports_mcmodule_morris <- eval_module(
      exp = imports_exp,
      sample_design = morris_sa,
      mctable = imports_mctable
    )
    #> imports_exp evaluated
    #> mcmodule created (expressions: imports_exp)

    # Calculate output (expected number of animals not detected)
    imports_mcmodule_morris <- trial_totals(
      imports_mcmodule_morris,
      mc_names = c("no_detect"),
      trials_n = "animals_n",
      subsets_n = "farms_n",
      subsets_p = "h_prev",
      sample_design = morris_sa,
      mctable = imports_mctable
    )

Finally, extract the output vector and estimate Morris indices. The `tell()` function updates the Morris object with the model output, and then you can print and plot the indices.

    # Extract the output vector and estimate Morris indices.
    y <- unmc(imports_mcmodule_morris$node_list$no_detect_set_n$mcnode)

    # Aggregated output used for sensitivity analysis.
    sensitivity::tell(morris_sa, y)

    # Print Morris indices
    morris_sa
    #>
    #> Call:
    #> sensitivity::morris(model = NULL, factors = b$factors, r = 2000,     design = list(type = "oat", levels = 4, grid.jump = 2), binf = b$binf,     bsup = b$bsup, scale = TRUE)
    #>
    #> Model runs: 13993
    #>                      mu    mu.star     sigma
    #> h_prev       139.808877 139.808877 124.93784
    #> w_prev        89.096448  89.096448  98.97369
    #> test_sensi    -7.335186   7.335186  10.79865
    #> farms_n       50.032335  50.032335  61.29715
    #> animals_n     55.904818  55.904818  69.23723
    #> test_origin -111.438795 111.438795 110.73603

    # Plot Morris indices (mu.star and sigma)
    plot(morris_sa)

In the Morris method, `mu` ( \(\mu\) ) is the mean of the elementary effects across trajectories and `mu.star` ( \(\mu^*\) ) is the absolute value of `mu`, which summarises each input’s overall importance (larger values indicate a stronger average influence on the output, regardless of direction). `h_prev` has the largest `mu.star`, making it the most influential input overall, followed by `test_origin`. `w_prev` is the next most important, while `farms_n` and `animals_n` have more moderate effects, and `test_sensi` is negligible within the explored bounds (note that the uncertainty is small, 0.875-0.91). `sigma` ( \(\sigma\) ) is the standard deviation of the elementary effects. Large `sigma` values indicate that the effect of an input varies substantially across the sampled space, which typically reflects nonlinearity and/or interactions with other inputs.

## Sobol indices

Sobol analysis Saltelli (2002) decomposes output variance into first-order and higher-order effects, providing a global sensitivity analysis. It is more computationally intensive than Morris, but it gives a more complete picture of input importance, including interactions. Sobol variance decomposition assumes independent inputs, if inputs are dependent, interpret Sobol indices with care or consider other approaches.

First, generate Sobol sampling matrices from `mctable`.

`mctable_sobol_matrices()` creates Sobol design matrices from your `mctable` definitions.

    library(sensobol)

    N <- 10000
    X <- mctable_sobol_matrices(imports_mctable, N = N, order = "second")

Evaluate the module, compute the target output as before.

    imports_mcmodule_sobol <- eval_module(
      exp = imports_exp,
      data = NULL,
      sample_design = X,
      mctable = imports_mctable
    )
    #> imports_exp evaluated
    #> mcmodule created (expressions: imports_exp)

    imports_mcmodule_sobol <- trial_totals(
      mcmodule = imports_mcmodule_sobol,
      mc_names = c("no_detect"),
      trials_n = "animals_n",
      subsets_n = "farms_n",
      subsets_p = "h_prev",
      sample_design = X,
      mctable = imports_mctable
    )

Then compute first-order (`Si`) and total-order (`Ti`) Sobol indices and print and plot the results.

    y <- unmc(imports_mcmodule_sobol$node_list$no_detect_set_n$mcnode)

    # Compute Sobol indices
    sobol_sa <- sensobol::sobol_indices(Y = y, N = N, params = colnames(X), order = "second", boot = TRUE, R = 1000)

    sobol_sa
    #>
    #> First-order estimator: saltelli | Total-order estimator: jansen
    #>
    #> Total number of model runs: 230000
    #>
    #> Sum of first order indices: 0.7854101
    #>          original          bias    std.error        low.ci      high.ci
    #>             <num>         <num>        <num>         <num>        <num>
    #>  1:  3.332841e-01  2.806764e-04 1.504341e-02  0.3035188778 0.3624879705
    #>  2:  1.377370e-01  1.157053e-04 1.010515e-02  0.1178155716 0.1574270136
    #>  3:  3.909247e-04  8.930736e-06 5.250725e-04 -0.0006471292 0.0014111171
    #>  4:  4.305921e-02 -2.409696e-04 5.966784e-03  0.0316054992 0.0549948618
    #>  5:  4.684154e-02  2.136317e-04 6.030061e-03  0.0348092104 0.0584466142
    #>  6:  2.240974e-01 -1.665205e-04 1.286802e-02  0.1990430331 0.2494847426
    #>  7:  4.907211e-01  5.028705e-04 1.220140e-02  0.4663039437 0.5141325541
    #>  8:  2.289120e-01  1.739086e-04 6.448966e-03  0.2160983631 0.2413778446
    #>  9:  7.520003e-04  1.397912e-06 3.137228e-05  0.0006891139 0.0008120909
    #> 10:  7.602997e-02  7.511655e-05 2.189468e-03  0.0716635768 0.0802461326
    #> 11:  9.089470e-02  2.177806e-05 2.819738e-03  0.0853463316 0.0963995033
    #> 12:  3.490372e-01  1.310243e-04 9.342170e-03  0.3305958773 0.3672165122
    #> 13:  4.242323e-02 -3.018290e-04 7.306883e-03  0.0284038338 0.0570462888
    #> 14:  2.276340e-04 -1.191114e-05 4.058860e-04 -0.0005559768 0.0010350670
    #> 15:  1.158978e-02 -1.230634e-04 4.097145e-03  0.0036825872 0.0197430993
    #> 16:  1.559113e-02 -6.954566e-05 4.275403e-03  0.0072810375 0.0240403091
    #> 17:  6.621890e-02 -3.633257e-04 8.412995e-03  0.0500930632 0.0830713962
    #> 18:  1.986587e-05  7.138970e-07 2.632880e-04 -0.0004968831 0.0005351870
    #> 19:  4.869421e-03 -1.850100e-05 2.874968e-03 -0.0007469115 0.0105227557
    #> 20:  5.898327e-03  5.734532e-05 2.902983e-03  0.0001512393 0.0115307250
    #> 21:  3.005418e-02  3.219238e-05 6.185212e-03  0.0178991922 0.0421447759
    #> 22: -5.842551e-06 -2.641477e-07 1.364565e-04 -0.0002730283 0.0002618715
    #> 23:  4.855472e-05  8.737696e-06 1.659606e-04 -0.0002854598 0.0003650939
    #> 24: -1.751435e-05 -1.019615e-05 3.833124e-04 -0.0007585966 0.0007439603
    #> 25:  2.403328e-03  2.678811e-05 1.661811e-03 -0.0008805490 0.0056336294
    #> 26:  7.210102e-03 -1.289130e-04 3.450360e-03  0.0005764332 0.0141015960
    #> 27:  8.240894e-03 -3.590833e-05 3.802195e-03  0.0008246376 0.0157289665
    #>          original          bias    std.error        low.ci      high.ci
    #>             <num>         <num>        <num>         <num>        <num>
    #>     sensitivity             parameters
    #>          <char>                 <char>
    #>  1:          Si                 h_prev
    #>  2:          Si                 w_prev
    #>  3:          Si             test_sensi
    #>  4:          Si                farms_n
    #>  5:          Si              animals_n
    #>  6:          Si            test_origin
    #>  7:          Ti                 h_prev
    #>  8:          Ti                 w_prev
    #>  9:          Ti             test_sensi
    #> 10:          Ti                farms_n
    #> 11:          Ti              animals_n
    #> 12:          Ti            test_origin
    #> 13:         Sij          h_prev.w_prev
    #> 14:         Sij      h_prev.test_sensi
    #> 15:         Sij         h_prev.farms_n
    #> 16:         Sij       h_prev.animals_n
    #> 17:         Sij     h_prev.test_origin
    #> 18:         Sij      w_prev.test_sensi
    #> 19:         Sij         w_prev.farms_n
    #> 20:         Sij       w_prev.animals_n
    #> 21:         Sij     w_prev.test_origin
    #> 22:         Sij     test_sensi.farms_n
    #> 23:         Sij   test_sensi.animals_n
    #> 24:         Sij test_sensi.test_origin
    #> 25:         Sij      farms_n.animals_n
    #> 26:         Sij    farms_n.test_origin
    #> 27:         Sij  animals_n.test_origin
    #>     sensitivity             parameters
    #>          <char>                 <char>

    plot(sobol_sa)

    plot(sobol_sa, order = "second")

First-order indices (`Si`) measure each input’s main effect, the share of output variance attributable to changing that input alone, averaged over uncertainty in all other inputs. Larger `Si` values indicate the model output is more sensitive to that factor. Here, `h_prev` is the dominant driver of variance, `test_origin` is the next most influential, and `w_prev` also contributes materially; `farms_n` and `animals_n` have smaller but non-zero effects, while `test_sensi` is negligible. Negative estimates can occur due to Monte Carlo error and should be interpreted as ~0 if the confidence interval overlaps 0. If important factors have wide CIs or many negatives appear, increase N (and/or bootstrap replicates) before interpreting small effects.

Total-order indices (`Ti`) measure each input’s total effect on output variance, including its main effect plus all interaction effects with other inputs. `Ti` is therefore always at least as large as `Si` for the same parameter.

Second-order indices (`Sij`) quantify interaction effects. They measure how much variance is explained only by varying two inputs together beyond what their individual first-order effects (`Si`) explain. Values near 0 indicate little interaction; larger values indicate the combined effect of the pair is important (e.g., `h_prev.test_origin` suggests a meaningful interaction between `h_prev` and `test_origin`).

## Other methods

You can use any method to analyse the importance of your inputs as long as you can build a table/matrix of input values to run the model with, and collect the corresponding model output(s). For example, you could use `sensitivity::fast99()` or Shapley values (`iml::shapley()`). The best method will depend on your model, computational resources, and the specific questions you want to answer.

# References

Iooss, Bertrand, Sebastien Da Veiga, Alexandre Janon, and Gilles Pujol. 2025. “Sensitivity: Global Sensitivity Analysis of Model Outputs and Importance Measures.” https://doi.org/10.32614/CRAN.package.sensitivity.
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Puy, Arnald, Samuele Lo Piano, Andrea Saltelli, and Simon A. Levin. 2022. “Sensobol: An r Package to Compute Variance-Based Sensitivity Indices” 102. https://doi.org/10.18637/jss.v102.i05.
Saltelli, Andrea. 2002. “Making Best Use of Model Evaluations to Compute Sensitivity Indices.” Computer Physics Communications 145 (2): 280297. https://doi.org/10.1016/S0010-4655(02)00280-1.
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