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    ConfidenceInterval

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    pub(all) struct ConfidenceInterval {
      estimate : Double
      lower : Double
      upper : Double
      confidence_level : Double
      sample_count : Int
    } derive(Eq, ToJson, 
    Debug    )

    DesignSummary

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    pub(all) struct DesignSummary {
      runs : Int
      factors : Int
      center_points : Int
      blocks : Int
      completed : Int
      response_mean : Double
      response_range : Double
    } derive(Eq, ToJson, 
    Debug    )

    DeterministicRng

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    pub(all) struct DeterministicRng {
      state : Int64
    } derive(Eq, ToJson, 
    Debug    )

    A reproducible pseudo-random generator for uncertainty studies.

    DeterministicRng::integer

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    DeterministicRng::new

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    fn DeterministicRng::new(seed : Int64) -> DeterministicRng

    DeterministicRng::next

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    fn DeterministicRng::next(self : DeterministicRng) -> (DeterministicRng, Double)

    DeterministicRng::normal_approx

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    fn DeterministicRng::normal_approx(self : DeterministicRng, mean~ : Double, standard_deviation~ : Double) -> (DeterministicRng, Double) raise 
    ExtractionError

    DeterministicRng::triangular

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    fn DeterministicRng::triangular(self : DeterministicRng, lower~ : Double, mode~ : Double, upper~ : Double) -> (DeterministicRng, Double) raise 
    ExtractionError

    DeterministicRng::uniform

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    Distribution

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    pub(all) enum Distribution {
      Uniform(lower~ : Double, upper~ : Double)
      Triangular(lower~ : Double, mode~ : Double, upper~ : Double)
      Normal(mean~ : Double, standard_deviation~ : Double)
    } derive(Eq, ToJson, 
    Debug    )

    ExperimentRun

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    pub(all) struct ExperimentRun {
      run_number : Int
      block : Int
      coded_values : Array[Double]
      actual_values : Array[Double]
      center_point : Bool
      response : Double?
    } derive(Eq, ToJson, 
    Debug    )

    FactorDefinition

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    pub(all) struct FactorDefinition {
      name : String
      low : Double
      high : Double
      center : Double
    } derive(Eq, ToJson, 
    Debug    )

    A named factor for a laboratory or process experiment.

    FactorDefinition::code

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    fn FactorDefinition::code(self : FactorDefinition, actual : Double) -> Double

    FactorDefinition::new

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    FactorDefinition::scale

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    fn FactorDefinition::scale(self : FactorDefinition, coded : Double) -> Double

    MonteCarloResult

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    pub(all) struct MonteCarloResult {
      seed : Int64
      outputs : Array[Double]
      summary : SampleSummary
      input_names : Array[String]
      input_samples : Array[Array[Double]]
    } derive(Eq, ToJson, 
    Debug    )

    Range

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    pub(all) struct Range {
      lower : Double
      upper : Double
    } derive(Eq, ToJson, 
    Debug    )

    Reproducible design-of-experiments and robust statistics for process studies.

    Range::new

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    fn Range::new(lower~ : Double, upper~ : Double) -> Range raise 
    ExtractionError

    RecoveryUncertainty

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    pub(all) struct RecoveryUncertainty {
      nominal : Double
      summary : SampleSummary
      lower_recovery : Double
      upper_recovery : Double
      failure_probability : Double
    } derive(Eq, ToJson, 
    Debug    )

    ReliabilityCurvePoint

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    pub(all) struct ReliabilityCurvePoint {
      threshold : Double
      pass_probability : Double
      failure_probability : Double
      sample_count : Int
    } derive(Eq, ToJson, 
    Debug    )

    RiskObservation

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    pub(all) struct RiskObservation {
      scenario : String
      outcome : Double
      threshold : Double
      weight : Double
      pass : Bool
    } derive(Eq, ToJson, 
    Debug    )

    Probability-of-success and risk reporting primitives for design studies.

    RiskSummary

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    pub(all) struct RiskSummary {
      observations : Int
      weighted_pass_probability : Double
      mean_outcome : Double
      lower_outcome : Double
      upper_outcome : Double
      expected_shortfall : Double
      worst_scenario : String
      risk_level : String
    } derive(Eq, ToJson, 
    Debug    )

    SampleSummary

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    pub(all) struct SampleSummary {
      count : Int
      minimum : Double
      q05 : Double
      q25 : Double
      median : Double
      q75 : Double
      q95 : Double
      maximum : Double
      mean : Double
      standard_deviation : Double
    } derive(Eq, ToJson, 
    Debug    )

    ScenarioMatrix

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    pub(all) struct ScenarioMatrix {
      factor_names : Array[String]
      rows : Array[ScenarioRow]
      response_mean : Double
      response_minimum : Double
      response_maximum : Double
      feasible_fraction : Double
    } derive(Eq, ToJson, 
    Debug    )

    ScenarioRow

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    pub(all) struct ScenarioRow {
      index : Int
      name : String
      factors : Array[Double]
      response : Double
      weight : Double
      feasible : Bool
    } derive(Eq, ToJson, 
    Debug    )

    ScenarioSummary

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    pub(all) struct ScenarioSummary {
      factor : String
      low_response : Double
      high_response : Double
      response_change : Double
      elasticity : Double
      limiting_level : Double
    } derive(Eq, ToJson, 
    Debug    )

    Statistics

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    pub(all) struct Statistics {
      count : Int
      minimum : Double
      q25 : Double
      median : Double
      q75 : Double
      maximum : Double
      mean : Double
      standard_deviation : Double
    } derive(Eq, ToJson, 
    Debug    )

    UncertainInput

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    pub(all) struct UncertainInput {
      name : String
      distribution : Distribution
    } derive(Eq, ToJson, 
    Debug    )

    assign_blocks

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    bootstrap_mean

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    fn bootstrap_mean(values : ArrayView[Double], replicates~ : Int, seed~ : Int64) -> Array[Double] raise 
    ExtractionError

    Bootstrap the mean of a measured output with deterministic resampling.

    bootstrap_mean_interval

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    fn bootstrap_mean_interval(values : ArrayView[Double], replicates~ : Int, confidence_level? : Double, seed? : Int64) -> ConfidenceInterval raise 
    ExtractionError

    cartesian_grid

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    center_point_variance

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    conditional_mean

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    fn conditional_mean(outcomes : ArrayView[Double], threshold : Double, above : Bool) -> Double

    conditional_value_at_risk

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    fn conditional_value_at_risk(outcomes : ArrayView[Double], probability : Double) -> Double raise 
    ExtractionError

    design_matrix

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    fn design_matrix(runs : ArrayView[ExperimentRun]) -> Array[Array[Double]]

    full_factorial

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    latin_hypercube

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    fn latin_hypercube(ranges : ArrayView[Range], samples : Int) -> Array[Array[Double]] raise 
    ExtractionError

    linspace

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    fn linspace(range : Range, count : Int) -> Array[Double] raise 
    ExtractionError

    make_risk_observation

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    fn make_risk_observation(scenario : String, outcome : Double, threshold : Double, weight : Double, higher_is_better : Bool) -> RiskObservation raise 
    ExtractionError

    make_scenario_matrix

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    fn make_scenario_matrix(factor_names : ArrayView[String], rows : ArrayView[ScenarioRow]) -> ScenarioMatrix

    monte_carlo

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    Run a deterministic Monte Carlo study. The callback receives one value per input.

    probability_above

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    fn probability_above(outcomes : ArrayView[Double], threshold : Double) -> Double

    probability_below

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    fn probability_below(outcomes : ArrayView[Double], threshold : Double) -> Double

    quantile

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    fn quantile(values : ArrayView[Double], probability : Double) -> Double raise 
    ExtractionError

    quasi_random_grid

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    fn quasi_random_grid(ranges : ArrayView[Range], count~ : Int) -> Array[Array[Double]] raise 
    ExtractionError

    radical_inverse

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    fn radical_inverse(index : Int, base : Int) -> Double raise 
    ExtractionError

    Generate a low-discrepancy one-dimensional sequence using radical inverse.

    randomize_runs

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    reliability_curve

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    fn reliability_curve(outcomes : ArrayView[Double], thresholds : ArrayView[Double], higher_is_better : Bool) -> Array[ReliabilityCurvePoint]

    reliability_curve_csv

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    fn reliability_curve_csv(curve : ArrayView[ReliabilityCurvePoint]) -> String

    risk_is_acceptable

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    fn risk_is_acceptable(summary : RiskSummary, minimum_probability : Double) -> Bool

    risk_level_from_probability

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    fn risk_level_from_probability(probability : Double) -> String

    risk_margin

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    fn risk_margin(observation : RiskObservation) -> Double

    risk_margin_vector

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    fn risk_margin_vector(observations : ArrayView[RiskObservation]) -> Array[Double]

    risk_summary_markdown

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    fn risk_summary_markdown(summary : RiskSummary) -> String

    scenario_matrix_alerts

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    fn scenario_matrix_alerts(matrix : ScenarioMatrix, minimum_feasible_fraction : Double) -> Array[String]

    scenario_matrix_best_row

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    fn scenario_matrix_best_row(matrix : ScenarioMatrix) -> ScenarioRow?

    scenario_matrix_bottom

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    fn scenario_matrix_bottom(matrix : ScenarioMatrix, count : Int) -> Array[ScenarioRow]

    scenario_matrix_confidence_width

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    fn scenario_matrix_confidence_width(matrix : ScenarioMatrix) -> Double

    scenario_matrix_csv

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    fn scenario_matrix_csv(matrix : ScenarioMatrix) -> String

    scenario_matrix_distance

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    fn scenario_matrix_distance(first : ScenarioMatrix, second : ScenarioMatrix) -> Double

    scenario_matrix_factor_count

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    fn scenario_matrix_factor_count(matrix : ScenarioMatrix) -> Int

    scenario_matrix_factor_mean

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    fn scenario_matrix_factor_mean(matrix : ScenarioMatrix, factor_index : Int) -> Double raise 
    ExtractionError

    scenario_matrix_factor_range

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    fn scenario_matrix_factor_range(matrix : ScenarioMatrix, factor_index : Int) -> (Double, Double) raise 
    ExtractionError

    scenario_matrix_factor_values

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    fn scenario_matrix_factor_values(matrix : ScenarioMatrix, factor_index : Int) -> Array[Double] raise 
    ExtractionError

    scenario_matrix_feasibility_vector

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    fn scenario_matrix_feasibility_vector(matrix : ScenarioMatrix) -> Array[Bool]

    scenario_matrix_feasible_rows

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    fn scenario_matrix_feasible_rows(matrix : ScenarioMatrix) -> Array[ScenarioRow]

    scenario_matrix_filter_factor

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    fn scenario_matrix_filter_factor(matrix : ScenarioMatrix, factor_index : Int, lower : Double, upper : Double) -> ScenarioMatrix raise 
    ExtractionError

    scenario_matrix_filter_feasible

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    fn scenario_matrix_filter_feasible(matrix : ScenarioMatrix, feasible : Bool) -> ScenarioMatrix

    scenario_matrix_infeasible_rows

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    fn scenario_matrix_infeasible_rows(matrix : ScenarioMatrix) -> Array[ScenarioRow]

    scenario_matrix_is_robust

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    fn scenario_matrix_is_robust(matrix : ScenarioMatrix, minimum_fraction : Double) -> Bool

    scenario_matrix_markdown

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    fn scenario_matrix_markdown(matrix : ScenarioMatrix) -> String

    scenario_matrix_merge

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    fn scenario_matrix_merge(first : ScenarioMatrix, second : ScenarioMatrix) -> ScenarioMatrix

    scenario_matrix_name_vector

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    fn scenario_matrix_name_vector(matrix : ScenarioMatrix) -> Array[String]

    scenario_matrix_pass_rate

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    fn scenario_matrix_pass_rate(matrix : ScenarioMatrix) -> Double

    scenario_matrix_response_correlation

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    fn scenario_matrix_response_correlation(matrix : ScenarioMatrix, factor_index : Int) -> Double raise 
    ExtractionError

    scenario_matrix_response_range

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    fn scenario_matrix_response_range(matrix : ScenarioMatrix) -> Double

    scenario_matrix_response_stddev

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    fn scenario_matrix_response_stddev(matrix : ScenarioMatrix) -> Double

    scenario_matrix_response_vector

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    fn scenario_matrix_response_vector(matrix : ScenarioMatrix) -> Array[Double]

    scenario_matrix_reweight

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    fn scenario_matrix_reweight(matrix : ScenarioMatrix, weights : ArrayView[Double]) -> ScenarioMatrix

    scenario_matrix_row_count

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    fn scenario_matrix_row_count(matrix : ScenarioMatrix) -> Int

    scenario_matrix_sort_by_response

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    fn scenario_matrix_sort_by_response(matrix : ScenarioMatrix, ascending : Bool) -> ScenarioMatrix

    scenario_matrix_summaries

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    fn scenario_matrix_summaries(matrix : ScenarioMatrix) -> Array[ScenarioSummary]

    scenario_matrix_top

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    fn scenario_matrix_top(matrix : ScenarioMatrix, count : Int) -> Array[ScenarioRow]

    scenario_matrix_values

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    fn scenario_matrix_values(matrix : ScenarioMatrix) -> Array[Double]

    scenario_matrix_weighted_feasibility

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    fn scenario_matrix_weighted_feasibility(matrix : ScenarioMatrix) -> Double

    scenario_matrix_weighted_response

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    fn scenario_matrix_weighted_response(matrix : ScenarioMatrix) -> Double

    scenario_matrix_worst_row

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    fn scenario_matrix_worst_row(matrix : ScenarioMatrix) -> ScenarioRow?

    scenario_risk_order

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    fn scenario_risk_order(observations : ArrayView[RiskObservation]) -> Array[String]

    scenario_row

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    fn scenario_row(index : Int, name : String, factors : ArrayView[Double], response : Double, weight : Double, feasible : Bool) -> ScenarioRow

    scenario_summary_markdown

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    fn scenario_summary_markdown(summaries : ArrayView[ScenarioSummary]) -> String

    set_response

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    single_stage_recovery_uncertainty

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    fn single_stage_recovery_uncertainty(feed_flow : UncertainInput, feed_concentration : UncertainInput, solvent_flow : UncertainInput, distribution : UncertainInput, sample_count~ : Int, seed~ : Int64, target_recovery~ : Double) -> RecoveryUncertainty raise 
    ExtractionError

    Propagate uncertain feed, solvent and distribution coefficient through one stage.

    standardized_sensitivity

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    fn standardized_sensitivity(inputs : ArrayView[Double], outputs : ArrayView[Double]) -> Double raise 
    ExtractionError

    Estimate rank-free standardized sensitivity from paired input/output samples.

    summarize

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    summarize_design

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    summarize_risk

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    fn summarize_risk(observations : ArrayView[RiskObservation], lower_probability : Double, upper_probability : Double) -> RiskSummary raise 
    ExtractionError

    summarize_samples

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    value_at_risk

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    fn value_at_risk(outcomes : ArrayView[Double], probability : Double) -> Double raise 
    ExtractionError

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