in index.html === acceptanceProportion ===

acceptanceProportion

Short Description: Acceptance Proportion
Long Description: An edge occurring in this proportion of individual FASK graphs will appear in the final graph.
Default Value: 0.5
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== addOriginalDataset ===

addOriginalDataset

Short Description: Yes, if adding the original dataset as another bootstrapping
Long Description: Select “Yes” here to include an extra run using the original dataset for improved accuracy.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== adjustOrientations ===

adjustOrientations

Short Description: Yes, if the orientation adjustment step should be included
Long Description: Yes, if the orientation adjustment step should be included
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== allowBidirected ===

allowBidirected

Short Description: Allow bidirected edges to show collider conflicts
Long Description: Allow bidirected edges to show collider conflicts
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== allowInternalRandomness ===

allowInternalRandomness

Short Description: Allow randomness inside algorithm
Long Description: This allows variables orders to be shuffled in certain sports to avoid local optima
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== alpha ===

alpha

Short Description: Cutoff for p values (alpha) (min = 0.0)
Long Description: The cutoff, beyond which test results are judged as dependent, for a statistical test of independence. Default 0.05. Higher alpha yields a sparser graph.
Default Value: 0.01
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== amBetaAlpha ===

amBetaAlpha

Short Description: The 'alpha' shape parameter for the Beta noise terms.
Long Description: The 'alpha' shape parameter for the Beta noise terms.
Default Value: 2
Lower Bound: 0
Upper Bound: Infinity
Value Type: Double

=== amBetaBeta ===

amBetaBeta

Short Description: The 'beta' shape parameter for the Beta noise terms.
Long Description: The 'beta' shape parameter for the Beta noise terms.
Default Value: 5
Lower Bound: 0
Upper Bound: Infinity
Value Type: Double

=== amCoefHigh ===

amCoefHigh

Short Description: High end of coefficient range (min = 0.0)
Long Description: Value m2 for coefficients drawn from U(-m2, -m1) U U(m1, m2).
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== amCoefLow ===

amCoefLow

Short Description: Low end of coefficient range (min = 0.0)
Long Description: The parameter m1 for coefficients drawn from U(-m2, -m1) U U(m1, m2).
Default Value: 0.2
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== amCoefSymmetric ===

amCoefSymmetric

Short Description: Yes if negative coefficient values should be considered
Long Description: Yes if coefficients should be drawn from +/-(a, b); No if from +(a, b).
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== amDerivativeMax ===

amDerivativeMax

Short Description: 'Max' for the U(min, max) range for random derivative values (with f(0) = 0)
Long Description: 'Max' for the U(min, max) range for random derivative values (with f(0) = 0)
Default Value: 1
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== amDerivativeMin ===

amDerivativeMin

Short Description: 'Min' for the U(min, max) range for random derivative values (with f(0) = 0)
Long Description: 'Min' for the U(min, max) range for random derivative values (with f(0) = 0)
Default Value: -1
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== amDistortionType ===

amDistortionType

Short Description: Add distortion: 1 = Before noise (additive) or 2 = After noise (post-nonlinear)
Long Description: Add distortion: 1 = Before noise (additive) or 2 = After noise (post-nonlinear)
Default Value: 1
Lower Bound: 1
Upper Bound: 2
Value Type: Integer

=== amEnsureInvertibility ===

amEnsureInvertibility

Short Description: Ensure that functions are invertible
Long Description: id="amEnsureInvertibility_short_desc"> Ensure that functions are invertible
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== amFirstDerivMax ===

amFirstDerivMax

Short Description: 'Max' for the U(min, max) range for f'(0) for the causal function
Long Description: 'Max' for the U(min, max) range for f'(0) for the causal function
Default Value: 1.0
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== amFirstDerivMin ===

amFirstDerivMin

Short Description: 'Min' for the U(min, max) range for f'(0) for the causal function
Long Description: 'Min' for the U(min, max) range for f'(0) for the causal function
Default Value: -1.0
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== amNumPostNonlinearFunctions ===

amNumPostNonlinearFunctions

Short Description: The number of random post-nonlinear functions to choose from
Long Description: The number of random post-nonlinear functions to choose from
Default Value: 3
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== amRescaleMax ===

amRescaleMax

Short Description: Variables will be rescaled to [min, max] for this max; if min = max no rescaling will be done
Long Description: Variables will be rescaled to [min, max] for this max; if min = max no rescaling will be done
Default Value: 1
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== amRescaleMin ===

amRescaleMin

Short Description: Variables will be rescaled to [min, max] for this min; if min = max no rescaling will be done
Long Description: Variables will be rescaled to [min, max] for this min; if min = max no rescaling will be done
Default Value: 1
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== amTaylorSeriesDegree ===

amTaylorSeriesDegree

Short Description: The maximum exponent for a Taylor series to use as a random post-nonlinear function
Long Description: The maximum exponent for a Taylor series to use as a random post-nonlinear function. The f(0) term is set to 0.
Default Value: 10
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== anmNoiseKind ===

anmNoiseKind

Short Description: Noise distribution family: 1 = Beta (skewed), 2 = Gaussian, 3 = Student-t (heavy-tailed)
Long Description: Selects the distribution of exogenous noise for the ANM simulator Options: 1 = Beta (skewed), 2 = Gaussian, 3 = Student-t (heavy-tailed).
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== anmNoiseStrength ===

anmNoiseStrength

Short Description: Controls variance/strength of noise in ANM simulator
Long Description: A slider in [0,1] that scales the standard deviation of the chosen noise distribution. Low values yield weak noise, high values yield stronger noise. For Student-t, this also interacts with degrees of freedom (heavier tails at higher strength).
Default Value: 0.4
Lower Bound: 0.0
Upper Bound: 10.0
Value Type: Double

=== anmNonlinearity ===

anmNonlinearity

Short Description: Controls strength of nonlinearity in ANM simulator
Long Description: A slider in [0,1] that simultaneously controls the number of basis units per edge and their amplitude. Low values produce nearly linear functions, high values produce strongly nonlinear functions.
Default Value: 0.6
Lower Bound: 0.0
Upper Bound: 10.0
Value Type: Double

=== anmPreset ===

anmPreset

Short Description: Preset function family: 1 = Smooth RBF, 2 = Wavy RBF, 3 = Tanh, 4 = Polynomial
Long Description: Selects the base family of nonlinear functions used on each edge for the ANM simulator. Options: 1 = Smooth RBF (gentle), 2 = Wavy RBF (richer), 3 = Tanh (sigmoidal), 4 = Polynomial (low-degree).
Default Value: 2
Lower Bound: 1
Upper Bound: 4
Value Type: Integer

=== applyR1 ===

applyR1

Short Description: Yes if the orient away from arrow rule should be applied
Long Description: Set this parameter to “No” if a chain of directed edges pointing in the same direction when only the first few such orientations are justified based on the data.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== avgDegree ===

avgDegree

Short Description: Average degree of graph (min = 0)
Long Description: The average degree of a graph is equal to 2E / V, where E is the number of edges in the graph and V the number of variables (vertices) in the graph, since each edge has two endpoints.
Default Value: 2
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Double

=== basisScale ===

basisScale

Short Description: Variables are scaled to [-b, b] for this b (0 = standardized)
Long Description: id="basisScale_short_desc"> Variables are scaled to [-b, b] for this b (0 = standardized)
Default Value: 1
Bound: 0
Upper Bound: 500000
Value Type: Double

=== basisType ===

basisType

Short Description: Basis type (0 = Polynomial, 1 = Legendre, 2 = Hermite, 3=Chebyshev)
Long Description: id="basisType_short_desc"> Basis type (0 = Polynomial, 1 = Legendre, 2 = Hermite, 3=Chebyshev)
Default Value: 1
Lower Bound: 0
Upper Bound: 3
Value Type: Integer

=== bootstrappingNumThreads ===

bootstrappingNumThreads

Short Description: The number of threads (>= 1) to use for the bootstrapping
Long Description: This is the number of threads for the bootstrapping itself. The number of threads that each algorithm uses is set by the individual algorithm.
Default Value: 1
Lower Bound: 1
Upper Bound: 1000000
Value Type: Integer

Note: You must specify the "Value Type" of each parameter, and the value type must be one of the following: Integer, Long, Double, String, Boolean.

=== bossAlg ===

bossAlg

Short Description: Picks the BOSS algorithm type, BOSS1 or BOSS2
Long Description: 1 = BOSS1, 2 = BOSS2, 3 = BOSS3
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== cacheScores ===

cacheScores

Short Description: Yes score results should be cached, no if not
Long Description: Caching scores can use a lot of memory.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== calculateEuclidean ===

calculateEuclidean

Short Description: Yes if the Euclidean norm squared should be calculated (slow), No if not
Long Description: The generalized information criterion is defined with an information term that take a Euclidean norm squares; there can be calculated directly.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== cciScoreAlpha ===

cciScoreAlpha

Short Description: Cutoff for p values (alpha) (min = 0.0)
Long Description: Alpha level (0 to 1)
Default Value: 0.01
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== cellTableType ===

cellTableType

Short Description: The type of cell table to use (optimization), 1 = AD Tree, 2 = Count Sample
Long Description: This is just whether table counts are to be calculated using one method or another, for optimization. The AD tree option uses AD trees to do the calculation; the Count Samples option simply counts the samples for each independence question and builds a table that way.
Default Value: 1
Lower Bound: 1
Upper Bound: 2
Value Type: Integer

=== cgExact ===

cgExact

Short Description: Yes if the exact algorithm should be used for continuous parents and discrete children
Long Description: For the conditional Gaussian likelihood, if the exact algorithm is desired for discrete children and continuous parents, set this parameter to “Yes”.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== checkAdjacencySepsets ===

checkAdjacencySepsets

Short Description: Yes if adjacency sepsets should be checked after all recursive sepsets check (default=No)
Long Description: Yes if adjacency sepsets should be checked after all recursive sepsets check (default=No). This is needed for FCIT to pass an Oracle test but may reduce accuracy.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== checkType ===

checkType

Short Description: Model significance check type: 1 = Significance, 2 = Clique, 3 = None
Long Description: Model significance check type: 1 = Significance, 2 = Clique, 3 = None
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== clusterSizes ===

clusterSizes

Short Description: Cluster sizes to check (comma separated, each >= 2, default = "2")
Long Description: Cluster sizes to check (comma separated, each >= 2, default = "2")
Default Value:
Lower Bound:
Upper Bound:
Value Type: String

=== coefHigh ===

coefHigh

Short Description: High end of coefficient range (min = 0.0)
Long Description: Value m2 for coefficients drawn from U(-m2, -m1) U U(m1, m2).
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== coefLow ===

coefLow

Short Description: Low end of coefficient range (min = 0.0)
Long Description: The parameter m1 for coefficients drawn from U(-m2, -m1) U U(m1, m2).
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== coefSymmetric ===

coefSymmetric

Short Description: Yes if negative coefficient values should be considered
Long Description: Yes if coefficients should be drawn from +/-(a, b); No if from +(a, b).
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== colliderDiscoveryRule ===

colliderDiscoveryRule

Short Description: Collider discovery: 1 = Lookup from adjacency sepsets, 2 = Conservative (CPC), 3 = Max-P
Long Description: One may look them up from sepsets, as in the original PC, or estimate them conservatively, as from the Conservative PC algorithm, or by choosing the sepsets with the maximum p-value, as in PC-Max.
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== colliderOrientationStyle ===

colliderOrientationStyle

Short Description: Collider Orientation Style: 1 = Sepsets 2 = Conservative 3 = Max-P
Long Description: Collider Orientation Style: 1 = Sepsets 2 = Conservative 3 = Max-P
Default Value: 3
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== completeRuleSetUsed ===

completeRuleSetUsed

Short Description: Yes if the complete FCI rule set should be used
Long Description: No if the (simpler) final orientation rules set due to P. Spirtes, guaranteeing arrow completeness, should be used; yes if the (fuller) set due to J. Zhang, should be used guaranteeing additional tail completeness.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== concurrentFAS ===

concurrentFAS

Short Description: Yes if a concurrent FAS should be done
Long Description: Yes if the version of the PC adjacency search that uses concurrent processing should be used, no if not.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== conditioningThreshold ===

conditioningThreshold

Short Description: Matrix conditioning values above which Eigenvalue whitening is used. Default 1e-10.
Long Description: Matrix conditioning values above which Eigenvalue whitening is used. For smaller tresholds, the faster Cholesky whitening is used. Default 1e-10, < 0 forces Eigenvalue whitening.
Default Value: 1e-10
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== conflictRule ===

conflictRule

Short Description: Collider conflicts: 1 = Prioritize existing colliders, 2 = Orient bidirected, 3 = Overwrite existing colliders
Long Description: 1 if the “overwrite” rule as introduced in the PCALG R package, 2 if all collider conflicts using bidirected edges, or 3 if existing colliders should be prioritized, ignoring subsequent conflicting information.
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== connected ===

connected

Short Description: Yes if graph should be connected
Long Description: Yes if a random graph should be generated in which paths exists from every node to every other, no if not.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== correlationThreshold ===

correlationThreshold

Short Description: Correlation Threshold
Long Description: The algorithm will complain if correlations are found that are greater than this in absolute value.
Default Value: 1
Lower Bound: 0
Upper Bound: 1
Value Type: Double

=== covHigh ===

covHigh

Short Description: High end of covariance range (min = 0.0)
Long Description: The parameter c2 for range +/-U(c1, c2) for covariance values, c1 >= 0.0
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== covLow ===

covLow

Short Description: Low end of covariance range (min = 0.0)
Long Description: The parameter c1 for range +/-U(c1, c2) for covariance values, c2 >= c1
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== covSymmetric ===

covSymmetric

Short Description: Yes if negative covariance values should be considered
Long Description: Usually covariance values are chosen from +/-U(a, b) for some a, b, no if from +U(a, b).
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== cpdag ===

cpdag

Short Description: True if a CPDAG should be returned, false if a DAG
Long Description: The algorithm returns a DAG; if this is set to True, this DAG is converted to a CPDAG
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== cstarCpdagAlgorithm ===

cstarCpdagAlgorithm

Short Description: Algorithm: 1 = PC Stable, 2 = FGES, 3 = BOSS, 4 = Restricted BOSS
Long Description: The CPDAG algorithm to use: 1 = PC Stable, 2 = FGES, 3 = BOSS, 4 = Restricted BOSS
Default Value: 4
Lower Bound: 1
Upper Bound: 4
Value Type: Integer

=== cutoffConstrainSearch ===

cutoffConstrainSearch

Short Description: Constraint-independence cutoff threshold
Long Description: null
Default Value: 0.5
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== cutoffDataSearch ===

cutoffDataSearch

Short Description: Independence cutoff threshold
Long Description: null
Default Value: 0.5
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== cutoffIndTest ===

cutoffIndTest

Short Description: Independence cutoff threshold
Long Description: null
Default Value: 0.5
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== cyclicCoefHigh ===

cyclicCoefHigh

Short Description: Cyclic: High end of coefficient range for coefficients in cycles
Long Description: Cyclic: Higb end of coefficient range for coefficients in cycles
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== cyclicCoefLow ===

cyclicCoefLow

Short Description: Cyclic: Low end of coefficient range for coefficients in cycles
Long Description: Cyclic: Low end of coefficient range for coefficients in cycles
Default Value: 0.2
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== cyclicCoefStyle ===

cyclicCoefStyle

Short Description: Cyclic: 0 = Auto 1 = Fix Radius 2 = Cap Products, 3 = None,
Long Description: Cyclic: 0 = Choose for Me 1 = Scale SCCs to cyclic radius 2 = Cap cyclic products in SCCs, 3 = Regular SEM initialization
Default Value: 0
Lower Bound: 0
Upper Bound: 3
Value Type: Integer

=== cyclicMaxProd ===

cyclicMaxProd

Short Description: Cyclic: Upper bound on product of coefficients around feedback loops.
Long Description: Cyclic: Upper bound on product of coefficients around feedback loops.
Default Value: 0.5
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== cyclicRadius ===

cyclicRadius

Short Description: Cyclic: Target spectral radius used to stabilize cyclic feedback.
Long Description: Cyclic: Target spectral radius used to stabilize cyclic feedback.
Default Value: 0.6
Lower Bound: 0
Upper Bound: 1
Value Type: Double

=== dataType ===

dataType

Short Description: "continuous" or "discrete"
Long Description: For a mixed data type simulation, if this is set to “continuous” or “discrete”, all variables are taken to be of that sort. This is used as a double-check to make sure the percent discrete is set appropriately.
Default Value: categorical
Lower Bound:
Upper Bound:
Value Type: String

=== depth ===

depth

Short Description: Maximum size of conditioning set ('depth', unlimited =-1)
Long Description: The depth of search for algorithms like the PC adjacency search, which is the maximum size of any conditioning set considered. In order to express that no limit should be imposed, use the value -1.
Default Value: -1
Lower Bound: -1
Upper Bound: 2147483647
Value Type: Integer

=== determinismThreshold ===

determinismThreshold

Short Description: Threshold for judging a regression of a variable onto its parents to be deterministic (min = 0.0)
Long Description: When regressing a child variable onto a set of parent variables, one way to test for determinism is to test how close to singular the data is; this gives a threshold for this. The default value is 0.1.
Default Value: 0.1
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== differentGraphs ===

differentGraphs

Short Description: Yes if a different graph should be used for each run
Long Description: If ‘Yes’ a new random graph is chosen for each run; if ‘No’, the same graph is always used.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== discretize ===

discretize

Short Description: Yes if continuous variables should be discretized when child is discrete
Long Description: Yes if for the conditional Gaussian likelihood, when scoring X->D where X is continuous and D discrete, one should to simply discretize X for just those cases. If no, the integration will be exact.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== doColliderOrientation ===

doColliderOrientation

Short Description: Yes if unshielded collider orientation should be done
Long Description: Please see the description of this algorithm in Thomas Richardson and Peter Spirtes in Chapter 7 of Computation, Causation, & Discovery by Glymour and Cooper eds.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== doFgesFirst ===

doFgesFirst

Short Description: Yes if FGES should be done as an initial step
Long Description: For BOSS, for some cases, doing FGES as an initial step can reduce the maximum permutation size needed to solve a problem.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== doOneEquationOnly ===

doOneEquationOnly

Short Description: True if only one equation should be used when expanding the basis
Long Description: True if only one equation should be used when expanding the basis
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== doPossibleDsep ===

doPossibleDsep

Short Description: Yes if the possible d-sep search should be done
Long Description: This algorithm has a possible d-sep path search, which can be time-consuming. See Spirtes, Glymour, and Scheines (2000) for details.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== ebicGamma ===

ebicGamma

Short Description: EBIC Gamma (0-1)
Long Description: The gamma parameter for Extended BIC (Chen and Chen). In [0, 1].
Default Value: 0.8
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== effectiveSampleSize ===

effectiveSampleSize

Short Description: The effective sample size, or -1 if the true sample size is to be used.
Long Description: The effective sample size, or -1 is the true sample size is to be used.
Default Value: -1
Lower Bound: -1
Upper Bound: 2147483647
Value Type: Integer

=== errorsNormal ===

errorsNormal

Short Description: Yes if errors should be Normal; No if they should be abs(Normal) (i.e., non-Gaussian)
Long Description: A “quick and dirty” way to generate linear, non-Gaussian data is to set this parameter to “No”; then the errors will be sampled from a Beta distribution.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== errorThreshold ===

errorThreshold

Short Description: Error Threshold
Long Description: Adjusts the threshold for judging conditional dependence.
Default Value: 0.5
Lower Bound: 0.0
Upper Bound: 1
Value Type: Double

=== ess ===

ess

Short Description: Yes if the equivalent sample size should be used in place of N
Long Description: We calculate the equivalent sample size by assuming that all record are equally correlated
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== excludeSelectionBias ===

excludeSelectionBias

Short Description: Yes if the possibility of selection bias should be excluded
Long Description: If true ("Yes"), the algorithm assumes no selection bias and disables selection-related orientation rules (including certain final rules in Zhang 2008). If false ("No") the algorithm allows for selection bias and uses the full rule set.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== extraEdgeRemovalStep ===

extraEdgeRemovalStep

Short Description: The extra edge removal step to use: 1 = LV_LITE, 2 = Greedy, 3 = Max P, 4 = Min P
Long Description: The extra edge removal step to use: 1 = LV_LITE, 2 = Greedy, 3 = Max P, 4 = Min P
Default Value: 1
Lower Bound: 1
Upper Bound: 4
Value Type: Integer

=== faithfulnessAssumed ===

faithfulnessAssumed

Short Description: Yes if (one edge) faithfulness should be assumed
Long Description: Assumes that if X _||_ Y, by an independence test, then X _||_ Y | Z for nonempty Z.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== faskAdjacencyMethod ===

faskAdjacencyMethod

Short Description: Non-skewness Adjacencies: 1 = FAS Stable, 2 = FGES, 3 = External Graph, 4 = None
Long Description: This is the method FASK will use to find non-skewness adjacencies. For External graph, an external graph must be supplied.
Default Value: 1
Lower Bound: 1
Upper Bound: 4
Value Type: Integer

=== faskAssumeLinearity ===

faskAssumeLinearity

Short Description: Linearity assumed
Long Description: True if a linear, non-Gaussian, additive model is assume; false if a nonlinear, non-Gaussian, additive model is assumed.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== faskDelta ===

faskDelta

Short Description: For FASK v1, the bias for orienting with negative coefficients ('0' means no bias.)
Long Description: The bias procedure for v1 is given in the published description.
Default Value: 0.0
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== faskLeftRightRule ===

faskLeftRightRule

Short Description: The left right rule: 1 = FASK v1, 2 = FASK v2, 3 = RSkew, 4 = Skew, 5 = Tanh
Long Description: The FASK left right rule v2 is default, but two other (related) left-right rules are given for relation to the literature, and the v1 FASK rule is included for backward compatibility.
Default Value: 3
Lower Bound: 1
Upper Bound: 5
Value Type: Integer

=== faskNonempirical ===

faskNonempirical

Short Description: Variables should be assumed to have positive skewness
Long Description: If false (default), each variable is multiplied by the sign of its skewness in the left-right rule.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== fasRule ===

fasRule

Short Description: Adjacency search: 1 = PC, 2 = PC-Stable, 3 =f Concurrent PC-Stable
Long Description: For variants of PC, one may select either to use the usual PC adjacency search, or the procedure from the PC-Stable algorithm (Diego and Maathuis), or the latter using a concurrent algorithm.
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== fastIcaA ===

fastIcaA

Short Description: Fast ICA 'a' parameter.
Long Description: This is the 'a' parameter of Fast ICA. (See Hyvarinen, A. (2001); it ranges between 1 and 2; we use a default of 1.1.
Default Value: 1.1
Lower Bound: 1.0
Upper Bound: 2.0
Value Type: Double

=== fastIcaMaxIter ===

fastIcaMaxIter

Short Description: The maximum number of optimization iterations.
Long Description: This is the maximum number if iterations of the optimization procedure of ICA. (See Hyvarinen, A. (2001). It's an integer greater than 0; we use a default of 2000.
Default Value: 2000
Lower Bound: 1
Upper Bound: 500000
Value Type: Double

=== fastIcaTolerance ===

fastIcaTolerance

Short Description: Fast ICA tolerance parameter.
Long Description: This is the tolerance parameter of Fast ICA. (See Hyvarinen, A. (2001); we use a default of 1e-6.
Default Value: 1e-6
Lower Bound: 0.0
Upper Bound: 1000.0
Value Type: Double

=== fcitStartsWith ===

fcitStartsWith

Short Description: The algorithm to find the initial CPDAG: 1 = BOSS, 2 = GRaSP, 3 = SP
Long Description: The algorithm to find the initial CPDAG: 1 = BOSS, 2 = GRaSP, 3 = SP
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== fdrQ ===

fdrQ

Short Description: FDR q value, often 0.01 - 0.1, or 0 if FDR should not be done.
Long Description: FDR q value, often 0.01 - 0.1, or 0 if FDR should not be done.
Default Value: 0
Lower Bound: 0
Upper Bound: 1
Value Type: Double

=== fileOutPath ===

fileOutPath

Short Description: Results output path
Long Description: Path to a directory in which results can be stored
Default Value: cstar-out
Lower Bound:
Upper Bound:
Value Type: String

=== fisherEpsilon ===

fisherEpsilon

Short Description: Epsilon where |xi.t - xi.t-1| < epsilon, criterion for convergence
Long Description: This is a parameter for the linear Fisher option. The idea of Fisher model (for the linear case) is to shock the system every so often and let it converge by applying the rules of transformation (that is, the linear model) repeatedly until convergence.
Default Value: 0.001
Lower Bound: 4.9E-324
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== fofcAlpha ===

fofcAlpha

Short Description: Cutoff for p values (alpha) (min = 0.0)
Long Description: Alpha level (0 to 1)
Default Value: 0.001
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== generalSemErrorTemplate ===

generalSemErrorTemplate

Short Description: General function for error terms
Long Description: This template specifies how distributions for error terms are to be generated. For help in constructing such templates, see the Generalized SEM PM model.
Default Value: Beta(2, 5)
Lower Bound:
Upper Bound:
Value Type: String

=== generalSemFunctionTemplateLatent ===

generalSemFunctionTemplateLatent

Short Description: General function template for latent variables
Long Description: This template specifies how equations for latent variables are to be generated. For help in constructing such templates, see the Generalized SEM PM model.
Default Value: TSUM(NEW(B)*$)/>
Lower Bound:
Upper Bound:
Value Type: String

=== generalSemFunctionTemplateMeasured ===

generalSemFunctionTemplateMeasured

Short Description: General function template for measured variables
Long Description: This template specifies how equations for measured variables are to be generated. For help in constructing such templates, see the Generalized SEM PM model.
Default Value: TSUM(NEW(B)*$>
Lower Bound:
Upper Bound:
Value Type: String

=== generalSemParameterTemplate ===

generalSemParameterTemplate

Short Description: General function for parameters
Long Description: This template specifies how distributions for parameter terms are to be generated. For help in constructing such templates, see the Generalized SEM PM model.
Default Value: Split(-1.0, -0.5, 0.5, 1.0)
Lower Bound:
Upper Bound:
Value Type: String

=== ginBackend ===

ginBackend

Short Description: Backend test: 1 = dcor 2 = pearson.
Long Description: Choose unconditional test for residual independence: “dcor” detects nonlinear relations, “pearson” is fast but linear only.
Default Value: 1
Lower Bound: 1
Upper Bound: 2
Value Type: Integer

=== ginPermutations ===

ginPermutations

Short Description: Number of permutations for dCor.
Long Description: Number of random shuffles used to compute p-values for dCor; higher values give more reliable p-values but increase runtime; ignored if backend is pearson.
Default Value: 200
Lower Bound: 0
Upper Bound: 100000
Value Type: Integer

=== ginRidge ===

ginRidge

Short Description: Ridge penalty for OLS regression
Long Description: Small positive value added to regression diagonals for numerical stability when fitting residual models; larger values regularize more but bias residuals.
Default Value: 1e-8
Lower Bound: 0
Upper Bound: 100000
Value Type: Double

=== graspAlg ===

graspAlg

Short Description: 1 = GRaSP1, 2 = GRaSP2, 3 = esp, 4 = GRaSP4, 5 = GRaSP4
Long Description: Which version of GRaSP (temp parameter)
Default Value: 1
Lower Bound: 1
Upper Bound: 5
Value Type: Integer

=== graspBreakAfterImprovement ===

graspBreakAfterImprovement

Short Description: Yes if depth first search returns after first improvement, No for depth first traversal.
Long Description: Exploring the full list in every DFS call is equivalent to what we've been calling the Random Carnival Game procedure (RCG).
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== graspCheckCovering ===

graspCheckCovering

Short Description: Yes if covering of edges should be checked (GASP), no if not (GRASP)
Long Description: An edge X is covered if Parents(X) = Parents(Y) \ {X}. Not checking covering expands the search space.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== graspDepth ===

graspDepth

Short Description: Recursion depth (for GRaSP)
Long Description: This is the depth of recursion for the depth first search.
Default Value: 3
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== graspForwardTuckOnly ===

graspForwardTuckOnly

Short Description: Yes if only forward tucks should be checked, no if also reverse tucks should be checked.
Long Description: A forward tuck for X->Y moves Y to the before position of X in the permutation. A reverse tuck moves Y to after the position of X in the permutation. Including reverse tucks expands the search space.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== graspNonSingularDepth ===

graspNonSingularDepth

Short Description: Recursion depth for nonsingular tucks
Long Description: This is the depth of recursion at which multiple tucks may be considered per score improvement
Default Value: 1
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== graspOrderedAlg ===

graspOrderedAlg

Short Description: Yes if earlier GRaSP stages should be performed before later stages
Long Description: GRaSP has three stages; these can be performed separately or in order; by default Yes.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== graspSingularDepth ===

graspSingularDepth

Short Description: Recursion depth for singular tucks
Long Description: This is the depth of recursion for the singular tucks.
Default Value: 1
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== graspToleranceDepth ===

graspToleranceDepth

Short Description: Recursion depth for tolerance tucks
Long Description: This is the maximum number of non-greedy tucks in depth first order --that is, tucks where the score is allowed to decrease rather than increase.
Default Value: 0
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== graspUseRaskuttiUhler ===

graspUseRaskuttiUhler

Short Description: Yes to use Raskutti and Uhler's DAG-building method (test), No to use Grow-Shrink (score).
Long Description: Raskutti and Uhler's method adds and edge X->Y if Y ~_||_ X | Prefix(Y, pi) \ {X}. Grow-Shrink adds an edge X->Y if X is in the Markov blanket of Y where the variable set is restricted to Prefix(Y, pi).
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== graspUseScore ===

graspUseScore

Short Description: Yes if the score should be used for MB calculations, no if the test should be used instead.
Long Description: In either case, compositional graphoid axioms are assumed by the Grow-Shrink algorithm.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== graspUseVpScoring ===

graspUseVpScoring

Short Description: No sure
Long Description: Not sure
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== guaranteeAcyclic ===

guaranteeAcyclic

Short Description: True if the output should be guaranteed to be acyclic
Long Description: The estimated B matrix is further thresholded by setting small coefficients to zero until an acyclic model is produced.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== guaranteeCpdag ===

guaranteeCpdag

Short Description: Guarantee that the output is a legal CPDAG
Long Description: It is possible due to unfaithfulness for the Meek rules to output a non-CPDAG; this parameter guarantees a CPDAG if set to 'Yes'.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== guaranteeIid ===

guaranteeIid

Short Description: Recursive simulation is used for acyclic models; if not should i.i.d. be assumed?
Long Description: For cyclic models, the Fisher simulation model is used, which is a time series. Selecting 'Yes' here guarantees that a new data point starts from a new shock without influence from the previous time step.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== guaranteePag ===

guaranteePag

Short Description: Ensure the output is a legal PAG (where feasible)
Long Description: Repairs errors in PAGs due to almost cyclic paths or non-maximalities. This comes with a certain risk; errors in PAGs indicate that the PAG assumptions were not met; the user may wish to consider why before selecting this
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== henckelPruning ===

henckelPruning

Short Description: Whether to do Henckel et al. (2020) Algorithm 1 pruning.
Long Description: Whether to do Henckel et al. (2020) Algorithm 1 pruning.
Default Value: False
Lower Bound:
Upper Bound:
Value Type: Boolean

=== hiddenDimension ===

hiddenDimension

Short Description: For Nonlinear Additive Model, the number of nodes per edge
Long Description: For a shallow multilayer perception (MLP), the number of nodes in the hidden layer
Default Value: 10
Lower Bound: 1
Upper Bound: 500000
Value Type: Integer

=== hiddenDimensions ===

hiddenDimensions

Short Description: For perceptrons, the number of nodes in hidden layers (comma separated)
Long Description: For perceptrons, the number of nodes in hidden layers (comma separated)
Default Value: 50,50,50,50,50
Lower Bound:
Upper Bound:
Value Type: String

=== ia ===

ia

Short Description: IA parameter (GLASSO)
Long Description: Sets the maximum number of iterations of the optimization loop.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== imagesMetaAlg ===

imagesMetaAlg

Short Description: IMaGES "meta" algorithm. 1 = FGES, 2 = BOSS-Tuck
Long Description: Sets the meta algorithm to be optimized using the IMaGES (average BIC) score.
Default Value: 1
Lower Bound: 1
Upper Bound: 5
Value Type: Integer

=== includeAllNodes ===

includeAllNodes

Short Description: True if all nodes should be included in the output
Long Description: True if all nodes should be included in the output.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== includeNegativeCoefs ===

includeNegativeCoefs

Short Description: Yes if negative coefficients should be included in the model
Long Description: One may include positive coefficients, negative coefficients, or both, in the model. To include negative coefficients, set this parameter to “Yes”.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== includeNegativeSkewsForBeta ===

includeNegativeSkewsForBeta

Short Description: Yes if negative skew values should be included in the model, if Beta errors are chosen
Long Description: Yes if negative skew values should be included in the model, if Beta errors are chosen.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== includePositiveCoefs ===

includePositiveCoefs

Short Description: Yes if positive coefficients should be included in the model
Long Description: Yes if We may include positive coefficients, should be included in the model, no if not.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== includePositiveSkewsForBeta ===

includePositiveSkewsForBeta

Short Description: Yes if positive skew values should be included in the model, if Beta errors are chosen
Long Description: Yes if positive skew values should be included in the model, if Beta errors are chosen.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== inputScale ===

inputScale

Short Description: For a shallow multilayer perception (MLP), the input scale (affects bumpiness)
Long Description: For a shallow multilayer perception (MLP), the input scale (affects bumpiness)
Default Value: 5.0
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== instanceRow ===

instanceRow

Short Description: Indicates a particular row in the testing dataset (one-indexed)
Long Description: If the algorithm uses a testing dataset, this row index points to a specific row in the data to be used as input to the algorithm. This is one-indexed.
Default Value: 1
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== instanceSpecificAlpha ===

instanceSpecificAlpha

Short Description: Weight for instance-specific component to the score
Long Description: Weight for instance-specific component to the score.
Default Value: 1.0
Lower Bound: 0
Upper Bound: Infinity
Value Type: Double

=== intervalBetweenRecordings ===

intervalBetweenRecordings

Short Description: Interval between data recordings for the linear Fisher model (min = 1)
Long Description:
Default Value: 10
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== intervalBetweenShocks ===

intervalBetweenShocks

Short Description: Interval between shocks (R. A. Fisher simulation model) (min = 1)
Long Description: This is a parameter for the linear Fisher option. This sets the number of step between shocks.
Default Value: 10
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== ipen ===

ipen

Short Description: IPEN parameter (GLASSO)
Long Description: This sets the maximum number of iterations of the optimization loop.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== is ===

is

Short Description: IS parameter (GLASSO)
Long Description: Sets the maximum number of iterations of the optimization loop.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== itr ===

itr

Short Description: ITR parameter (GLASSO)
Long Description: Sets the maximum number of iterations of the optimization loop.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== kciAlpha ===

kciAlpha

Short Description: Cutoff for p values (alpha) (min = 0.0)
Long Description: Alpha level (0 to 1)
Default Value: 0.05
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== kciCutoff ===

kciCutoff

Short Description: Cutoff
Long Description: Cutoff for p-values.
Default Value: 6
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== kciEpsilon ===

kciEpsilon

Short Description: Epsilon, a small positive number
Long Description: See Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2012). Kernel-based conditional independence test and application in causal discovery.. This parameter is the epsilon for Proposition 5, a small positive number.
Default Value: 0.001
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== kciNumBootstraps ===

kciNumBootstraps

Short Description: Number of bootstraps
Long Description: This parameter is the number of bootstraps for Theorems 4 from Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2012) and Proposition 5, a positive integer.
Default Value: 1000
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== kciUseApproximation ===

kciUseApproximation

Short Description: Use the Gamma approximation algorithm
Long Description: Referring to Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2012), if this parameter is set to ‘Yes’, the Gamma approximation algorithm is used; if no, the exact procedure is used.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== kernelRegressionSampleSize ===

kernelRegressionSampleSize

Short Description: Minimum sample size to use per conditioning for kernel regression
Long Description: The smallest set of nearest data points on which to allow a judgment to be based for a nonlinear regression.
Default Value: 100
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== kernelType ===

kernelType

Short Description: Kernel type (1 = Gaussian, 2 = Linear, 3 = Polynomial)
Long Description: Determines which kernel type will be used (1 = Gaussian, 2 = Linear, 3 = Polynomial).
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== kernelWidth ===

kernelWidth

Short Description: Kernel width
Long Description: A larger kernel width means that more information will be taken into account but possibly less focused information.
Default Value: 1.0
Lower Bound: 4.9E-324
Upper Bound: Infinity
Value Type: Double

=== lambda1 ===

lambda1

Short Description: lambda1
Long Description: Tuning parameter for DAGMA
Default Value: 0.05
Lower Bound: 0
Upper Bound: Infinity
Value Type: Double

=== lowerBound ===

lowerBound

Short Description: Lower bound cutoff threshold
Long Description: null
Default Value: 0.3
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== manualLambda ===

manualLambda

Short Description: Lambda (manually set)
Long Description: The manually set lambda for GIC--the default is 10, though this should be set by the user to a good value.
Default Value: 10.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== maxBlockingPathLength ===

maxBlockingPathLength

Short Description: Maximum path length for paths for searching for path blocking sets (-1 = no limit)
Long Description: The maximum length of paths to search for path blocking sets.
Default Value: -1
Lower Bound: -1
Upper Bound: 2147483647
Value Type: Integer

=== maxCategories ===

maxCategories

Short Description: Maximum number of categories (min = 2)
Long Description: The maximum number of categories to be used for randomly generated discrete variables. The default is 2. This needs to be greater or equal to than the minimum number of categories.
Default Value: 3
Lower Bound: 2
Upper Bound: 2147483647
Value Type: Integer

=== maxCorrelation ===

maxCorrelation

Short Description: Maximum absolute correlation considered
Long Description: For the Nandy rule, the absolute max correlation r. For the standard BIC or high-dimensional rule, the maximum absolute residual correlation.
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== maxDegree ===

maxDegree

Short Description: The maximum degree of the graph (min = -1)
Long Description: An upper bound on the maximum degree of any node in the graph. If no limit is to be placed on the maximum degree, use the value -1.
Default Value: 1000
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== maxDiscriminatingPathLength ===

maxDiscriminatingPathLength

Short Description: The maximum length for any discriminating path. -1 if unlimited (min = -1)
Long Description: See Spirtes, Glymour, and Scheines (2000) for the definition of discrimination path. Finding discriminating paths can be expensive. This sets the maximum length of such paths that the algorithm tries to find.
Default Value: -1
Lower Bound: -1
Upper Bound: 2147483647
Value Type: Integer

=== maxDistinctValuesDiscrete ===

maxDistinctValuesDiscrete

Short Description: The maximum number of distinct values in a column for discrete variables (min = 0)
Long Description: Discrete variables will be simulated using any number of categories from 2 up to this maximum. If set to 0 or 1, discrete variables will not be generated.
Default Value: 0
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== maxIndegree ===

maxIndegree

Short Description: Maximum indegree of graph (min = 1)
Long Description: An upper bound on the maximum indegree of any node in the graph. If no limit is to be placed on the maximum degree, use the value -1.
Default Value: 1000
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== maxit ===

maxit

Short Description: MAXIT parameter (GLASSO) (min = 1)
Long Description: Sets the maximum number of iterations of the optimization loop.
Default Value: 10000
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== maxIterations ===

maxIterations

Short Description: The maximum number of iterations the algorithm should go through orienting edges
Long Description: In orienting, this algorithm may go through a number of iterations, conditioning on more and more variables until orientations are set. This sets that number.
Default Value: 15
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== maxOutdegree ===

maxOutdegree

Short Description: Maximum outdegree of graph (min = 1)
Long Description: An upper bound on the maximum outdegree of any node in the graph. If no limit is to be placed on the maximum degree, use the value -1.
Default Value: 1000
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== maxPaxPOrientationHeuristicMaxLength ===

maxPaxPOrientationHeuristicMaxLength

Short Description: The maximum path length to use for the max p heuristic version.
Long Description: The maximum path length to use for the max p heuristic version.
Default Value: 5
Lower Bound: 0
Upper Bound: 100000
Value Type: Integer

=== maxPOrientationMaxPathLength ===

maxPOrientationMaxPathLength

Short Description: Maximum path length for the unshielded collider heuristic for max P (min = 0)
Long Description: For the Max P “heuristic” to work, it must be the case that X and Z are only weakly associated—that is, that paths between them are not too short. This bounds the length of paths for this purpose.
Default Value: 3
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== maxRank ===

maxRank

Short Description: The algorithm looks for clusters from rank 1 up through this rank
Long Description: The algorithm looks for clusters from rank 1 up through this rank
Default Value: 2
Lower Bound: 1
Upper Bound: 1000
Value Type: Integer

=== maxScoreDrop ===

maxScoreDrop

Short Description: Maximum score drop for the process triples step
Long Description: In orienting unshielded colliders by examining triples of nodes, the score is permitted to drop by this much.
Default Value: 5
Lower Bound: 0
Upper Bound: Infinity
Value Type: Double

=== maxSepsetSize ===

maxSepsetSize

Short Description: For testing steps in FCIT, the maximum conditioning set size
Long Description: In the extra edge removal step, we build conditioning sets based on the current PAG to attempt to remove adjacencies from the graph, by blocking paths from x to y of up to this length. This is the maximum size these sets are allowed to grow to.
Default Value: 8
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== mb ===

mb

Short Description: Find Markov blanket(s)
Long Description: Looks for the graph over the Markov blanket(s) and target(s) if true
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== mcAlpha ===

mcAlpha

Short Description: Markov Checker Alpha Level (0 to 1)
Long Description: Markov Checker Alpha Level (0 to 1)
Default Value: 0.05
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== meanHigh ===

meanHigh

Short Description: High end of mean range (min = 0.0)
Long Description: The default is for there to be no shift in mean, but shifts from a minimum value to a maximum value may be specified. The minimum must be less than or equal to this maximum.
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== meanLow ===

meanLow

Short Description: Low end of mean range (min = 0.0)
Long Description: The default is for there to be no shift in mean, but shifts from a minimum value to a maximum value may be specified. The minimum must be greater than or equal to this minimum.
Default Value: 0.5
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== measurementVariance ===

measurementVariance

Short Description: Additive measurement noise variance (min = 0.0)
Long Description: If the value is greater than zero, independent Gaussian noise will be added with mean zero and the given variance to each variable in the simulated output.
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== mgmParam1 ===

mgmParam1

Short Description: MGM tuning parameter #1 (min = 0.0)
Long Description: The MGM algorithm has three internal tuning parameters, of which this is one.
Default Value: 0.1
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== mgmParam2 ===

mgmParam2

Short Description: MGM tuning parameter #2 (min = 0.0)
Long Description: The MGM algorithm has three internal tuning parameters, of which this is one.
Default Value: 0.1
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== mgmParam3 ===

mgmParam3

Short Description: MGM tuning parameter #3 (min = 0.0)
Long Description: The MGM algorithm has three internal tuning parameters, of which this is one.
Default Value: 0.1
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== mimbuildType ===

mimbuildType

Short Description: Mimbuild type: 1 = PCA, 2 = Bollen
Long Description: Mimbuild type: 1 = PCA, 2 = Bollen
Default Value: 1
Lower Bound: 1
Upper Bound: 2
Value Type: Integer

=== mimLatentGroupSpecs ===

mimLatentGroupSpecs

Short Description: MIM: List of count:children:(rank), comma separated; e.g. 5:6(1),2:8(2):
Long Description: List of count:children:(rank), comma separated; e.g. 5:6(1),2:8(2)
Default Value: 5:6(1)
Lower Bound:
Upper Bound:
Value Type: String

=== mimLatentMeasuredImpureParents ===

mimLatentMeasuredImpureParents

Short Description: MIM: Number of Latent --> Measured impure edges
Long Description: It is possible for structural nodes to have as children measured variables that are children of other structural nodes. These edges in the graph will be considered impure.
Default Value: 0
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== mimMeasuredMeasuredImpureAssociations ===

mimMeasuredMeasuredImpureAssociations

Short Description: MIM: Number of Measured <-> Measured impure edges
Long Description: It is possible for measures from two different structural nodes to be confounded. These confounding (bidirected) edges will be considered to be impure.
Default Value: 0
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== mimMeasuredMeasuredImpureParents ===

mimMeasuredMeasuredImpureParents

Short Description: MIM: Number of Measured --> Measured impure edges
Long Description: It is possible for measures from two different structural nodes to have directed edges between them. These edges will be considered to be impure.
Default Value: 0
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== mimMetaEdgeConnectionType ===

mimMetaEdgeConnectionType

Short Description: 1 = Cartesian Product 2 = Corresponding, 3 = Patchy Connections
Long Description: 1 = Cartesian Product 2 = Corresponding, 3 = Patchy Connections
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== mimMimNumStructuralNodes ===

mimMimNumStructuralNodes

Short Description: Number of measurements per Latent
Long Description: Each structural node in the MIM will be created to have this many measured children.
Default Value: 5
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== mimNumChildrenPerGroup ===

mimNumChildrenPerGroup

Short Description: MIM: Number of children for each group latents
Long Description: Each group of latents shares a common set of children of this size.
Default Value: 0
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== mimNumStructuralEdges ===

mimNumStructuralEdges

Short Description: MIM: Number of structural edges
Long Description: This is a parameter for generating random multiple indicator models (MIMs). A structural edge is an edge connecting two structural nodes.
Default Value: 5
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== mimNumStructuralNodes ===

mimNumStructuralNodes

Short Description: Number of structural nodes
Long Description: This is a parameter for generating random multiple indicator models (MIMs). A structural node is one of the latent variables in the model; each structural node has a number of child measured variables.
Default Value: 3
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== minCategories ===

minCategories

Short Description: Minimum number of categories (min = 2)
Long Description: The minimum number of categories to be used for randomly generated discrete variables. The default is 2.
Default Value: 3
Lower Bound: 2
Upper Bound: 2147483647
Value Type: Integer

=== minCountPerCell ===

minCountPerCell

Short Description: The minimum count per cell in a chi square table.
Long Description: Increasing this can improve accuracy of chi square estimates.
Default Value: 1
Lower Bound: 1
Upper Bound: 1000000
Value Type: Integer

=== minParamSampleSize ===

minParamSampleSize

Short Description: The minimum sample size per parameter
Long Description: The minimum sample size per parameter
Default Value: 20
Lower Bound: 1
Upper Bound: 100000000
Value Type: Integer

=== minSampleSizePerCell ===

minSampleSizePerCell

Short Description: For conditional Gaussian, the minimum sample size per cell
Long Description: For conditional Gaussian, the minimum sample size per cell
Default Value: 4
Lower Bound: 2
Upper Bound: 100000000
Value Type: Integer

=== mnarNumExtraInfluences ===

mnarNumExtraInfluences

Short Description: MNAR: The number of extra influences on missing value selection.
Long Description: id="mnarNumExtraInfluences_short_desc"> MNAR: The number of extra influences on missing value selection.
Default Value: 0
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== mnarNumVariablesWithMissing ===

mnarNumVariablesWithMissing

Short Description: MNAR: The number of variables with missing values.
Long Description: id="mnarNumVariablesWithMissing_short_desc"> MNAR: The number of variables with missing values.
Default Value: 5
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== mnarThreshold ===

mnarThreshold

Short Description: MNAR: Remove this fraction upper tail values for columns with missing values
Long Description: id="mnarThreshold_short_desc"> MNAR: Remove this fraction upper tail values for columns with missing values
Default Value: 0.1
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== noRandomlyDeterminedIndependence ===

noRandomlyDeterminedIndependence

Short Description: Yes, if using the cutoff threshold for the independence test.
Long Description: null
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== numBasisFunctions ===

numBasisFunctions

Short Description: Number of functions to use in (truncated) basis
Long Description: This parameter specifies how many of the most significant basis functions to use as a basis.
Default Value: 3
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== numberOfExpansions ===

numberOfExpansions

Short Description: Number of expansions of the algorithm away from the target
Long Description: Each expansion iterates to concentrically more variables
Default Value: 2
Lower Bound: 1
Upper Bound: 1000
Value Type: Integer

=== numberResampling ===

numberResampling

Short Description: The number of bootstraps/resampling iterations (min = 0)
Long Description: For bootstrapping, the number of bootstrap iterations that should be done by the algorithm, with results summarized.
Default Value: 0
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== numBscBootstrapSamples ===

numBscBootstrapSamples

Short Description: The number of bootstrappings drawing from posterior dist. (min = 1)
Long Description: The number of bootstrappings drawing from posterior dist. (min = 1)
Default Value: 50
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== numCategories ===

numCategories

Short Description: Number of categories for discrete variables (min = 2)
Long Description: The number of categories to be used for randomly generated discrete variables. The default is 4; the minimum is 2.
Default Value: 4
Lower Bound: 2
Upper Bound: 2147483647
Value Type: Integer

=== numCategoriesToDiscretize ===

numCategoriesToDiscretize

Short Description: The number of categories used to discretize continuous variables, if necessary (min = 2)
Long Description: In case the exact algorithm is not used for discrete children and continuous parents is not used, this parameter gives the number of categories to use for this second (discretize) backup copy of the continuous variables.
Default Value: 3
Lower Bound: 2
Upper Bound: 2147483647
Value Type: Integer

=== numLags ===

numLags

Short Description: The number of lags in the time lag model
Long Description: A time lag model may take variables from previous time steps into account. This determines how many steps back these relevant variables might go.
Default Value: 1
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== numLatents ===

numLatents

Short Description: Number of additional latent variables (min = 0)
Long Description: The number of additional latent variables to include in the datasets
Default Value: 0
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== numMeasures ===

numMeasures

Short Description: Number of measured variables (min = 1)
Long Description: The number of measured (recorded in data) variables to include in the dataset.
Default Value: 10
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== numRandomizedSearchModels ===

numRandomizedSearchModels

Short Description: The number of search probabilistic model (min = 1)
Long Description: The number of search probabilistic model (min = 1)
Default Value: 10
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== numRuns ===

numRuns

Short Description: Number of runs (min = 1)
Long Description: An analysis(randomly pick graph, randomly simulate a dataset, run an algorithm on it, look at the result) may be run over and over again this many times.
Default Value: 1
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== numStarts ===

numStarts

Short Description: The number of restarts, random after the first (default 1)
Long Description: The number of times the algorithm should be started from different initializations. By default, the algorithm will be run through at least once using the initialized parameters (zero random restarts).
Default Value: 1
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== numSub-samples ===

numSub-samples

Short Description: Number of sub-samples
Long Description: Number of sub-samples
Default Value: 50
Lower Bound: 1
Upper Bound: 500000
Value Type: Integer

=== numSubsamples ===

numSubsamples

Short Description: The number of subsamples to generate.
Long Description: CStaR works by generating subsamples and summarizing across them; this specified the number of subsamples to generate. Must be >= 1. effects in the CStaR table
Default Value: 10
Lower Bound: 1
Upper Bound: 100000
Value Type: Integer

=== numThreads ===

numThreads

Short Description: The number of threads (>= 1) to use for the search
Long Description: The number of threads to use for the search.
Default Value: 1
Lower Bound: 1
Upper Bound: 1000000
Value Type: Integer

=== orientationAlpha ===

orientationAlpha

Short Description: Alpha threshold used for orientation (where necessary). ('0' turns this off.)
Long Description: Used for orienting 2-cycles and testing for zero edges.
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== orientTowardMConnections ===

orientTowardMConnections

Short Description: Yes if Richardson's step C (orient toward d-connection) should be used
Long Description: Please see the description of this algorithm in Thomas Richardson and Peter Spirtes in Chapter 7 of Computation, Causation, & Discovery by Glymour and Cooper eds.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== orientVisibleFeedbackLoops ===

orientVisibleFeedbackLoops

Short Description: Yes if visible feedback loops should be oriented
Long Description: Please see the description of this algorithm in Thomas Richardson and Peter Spirtes in Chapter 7 of Computation, Causation, & Discovery by Glymour and Cooper eds.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== otherPermMethod ===

otherPermMethod

Short Description: 1 = RCG, 2 = GSP, 3 = ESP, 4 = SP
Long Description: RCG (Random Carnival Game); GSP ("Greedy SP") GSP using tucking ESP ("Edge SP") is from Solus et al. SP ("Sparsest Permutation") Raskutti and Uhler
Default Value: 1
Lower Bound: 1
Upper Bound: 5
Value Type: Integer

=== outputCpdag ===

outputCpdag

Short Description: Yes if CPDAG should be output, no if a DAG.
Long Description: BOSS can output a DAG or the CPDAG of the DAG.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== outputRBD ===

outputRBD

Short Description: Constraint Scoring: Yes: Dependent Scoring, No: Independent Scoring.
Long Description: Constraint Scoring: Yes: Dependent Scoring, No: Independent Scoring.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== parallelized ===

parallelized

Short Description: Yes if the search should be parallelized
Long Description: This search is capable of being parallelized; select yes if the search should be parallelized, not if it should be run in a single thread
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== pathsMaxDistanceFromEndpoint ===

pathsMaxDistanceFromEndpoint

Short Description: The maximum distance of an allowable node from the endpoint of a path for adjustment
Long Description: In order to give guidance to which adjustment sets to report, this parameter lets one give a maximum distance from the endpoint of a path for a node to be included in an adjustment set.
Default Value: 3
Lower Bound: 0
Upper Bound: 100000
Value Type: Integer

=== pathsMaxLength ===

pathsMaxLength

Short Description: The maximum length of a path to report
Long Description: Since paths may be long, especially for large graphs, this parameter allows one to limit the length of a path to report. It must be at least 2.
Default Value: 8
Lower Bound: 2
Upper Bound: 100000
Value Type: Integer

=== pathsMaxLengthAdjustment ===

pathsMaxLengthAdjustment

Short Description: The maximum length of a backdoor path to consider for adjustment.
Long Description: The maximum length of a backdoor path to consider for finding an adjustment set. Amenable paths of any length are considered.
Default Value: 8
Lower Bound: 2
Upper Bound: 100000
Value Type: Integer

=== pathsMaxNumSets ===

pathsMaxNumSets

Short Description: The maximum number of adjustment sets to output
Long Description: There may be too many legal adjustments to sets to output; this places a bound on how many to output. These will be listed in order of increasing size.
Default Value: 4
Lower Bound: 0
Upper Bound: 100000
Value Type: Integer

=== pathsNearWhichEndpoint ===

pathsNearWhichEndpoint

Short Description: 1 = near source, 2 = near target, 3 = near either
Long Description: Adjustment sets may be found near the source, near the target, or near either.
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== pcHeuristic ===

pcHeuristic

Short Description: Heuristics to stabilize skeleton: 0 = None, 1 = Heuristic 1, 2 = Heuristic 2, 3 = Heuristic 3
Long Description: NONE = no heuristic, PC-1 = sort nodes alphabetically; PC-1 = sort edges by p-value; PC-3 = additionally sort edges in reverse order using p-values of associated independence facts. See CPS.
Default Value: 0
Lower Bound: 0
Upper Bound: 3
Value Type: Integer

=== penaltyDiscount ===

penaltyDiscount

Short Description: Penalty discount (min = 0.0)
Long Description: The parameter c added to a modified BIC score of the form 2L – c k ln N, where L is the likelihood, k the number of degrees of freedom, and N the sample size. Higher c yield sparser graphs.
Default Value: 2.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== penaltyDiscountZs ===

penaltyDiscountZs

Short Description: Penalty discount (min = 0.0)
Long Description: The parameter c added to a modified BIC score of the form 2L – c k lambda, where L is the likelihood, k the number of degrees of freedom, and lambda the choice of GIC lambda. Higher c yield sparser graphs.
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== percentDiscrete ===

percentDiscrete

Short Description: Percentage of discrete variables (0 - 100) for mixed data
Long Description: For a mixed data type simulation, specifies the percentage of variables that should be simulated (randomly) as discrete. The rest will be taken to be continuous. The default is 0—i.e. no discrete variables.
Default Value: 50.0
Lower Bound: 0.0
Upper Bound: 100.0
Value Type: Double

=== percentResampleSize ===

percentResampleSize

Short Description: The percentage of resample size (min = 10%)
Long Description: This parameter specifies the percentage of records in the bootstrap (as a percentage of the total original sample size of the data being bootstrapped).
Default Value: 100
Lower Bound: 10
Upper Bound: 100
Value Type: Integer

=== piThr ===

piThr

Short Description: A fixed threshold for calculating E[V] and PCER
Long Description: A fixed threshold, default 0.5
Default Value: 0.6
Lower Bound: 0
Upper Bound: 1
Value Type: Double

=== poissonLambda ===

poissonLambda

Short Description: Lambda parameter for the Poisson distribution (> 0)
Long Description: Lambda parameter for the Poisson distribution
Default Value: 1
Lower Bound: 1e-10
Upper Bound: Infinity
Value Type: Double

=== polynomialConstant ===

polynomialConstant

Short Description: For polynomial kernel: The constant
Long Description: The constant of the polynomial kernel, if used, which determine tradeoff between higher and lower order terms
Default Value: 1
Lower Bound: 0
Upper Bound: 5000
Value Type: Double

=== polynomialDegree ===

polynomialDegree

Short Description: For polynomial kernel: The degree
Long Description: The degree of the polynomial kernel, if used
Default Value: 2
Lower Bound: 1
Upper Bound: 5000000
Value Type: Double

=== precomputeCovariances ===

precomputeCovariances

Short Description: True if covariance matrix should be precomputed for tabular continuous data
Long Description: For more than 5000 variables or so, set this to false in order to calculate covariances on the fly from data.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== preserveMarkov ===

preserveMarkov

Short Description: Preserve the Markov property (checking MBs) if initial graph is Markov
Long Description: The Markov property checking MBs says that if msep(x, y | MB(x)) then x _||_ y | MB(x). Checking true for this property will tell the algorithm to ensure this property if the scoring step produces a Markov graph. Not applicable when running the algorithm from Oracle.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== priorEquivalentSampleSize ===

priorEquivalentSampleSize

Short Description: Prior equivalent sample size (min = 1.0)
Long Description: This sets the prior equivalent sample size. This number is added to the sample size for each conditional probability table in the model and is divided equally among the cells in the table.
Default Value: 10.0
Lower Bound: 1.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== probabilityOfEdge ===

probabilityOfEdge

Short Description: Probability of an adjacency being included in the graph
Long Description: Every possible adjacency in the graph is included it the graph with this probability.
Default Value: 0.05
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== probCycle ===

probCycle

Short Description: The probability of adding a cycle to the graph
Long Description: Sets the probability that any particular set of 3, 4, or 5 of nodes will be used to form a cycle in the graph.
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== probRemoveColumn ===

probRemoveColumn

Short Description: Probability of randomly removing a column from a dataset
Long Description: For testing algorithms with overlapping variables, columns may be removed from datasets with this probability.
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== probTwoCycle ===

probTwoCycle

Short Description: The probability of creating a 2-cycles in the graph (0 - 1)
Long Description: Any edge X*-*Y may be replaced with a 2-cycle (feedback loop) between X and Y with this probability.
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== randomizeColumns ===

randomizeColumns

Short Description: Yes if the order of the columns in each dataset should be randomized
Long Description: In the real world where unfaithfulness is an issue the order of variables in the data may for some algorithms affect the output. For testing purposes, if Yes, the data columns are randomly re-ordered.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== randomSelectionSize ===

randomSelectionSize

Short Description: The number of datasets that should be taken in each random sample
Long Description: The number of dataset that should be taken in each random sample of datasets.
Default Value: 1
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== rcit.approx ===

rcit.approx

Short Description: Null approximation method: 1 = LpB4, 2 = HBE, 3 = Gamma, 4 = Chi², 5 = Permutation
Long Description: 1 = Lindsay–Pilla–Basak (LpB4) 2 = Hall–Buckley–Eagleson (HBE) 3 = Gamma (Satterthwaite–Welch) 4 = Chi² (normalized) 5 = Permutation test (slower, more accurate for small samples)
Default Value: 1
Lower Bound: 1
Upper Bound: 5
Value Type: Integer

=== rcit.centerFeatures ===

rcit.centerFeatures

Short Description: Center feature matrices
Long Description: If true, center random-feature matrices before regression and test-statistic computation. Recommended for numerical stability and alignment with kernelized formulations.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== rcit.lambda ===

rcit.lambda

Short Description: Ridge regularization (λ)
Long Description: Ridge penalty used when residualizing feature maps of X and Y against Z. Prevents ill-conditioning when Z features are collinear.
Default Value: 0.001
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== rcit.numFeaturesXY ===

rcit.numFeaturesXY

Short Description: Random Fourier features for X and Y
Long Description: Number of random Fourier features for the tested variables X and Y. Small values (e.g., 3–10) often suffice; increasing improves power but adds cost.
Default Value: 5
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== rcit.numFeaturesZ ===

rcit.numFeaturesZ

Short Description: Random Fourier features for Z
Long Description: Number of random Fourier features used to represent the conditioning set Z. Larger values are more accurate but slower; values in the 50–300 range are typical.
Default Value: 100
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== rcit.permutations ===

rcit.permutations

Short Description: Permutations for "perm" approx
Long Description: Number of permutations used when rcit.approx = "perm". Ignored for analytic approximations (lpd4, hbe, gamma, chi2).
Default Value: 500
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== rcit.rcitMode ===

rcit.rcitMode

Short Description: Use RCIT (true) or RCoT (false)
Long Description: Chooses between the two randomized kernel tests: RCIT augments Y with Z features (tests X ⟂ Y,Z | Z), while RCoT uses only X and Y features with residualization against Z. In the original RCIT code base this switch is exposed as rcit=True/False.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== rcitNumFeatures ===

rcitNumFeatures

Short Description: The number of random features to use
Long Description:
Default Value: 10
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== recursive ===

recursive

Short Description: Yes if the algorithm should proceed recursively, no if not
Long Description: Where recursive or nonrecursive variants of an algorithm are available, this selects which one to use.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== regularizationLambda ===

regularizationLambda

Short Description: Small number >= 0 Add lambda to the the diagonal of correlation/covariance matrices. Default 1e-8.
Long Description: Small number >= 0 Add lambda to the the diagonal of correlation/covariance matricers. Default 1e-8.
Default Value: 1e-8
Lower Bound: 0
Upper Bound: Infinity
Value Type: Double

=== removeAlmostCycles ===

removeAlmostCycles

Short Description: Yes if almost-cycles should be removed from the PAG.
Long Description: When x <-> y, x ~~> y, removes any unshielded triples into x and rebuilds the PAG.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== removeEffectNodes ===

removeEffectNodes

Short Description: True if effect nodes should bre removed from possible causes
Long Description: True if effect nodes should be removed from possible causes
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== resamplingEnsemble ===

resamplingEnsemble

Short Description: Ensemble method: Preserved (1), Highest (2), Majority (3)
Long Description: Preserved = keep the highest frequency edges; Highest = keep the highest frequency edges but ignore the no edge case if maximal; Majority = keep edges only if their frequency is greater than 0.5.
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== resamplingWithReplacement ===

resamplingWithReplacement

Short Description: Yes, if sampling with replacement (bootstrapping)
Long Description: Yes if resampling can be done with replacement, No if not. or without replacement. If with replacement, it is possible to have more than one copy of some of the records in the original dataset being included in the bootstrap.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== resolveAlmostCyclicPaths ===

resolveAlmostCyclicPaths

Short Description: True just in case almost cyclic paths should be resolved in the direction of the cycle.
Long Description: If true we resolved <-> edges as --> if there is a directed path x~~>y.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== sampleSize ===

sampleSize

Short Description: Sample size (min = 1)
Long Description: Determines now many records should be generated for the data. The minimum number of records is 1; the default is set to 1000.
Default Value: 1000
Lower Bound: 1
Upper Bound: 2147483647
Value Type: Integer

=== sampleStyle ===

sampleStyle

Short Description: Sample style: 1 = Subsample 2 = Bootstrap
Long Description: Sample style: 1 = Subsample 2 = Bootstrap /span>
Default Value: 1
Lower Bound: 1
Upper Bound: 2
Value Type: Integer

=== saveBootstrapGraphs ===

saveBootstrapGraphs

Short Description: Yes if individual bootstrapping graphs should be saved
Long Description: Bootstrapping provides a summary over individual search graphs; select Yes here if these individual graphs should be saved
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== saveLatentVars ===

saveLatentVars

Short Description: Save latent variables.
Long Description: Yes if one wishes to have values for latent variables saved out with the rest of the data; No if only data for the measured variables should be saved.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== scaleFreeAlpha ===

scaleFreeAlpha

Short Description: For scale-free graphs, the parameter alpha (min = 0.0)
Long Description: We use the algorithm for generating scale free graphs described in B. Bollobas,C. Borgs, J. Chayes, and O. Riordan (2003). Please see this article for a description of the parameters.
Default Value: 0.05
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== scaleFreeBeta ===

scaleFreeBeta

Short Description: For scale-free graphs, the parameter beta (min = 0.0)
Long Description: We use the algorithm for generating scale free graphs described in B. Bollobas,C. Borgs, J. Chayes, and O. Riordan (2003). Please see this article for a description of the parameters.
Default Value: 0.9
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== scaleFreeDeltaIn ===

scaleFreeDeltaIn

Short Description: For scale-free graphs, the parameter delta_in (min = 0.0)
Long Description: We use the algorithm for generating scale free graphs described in B. Bollobas,C. Borgs, J. Chayes, and O. Riordan (2003). Please see this article for a description of the parameters.
Default Value: 3
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== scaleFreeDeltaOut ===

scaleFreeDeltaOut

Short Description: For scale-free graphs, the parameter delta_out (min = 0.0)
Long Description: We use the algorithm for generating scale free graphs described in B. Bollobas,C. Borgs, J. Chayes, and O. Riordan (2003). Please see this article for a description of the parameters.
Default Value: 3
Lower Bound: -2147483648
Upper Bound: 2147483647
Value Type: Integer

=== scalingFactor ===

scalingFactor

Short Description: Scaling factor.
Long Description: For Gaussian kernel: The scaling factor.
Default Value: 1.0
Lower Bound: 4.9E-324
Upper Bound: Infinity
Value Type: Double

=== seed ===

seed

Short Description: Seed for pseudorandom number generator (-1 = off)
Long Description: The seed is the initial value of the internal state of the pseudorandom number generator. A value of -1 skips setting a new seed.
Default Value: -1
Lower Bound: -1
Upper Bound: 9223372036854775807
Value Type: Long

=== selectionMinEffect ===

selectionMinEffect

Short Description: Minimum effect size for listing effects in the CStaR table
Long Description: Minimum effect size for listing effects in the CStaR table
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== selfLoopCoef ===

selfLoopCoef

Short Description: The coefficient for the self-loop (default 0.0)
Long Description: For simulating time series data, each variable depends on itself one time-step back with a linear edge that has this coefficient.
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== semBicRule ===

semBicRule

Short Description: Lambda: 1 = Chickering, 2 = Nandy
Long Description: The Chickering Rule uses the difference of BIC scores to add or remove edges. The Nandy et al. rule uses a single calculation of a partial correlation in place of the likelihood difference.
Default Value: 1
Lower Bound: 1
Upper Bound: 2
Value Type: Integer

=== semBicStructurePrior ===

semBicStructurePrior

Short Description: Structure Prior for SEM BIC (default 0)
Long Description: Structure prior; default is 0 (turned off); may be any positive number otherwise
Default Value: 0
Lower Bound: 0
Upper Bound: Infinity
Value Type: Double

=== semGicRule ===

semGicRule

Short Description: Lambda: 1 = ln n, 2 = pn^1/3, 3 = 2 ln pn, 4 = 2(ln pn + ln ln pn), 5 = ln ln n ln pn, 6 = ln n ln pn, 7 = Manual
Long Description: The rule used for calculating the lambda term of the score. We follow Kim, Y., Kwon, S., & Choi, H. (2012) and articles referenced therein. For high-dimensional data.
Default Value: 4
Lower Bound: 1
Upper Bound: 7
Value Type: Integer

=== semImSimulationType ===

semImSimulationType

Short Description: Yes if recursive simulation, No if reduced form simulation
Long Description: Determines the type of simulation done. If recursive, the graph must be a DAG in causal order. "Reduced form" means X = (I - B)^-1 e, which requires a possibly large matrix inversion.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== sepsetFinderMethod ===

sepsetFinderMethod

Short Description: The method to use for finding sepsets, 1 = Greedy, 2 = Min-p, 3 = Max-p (default).
Long Description: The method to use for finding sepsets, 1 = Greedy, 2 = Min-p, 3 = Max-p (default).
Default Value: 3
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== shrinkageMode ===

shrinkageMode

Short Description: Shrinkage Mode: 1 = None 2 = Ridge 3 = Ledoit-Wolf
Long Description: Shrinkage Mode: 1 = None 2 = Ridge 3 = Ledoit-Wolf
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== significanceChecked ===

significanceChecked

Short Description: True if the significance of the cluster should be checked.
Long Description: True if the significance of clusters should be checked, false if not.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== simulationErrorType ===

simulationErrorType

Short Description: 1 = Usual LG SEM, 2 = U(lb, ub), 3 = Exp(lambda), 4 = Gumbel(mu, beta), 5 = Gamma(shape, scale)
Long Description: Exogenous error type
Default Value: 1
Lower Bound: 1
Upper Bound: 5
Value Type: Integer

=== simulationParam1 ===

simulationParam1

Short Description: Indep error parameter #1
Long Description: Exogenous error parameter #1
Default Value: 0.0
Lower Bound: -1000
Upper Bound: 1000
Value Type: Double

=== simulationParam2 ===

simulationParam2

Short Description: Indep error parameter #2, if used
Long Description: Exogenous error parameter #2
Default Value: 1.0
Lower Bound: -1000
Upper Bound: 1000
Value Type: Double

=== singularityLambda ===

singularityLambda

Short Description: Singularities: Small number >= 0 Add lambda to the the diagonal, < 0 Pseudoinverse
Long Description: Singularities: Small number >= 0 Add lambda to the the diagonal, < 0 Pseudoinverse
Default Value: 0.0
Lower Bound: -Infinity
Upper Bound: Infinity
Value Type: Double

=== skewEdgeThreshold ===

skewEdgeThreshold

Short Description: Threshold for including additional edges detectable by skewness
Long Description: For FASK, this includes an adjacency X—Y in the model if |corr(X, Y | X > 0) – corr(X, Y | Y > 0)| exceeds some threshold. The default for this threshold is 0.3.
Default Value: 0.3
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== skipNumRecords ===

skipNumRecords

Short Description: Number of records that should be skipped between recordings (min = 0)
Long Description: Data recordings are made every this many steps.
Default Value: 0
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== stableFAS ===

stableFAS

Short Description: Yes if the Colombo et al. 'stable' FAS should be done, to avoid skeleton order dependency
Long Description: If Yes, the "stable" version of the PC adjacency search is used, which for k > 0 fixes the graph for depth k + 1 to that of the previous depth k.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== standardize ===

standardize

Short Description: Yes if the data should be standardized
Long Description: Yes if each variable in the data should be standardized to have mean zero and variance 1.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== startFromCompleteGraph ===

startFromCompleteGraph

Short Description: Yes, if the procedure should start from a complete graph
Long Description: Yes, if the procedure should start from a complete graph
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== structurePrior ===

structurePrior

Short Description: Structure prior coefficient (min = 0.0)
Long Description: The default number of parents for any conditional probability table. Higher weight is accorded to tables with about that number of parents. The prior structure weights are distributed according to a binomial distribution.
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== symmetricFirstStep ===

symmetricFirstStep

Short Description: Yes if the first step for FGES should do scoring for both X->Y and Y->X
Long Description: If Yes, scores for both X->Y and X<-Y will be calculated and the higher score used.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== takeLogs ===

takeLogs

Short Description: Yes logs should be taken, No if not
Long Description: The formula for the score allows a log to be taken optionally in the information term.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== targetName ===

targetName

Short Description: Target variable name
Long Description: The name of the target variables--for Markov blanket searches, this is the name of the variable for which one wants the Markov blanket or Markov blanket graph.
Default Value:
Lower Bound:
Upper Bound:
Value Type: String

=== targets ===

targets

Short Description: Target names (comma or space separated)
Long Description: Target names (comma or space separated).
Default Value:
Lower Bound:
Upper Bound:
Value Type: String

=== testTimeout ===

testTimeout

Short Description: Timeout for tests in milliseconds, or -1 if no timeout.
Long Description: Timeout for tests in milliseconds, or -1 if no timeout.
Default Value: -1
Lower Bound: -1
Upper Bound: 9223372036854775807
Value Type: Long

Short Description: Yes if the algorithm should try moving variables pairwise
Long Description: In some cases, two moves are required simultaneously to get an orientation right in the final step. This is not generally needed when optimizing using BIC or for large models.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== tetrad_test_bpc ===

tetrad_test_bpc

Short Description: The tetrad test used: 1 = Wishart, 2 = Delta (Bollen-Ting)
Long Description: The tetrad test used: 1 = Wishart, 2 = Delta
Default Value: 2
Lower Bound: 1
Upper Bound: 2
Value Type: Integer

=== tetrad_test_fofc ===

tetrad_test_fofc

Short Description: The tetrad test used: 1 = CCA, 2 = Bollen-Ting, 3 = Wishart
Long Description: The tetrad test used: 1 = CCA, 2 = Bollen-Ting, 3 = Wishart
Default Value: 1
Lower Bound: 1
Upper Bound: 4
Value Type: Integer

=== thr ===

thr

Short Description: THR parameter (GLASSO) (min = 0.0)
Long Description: Sets the maximum number of iterations of the optimization loop.
Default Value: 1.0E-4
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== thresholdBHat ===

thresholdBHat

Short Description: Threshold on the B Hat matrix.
Long Description: The estimated B matrix is thresholded by setting small entries less than this threshold to zero.
Default Value: 0.1
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== thresholdForNumEigenvalues ===

thresholdForNumEigenvalues

Short Description: Threshold to determine how many eigenvalues to use--the lower the more (0 to 1)
Long Description: Referring to Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2012), this parameter is the threshold to determine how many eigenvalues to use--the lower the more (0 to 1).
Default Value: 0.001
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== thresholdNoRandomConstrainSearch ===

thresholdNoRandomConstrainSearch

Short Description: Yes, if using the cutoff threshold for the meta-constraints independence test (stage 2).
Long Description: Yes, if using the cutoff threshold for the meta-constraints independence test (stage 2).
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== thresholdNoRandomDataSearch ===

thresholdNoRandomDataSearch

Short Description: Yes, if using the cutoff threshold for the constraints independence test (stage 1).
Long Description: null
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== thresholdW ===

thresholdW

Short Description: Threshold on the W matrix.
Long Description: The estimated W matrix is thresholded by setting small entries less than this threshold to zero.
Default Value: 0.1
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== timeLag ===

timeLag

Short Description: For time lag searches,`a time lag, automatically applied (zero if none)
Long Description: Automatically applies the time lag transform to the data, creating additional lagged variables. If zero, no time lag is applied. A positive integer
Default Value: 0
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== timeLagReplicatingGraph ===

timeLagReplicatingGraph

Short Description: For time lag searches, whether to make the graph replicate edges across time lags, SVAR-style
Long Description: For time lag searches, whether to make the graph replicate edges across time lags, SVAR-style
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== timeLimit ===

timeLimit

Short Description: Time limit
Long Description: T-Separation requires a time limit. Default 1000.
Default Value: 1000.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== timeout ===

timeout

Short Description: Timeout (best graph returned, -1 = no timeout)
Long Description: The algorithm will time out at approximately this number of seconds from when it started and return the final graph found at that point.
Default Value: -1
Lower Bound: -1
Upper Bound: 2147483647
Value Type: Integer

=== topBracket ===

topBracket

Short Description: Top bracket to look for causes in
Long Description: Top bracket, 'q'
Default Value: 5
Lower Bound: 1
Upper Bound: 500000
Value Type: Integer

=== trimmingStyle ===

trimmingStyle

Short Description: Trimming Style: 1 = None, 2 = Adjacencies, 3 = MB DAG, 4 = Possibly directed paths
Long Description: 'Adjacencies' trims to the adjacencies the targets, MB DAGs to the Union(MB(targets)) U targets, potentially directed trims to nodes with potentially directed paths to the targets.
Default Value: 3
Lower Bound: 1
Upper Bound: 4
Value Type: Integer

=== trueErrorVariance ===

trueErrorVariance

Short Description: True error variance
Long Description: The true error variance of the model, assuming this is the same for all variables.
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== truncationLimit ===

truncationLimit

Short Description: Truncation limit for basis functions
Long Description: Basis functions 1 though this number will be used.. The Degenerate Gaussian category indicator variables for mixed data are also used.
Default Value: 3
Lower Bound: 1
Upper Bound: 1000
Value Type: Integer

=== tscClusterRank ===

tscClusterRank

Short Description: TSC cluster rank (if desired)
Long Description: TSC cluster rank (if desired)
Default Value: 1
Lower Bound: 0
Upper Bound: 500
Value Type: Integer

=== tscClusterSize ===

tscClusterSize

Short Description: TSC cluster size (if desired)
Long Description: TSC cluster size (if desired)
Default Value: 2
Lower Bound: 0
Upper Bound: 500
Value Type: Integer

=== tscEnableHierarchy ===

tscEnableHierarchy

Short Description: Yes, if hierarchical latents should be detected
Long Description: Yes, if hierarchical latents should be detected
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== tscMinRankDrop ===

tscMinRankDrop

Short Description: Min rank drop for detecting hierarchical latents
Long Description: Min rank drop for detecting hierarchical latents
Default Value: 1
Lower Bound: 1
Upper Bound: 100
Value Type: Integer

=== tscMinRedundancy ===

tscMinRedundancy

Short Description: Minimum redundancy for clusters beyond size = rank + 1
Long Description: Minimum redundancy: require at least k extra indicators per latent (|C| ≥ r+1+k). Higher values suppress trivially sized clusters.
Default Value: 0
Lower Bound: 0
Upper Bound: 1000
Value Type: Integer

=== tscMode ===

tscMode

Short Description: TSC mode: 1 = Metaloop, 2 = Specific size/rank
Long Description: TSC mode: 1 = Metaloop, 2 = Specific cluster size/rank
Default Value: 1
Lower Bound: 1
Upper Bound: 2
Value Type: Integer

=== tscPcUseBoss ===

tscPcUseBoss

Short Description: Yes, if the procedure should use BOSS (with the BOSS-specific parameters) and not PC
Long Description: Yes, if the procedure should use BOSS (with the BOSS-specific parameters) and not PC
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== tscSingletonPolicy ===

tscSingletonPolicy

Short Description: Singletons: 1 = Exclude 2 = Include 3 = Collect as Noise
Long Description: Singletons: 1 = Exclude 2 = Include 3 = Collect as Noise
Default Value: 1
Lower Bound: 1
Upper Bound: 3
Value Type: Integer

=== twoCycleAlpha ===

twoCycleAlpha

Short Description: Alpha orienting 2-cycles (min = 0.0)
Long Description: The alpha level of a T-test used to determine where 2-cycles exist in the graph. A value of zero turns off 2-cycle detection.
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== twoCycleScreeningThreshold ===

twoCycleScreeningThreshold

Short Description: Upper bound for |left-right| to count as 2-cycle. (Set to zero to turn off pre-screening.)
Long Description: 2-cycles are screened by looking to see if the left-right rule returns a difference smaller than this threshold. To turn off the screening, set this to zero.
Default Value: 0.0
Lower Bound: 0.0
Upper Bound: Infinity
Value Type: Double

=== upperBound ===

upperBound

Short Description: Upper bound cutoff threshold
Long Description: null
Default Value: 0.7
Lower Bound: 0.0
Upper Bound: 1.0
Value Type: Double

=== useBes ===

useBes

Short Description: True if the optional BES step should be used
Long Description: This algorithm can use the backward equivalence search from the GES algorithm as one of its steps.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== useCorrDiffAdjacencies ===

useCorrDiffAdjacencies

Short Description: Yes if adjacencies from conditional correlation differences should be used
Long Description: FASK can use adjacencies X—Y where |corr(X,Y|X>0) – corr(X,Y|Y>0)| > threshold. This expression will be nonzero only if there is a path between X and Y; heuristically, if the difference is greater than, say, 0.3, we infer an adjacency.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== useDataOrder ===

useDataOrder

Short Description: Yes just in case data variable order should be used for the first initial permutation.
Long Description: In either case, if multiple starting points are used, taking the best scoring model from among these, subsequent starting points will all be random shuffles.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== useFasAdjacencies ===

useFasAdjacencies

Short Description: Yes if adjacencies from the FAS search (correlation) should be used
Long Description: Determines whether adjacencies found by conditional correlation should be included in the final model.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== useGap ===

useGap

Short Description: Yes if the GAP algorithms should be used. Not if the SAG algorithm should be used
Long Description: True if one should first find all possible initial sets, grows these out, and then picks a non-overlapping such largest sets from these. Not if one should grow pure clusters one at a time, excluding variables found in earlier clusters.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== useMaxPHeuristic ===

useMaxPHeuristic

Short Description: Yes if the max P heuristic version should be used to search for sepsets
Long Description: Yes if the max P heuristic version should be used to search for sepsets
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== useMaxPOrientationHeuristic ===

useMaxPOrientationHeuristic

Short Description: Use the max p heuristic version
Long Description: Use the max p heuristic version
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== useScore ===

useScore

Short Description: Yes if the score should be used; no if the test should be used
Long Description: BOSS can run either from a score or a test; this lets you choose which.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== useSkewAdjacencies ===

useSkewAdjacencies

Short Description: Yes if adjacencies based on skewness should be used
Long Description: FASK can use adjacencies X—Y where |corr(X,Y|X>0) – corr(X,Y|Y>0)| > threshold. This expression will be nonzero only if there is a path between X and Y; heuristically, if the difference is greater than, say, 0.3, we infer an adjacency. To see adjacencies included for this reason, set this parameter to “Yes”. Sanchez-Romero, Ramsey et al., (2018) Network Neuroscience.
Default Value: true
Lower Bound:
Upper Bound:
Value Type: Boolean

=== varHigh ===

varHigh

Short Description: High end of variance range (min = 0.0)
Long Description: The parameter 'b' for drawing independent variance values, from +U(a, b).
Default Value: 3.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== varLow ===

varLow

Short Description: Low end of variance range (min = 0.0)
Long Description: The parameter 'a' for drawing independent variance values, from +U(a, b).
Default Value: 1.0
Lower Bound: 0.0
Upper Bound: 1.7976931348623157E308
Value Type: Double

=== verbose ===

verbose

Short Description: Yes if verbose output should be printed or logged
Long Description: If this parameter is set to ‘Yes’, extra (“verbose”) output will be printed if available giving some details about the step-by-step operation of the algorithm.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== verbose ===

verbose

Short Description: Yes if the (MimBuild) structure model should be included in the output graph
Long Description: FOFC proper yields a measurement model--that is, a set of pure children for each of the discovered latents. One can estimate the structure over the latents (the structure model) using Mimbuild. This structure model is included in the output if this parameter is set to Yes.
Default Value: false
Lower Bound:
Upper Bound:
Value Type: Boolean

=== wThreshold ===

wThreshold

Short Description: wThreshold
Long Description: Tuning parameter for DAGMA
Default Value: 0.1
Lower Bound: 0
Upper Bound: Infinity
Value Type: Double

=== zsMaxIndegree ===

zsMaxIndegree

Short Description: Maximum indegree of true graph (min = 0)
Long Description: This is the maximum number of parents one expects any node to have in the true model.
Default Value: 4
Lower Bound: 0
Upper Bound: 2147483647
Value Type: Integer

=== zSRiskBound ===

zSRiskBound

Short Description: Risk bound
Long Description: This is the probability of getting the true model if a correct model is discovered. Could underfit.
Default Value: 0.1
Lower Bound: 0
Upper Bound: 1
Value Type: Double