Package edu.cmu.tetrad.sem
package edu.cmu.tetrad.sem
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ClassDescriptionAdditiveNoiseSimulationParameters for model adequacy assessment.Represents a report evaluating the adequacy of a model's performance relative to a baseline.Created by IntelliJ IDEA.Result of k-fold cross-validation performed by NNEstimator#crossValidate(int).Utilities to stabilize arbitrary directed graphs with cycles.Represents a functional interface for computing metrics on directed acyclic graphs (DAGs).Represents a functional interface for computing a metric value from a
DataSetandGraph.Registry class that provides utility methods for working with DAG metrics.Represents the result of a metric related to a Directed Acyclic Graph (DAG), including its name, value, an associated note, and an indication of whether a higher or lower value is considered better.Represents the evaluation criteria for a metric, describing whether a higher value, a lower value, or neither is considered "better" for the given context.Utility class that provides a set of predefined metrics for evaluating Directed Acyclic Graphs (DAGs).Estimates a SemIm given a CovarianceMatrix and a SemPm.Utility class containing various scoring methods for Directed Acyclic Graphs (DAGs).TheSamplerclass provides functionality for sampling values from a specified probability distribution.Result of a single edge-strength computation performed byNNEstimator.computeEdgeStrength(String, String, int).Only the cumulativeProbability, density, setShift methods are implemented.Returns a sample empirical distribution for a particular expression.TheExpressionSamplerclass provides functionality for parsing, storing, and evaluating mathematical expressions.GeneralAdditiveModelEstimates a Generalized SEM I'M given a Generalized SEM PM and a data set.Context.Represents a generalized SEM-instantiated model.Parametric model for a Generalized SEM model.General-noise simulator: X_j = f_j(Pa(X_j), e_j)An interface for SemIM's; see implementations.Stores a SEM model, pared down, for purposes of simulating data sets with large numbers of variables and sample sizes.Maps a parameter to the matrix element where its value is stored in the model.Pure-library neural-network estimator for a DAG factorization.Tuning parameters forNNEstimator.Represents the adequacy summary of a specific node in a model with information about its characteristics, performance metrics, and improvement over a baseline.Immutable per-node summary produced by k-fold cross-validation in NNEstimator#crossValidate(int).Represents a nonlinear function of linear model for generating synthetic data based on a directed acyclic graph (DAG).Represents the type of distortion applied in a nonlinear additive causal model.An enum of the types of the various comparisons a parameter may have with respect to one another for SEM estimation.A class for implementing constraints on the values of the freeParameters of of instances of the SemIm class.Enum for representing different types of parameter constraints.Stores information about the identity of a SEM parameter--its name, its type (COEF, COVAR), and the node(s) it is associated with.Implements an ordered pair of objects (a, b) suitable for storing in HashSets.An enum of the free parameter types for SEM models (COEF, MEAN, VAR, COVAR).Result of a partial edge strength computation performed byNNEstimator.computePartialEdgeStrength(String, String, int).Simple command-line driver for PlotMatrixMimicSimulator.PlotMatrixMimicSimulator (continuous-only, v1)Represents a Post-nonlinear Causal Model (Zhang and Hyvarinen, 2009, 2012).Result ofNNEstimator.pruneByPredictiveContribution(int, double): a per-child backward elimination of parents whose held-out predictive contribution is not distinguishable from zero.One proposed edge deletion.Utility for reidentifying variables for multiple indicator structure searches.The covariance among the measured variables that an estimated SEM leaves unexplained: the sample covariance minus the covariance the model implies, with the model's error variances put back on the diagonal.Implements ICF as specified in Drton and Richardson (2003), Iterative Conditional Fitting for Gaussian Ancestral Graph Models, using hints from previous implementations by Drton in the ggm package in R and by Silva in the Purify class.The fit con graph result.RICF result.Residual Iterative Conditional Fitting (RICF) for Gaussian ADMG / MAG models (directed and bidirected edges only; no undirected/selection block).Result of RICF: the implied covariance, the error covariance, both coefficient matrices (BetaandI - Beta), the sweep count, the final convergence metric, and the Gaussian log-likelihood of the fitted model.Represents a generic sampling interface that provides a method to obtain a sampled value.Interface for a class that represents a scoring of a SEM model.Author : Jeremy Espino MD Created 1/12/18 2:05 PMEstimates a SemIm given a CovarianceMatrix and a SemPm.Implements the Gibbs sampler apporach to obtain samples of arbitrary size from the posterior distribution over the freeParameters of a SEM given a continuous dataset and a SemPm.Stores the freeParameters for an instance of a SemEstimatorGibbs.Stores information for a SemIm about evidence we have for each variable as well as whether each variable has been manipulated.Stores an instantiated structural equation model (SEM), with error covariance terms, possibly cyclic, suitable for estimation and simulation.Represents the result of a cyclic simulation.Represents the result of a computation or analysis, encapsulating the following: - The shrinkage mode used in an independent test.This class provides functionality for rendering a parameterized structural equation model (SEM) into lavaan syntax.Stores information for a BayesIm about evidence we have for each variable as well as whether each variable has been manipulated.Interface for algorithm that optimize the fitting function of a SemIm model by adjusting its freeParameters in search of a global maximum.Optimizes a DAG SEM with hidden variables using expectation-maximization.Optimizes a SEM using Powell's method from the Apache library.Optimizes a DAG SEM by regressing each varaible onto its parents using a linear regression.Optimizes a SEM using RICF for ADMGs (no selection bias).Optimizes a SEM by randomly selecting points in cubes of decreasing size about a given point.Parametric model for Structural Equation Models.Represents propositions over the variables of a particular BayesIm describing and event of a fairly general sort--namely, conjunctions of propositions that particular variables take on values from a particular disjunctive list of categories.Computes Satorra-Bentler robust ("sandwich") standard errors and the Satorra-Bentler scaled chi-square for an estimated SEM, correcting normal-theory inference for non-normal (e.g., heavy-tailed) data.Random starting values for a SEM's free parameters that respect the scale of the sample covariance.Includes methods for estimating the standard errors of the freeParameters of an estimated SEM.Calculates updated structural equation models given evidence of the form X1=x1',...,The main task of such and algorithm is to calculate P(X = x' | evidence), where evidence takes the form of a Proposition over the variables in the Bayes net, possibly with additional information about which variables in the Bayes net have been manipulated.This class takes an xml element representing a SEM semIm and converts it to a SemIMThis class converts a SemIm into xml.A special SEM model in which variances of variables are always 1 and means of variables are always 0.The initialization method for the model.Stores a coefficient range--i.e.Expands templates for the generalized SEM PM.Simple driver: args[0] = input data path (tab-delimited with header) args[1] = input DAG path (one directed edge per line, like "A -> B" or "A-->B" or "A->B") args[2] = output data path (tab-delimited) args[3] = output DAG path (edge list) args[4] = (optional) nSamples (default = input N)TheTrainedDagAdequacyclass provides methods to evaluate the adequacy of a trained Directed Acyclic Graph (DAG) by comparing real data and simulated data generated from the DAG model.Trained DAG simulator (ANM): learn local mechanisms from a real dataset and resimulateClass representing a report for a node in the simulation results.Defines various configuration parameters for simulations involving directed acyclic graphs (DAGs).Represents the result of a simulation, encapsulating the simulation data, variables, the true Directed Acyclic Graph (DAG), and optional diagnostic information.Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulateRepresents a report for a single node in a trained directed acyclic graph (DAG) structure.Represents a configuration class containing various hyperparameters and settings used for the simulation and training of a directed acyclic graph (DAG) model.Represents the result of a simulation, containing continuous and discrete data for variables, metadata about the simulation, and support for generating reports.Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulateRepresents a report for a single node in a trained directed acyclic graph (DAG) structure.Encapsulates configuration parameters for the TrainedDagSimulatorGNM class.Represents the result of a simulation, containing continuous and discrete data for variables, metadata about the simulation, and support for generating reports.Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulateRepresents a report for a single node in a trained directed acyclic graph (DAG) structure.Encapsulates configuration parameters for the TrainedDagSimulatorGNM class.Represents the result of a simulation, containing continuous and discrete data for variables, metadata about the simulation, and support for generating reports.