Package edu.cmu.tetrad.sem
package edu.cmu.tetrad.sem
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ClassDescriptionCreated by IntelliJ IDEA.Estimates a SemIm given a CovarianceMatrix and a SemPm.Only the cumulativeProbability, density, setShift methods are implemented.Returns a sample empirical distribution for a particular expression.Estimates 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.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.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).Utility for reidentifying variables for multiple indicator structure searches.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.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.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 (see that class).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.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 im 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.