Package edu.cmu.tetrad.data
package edu.cmu.tetrad.data
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ClassDescriptionBase class for variable specifications for DataSet.Implements the Anderson-Darling test for normality, with P values calculated as in R's ad.test method (in package nortest).Provides a static method for sampling with replacement from a dataset to create a new dataset with a sample size supplied by the user.Wraps a DataBox in such a way that mixed data sets can be stored.Stores a 2D array of byte data.Makes a new data set in which cases in the given data set that have been assigned multiplicies other than n = 1 are copied out n times.Represents a cell table that stores the values of the cells in a table of arbitrary dimension, for use, e.g., in the context of a contingency table--e.g.Stores a cell count table of arbitrary dimension.Stores a cell count table of arbitrary dimension.Specifies how a column (continuous or discrete) should be discretized.Represents a real-valued variable.Stores a correlation matrix together with variable names and sample size; intended as a representation of a data set.Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.Some comemon methods for the various covariance implementations.Computes covariances using the standard calculation.Stores a 2D array of data.Interface implemented by classes whose purpose is to generate modifications of data sets.Interface implemented by classes, instantiations of which can serve as data models in Tetrad.Stores a list of data models and keeps track of which one is selected.A utility for resampling dataset.Implements a rectangular data set, in the sense of being a dataset with a fixed number of columns and a fixed number of rows, the length of each column being constant.DataTransforms class.The type of the data set--continuous if all continuous variables, discrete if all discrete variables; otherwise, mixed.Some static utility methods for dealing with data sets.Provides static methods for saving data to files.Type-safe enum of delimiter types for parsing data.Specifies how a column (continuous or discrete) should be discretized.Represents a discrete variable as a range of integer-valued categories 0, 1, ..., m - 1, where m is the number of categories for the variable.Type-safe enum of discrete variable types.A continuous or discrete discretization spec (see).Discretizes individual columns of discrete or continuous data.A discretization specification for a continuous variable.Stores a 2D array of double data.Estimates the mean and covariance matrix of a continuous dataset with missing values by maximum likelihood, under a saturated multivariate Gaussian model, using the EM algorithm.The result of an EM run: the estimated mean and covariance, together with convergence information.Stores a 2D array of float data.Implements the Anderson-Darling test against the given CDF, with P values calculated as in R's ad.test method (in package nortest).Model for a conditional histogram for mixed continuous and discrete variables.Interface for covariance matrices.Stores a list of independence facts.Stores a 2D array of integer data.Joins two tabular data sets on a key, in the manner of a database join: each row of the left data set is looked up in the right data set by its key value(s), and the right data set's non-key columns are appended to it.Which unmatched rows to keep.The joined data set and a findings-only report.Stores information about required and forbidden edges and common causes for use in algorithm.Implements a knowledge edge X-->Y as a simple ordered pair of strings.Represents a "Other Group" in Knowledge, which can be understood as: Group1 -> Group2 where there are edges between all members of Group1 to Group2.An additional, named tier structure over (a subset of) the variables of a
Knowledgeobject, independent of the main temporal tiers and of every other tier structure.Interface implemented by classes that are capable of participating in the transfer of knowledge objects.Linearizing ("un-warping") transform: estimates, for each variable, a monotone Yeo-Johnson transform chosen to make that variable's relations to the other variables as LINEAR as possible, and returns the dataset with all transforms applied (columns standardized).Sundry methods for logging data.A logarithmic transform to apply to a single variable: the direction (log or unlog), the offset a, and the base.Stores a 2D array of long data.Reshapes a long-format data set (one row per unit per occasion, e.g., one row per student per assessment) into a wide-format data set (one row per unit, one column per occasion per measured variable).How multiple long rows for the same (unit, level) combination are combined into one wide cell.The wide data set, the unit keys in row order (one list of row-key values per wide row), and a findings-only report.Returns a data set in which missing values in each column are filled using the mean of that column.Injects missing values into a dataset under a per-cell logistic propensity, with optional terms that make the mechanism MAR or MNAR as well as MCAR.One term in the deletion propensity of a target variable: the effect of a driver variable's standardized value, or of its missingness indicator, on the log odds that the target is deleted.The terms of the deletion mechanism beyond the per-variable level and the row effect.The mechanism class of an injection.What the mechanism was, and how monotone the realized pattern is.A report of what was actually injected, for checking that the realized pattern is the intended one.The injected data together with its reports.The injection specification.Stores a 2D array of double continuousData.Implements the Anderson-Darling test against the given CDF, with P values calculated as in R's ad.test method (in package nortest).Wraps a 2D array of Number objects in such a way that mixed data sets can be stored.Provides a static method for sampling without replacement from a dataset to create a new dataset with a sample size supplied by the user.Tokenizes the given input character sequence using the type of delimiter specified bythe given CPDAG.Stores a 2D array of short data.SimpleDataLoader class.Created by jdramsey on 12/22/15.Specifies how a column (continuous or discrete) should be discretized.Stores time series data as a list of continuous columns.Minimal, UI-friendly spec for the unmixer.The ColliderStyle enum represents different styles for handling colliders during graph learning processes.The CovarianceMode enum represents different modes for handling covariance calculations during graph learning processes.The GraphLearner enum represents different algorithms or strategies used for graph learning processes within a system.The SupersetScore enum represents statistical ranking correlation coefficients that can be used to assess the relationship between two rankings.Interface implemented by classes, instantiations of which are capable of serving as variables for columns in a DataSet.Inteface implemented by classes, instantiations of which are associated with lists of variables.Stores a 2D array of double data.Stores a 2D array of int data.