Extreme.
The Extreme.Statistics namespace contains classes that are used to represent statistical models.
Classes
| Anova | Represents an Analysis of Variance (ANOVA) model. |
| Anova | Represents a row representing a contribution from the model in an AnovaTable. |
| Anova | Represents a row in an AnovaTable. |
| Anova | Represents a table containing the results of an ANOVA analysis. |
| Contingency | Represents a table that cross-tabulates totals from two categorical variables. |
| Descriptives<T> | Collects descriptive statistics for a variable. |
| Filter | Represents a filter that can be used to select observations in a Vector<T> or IDataFrame. |
| Generalized | Represents a generalized linear model. |
| Hypothesis | Contains static methods to create hypothesis tests. |
| Kernel | Represents a kernel used for kernel density estimation. |
| Kernel | Contains methods for computing kernel density estimates. |
| Linear | Represents a linear regression model. |
| Link | Represents a link function in a GeneralizedLinearModel. |
| Logistic | Represents a logistic regression model. |
| Model | Represents a family of distributions for the dependent variable in a GeneralizedLinearModel. |
| Nonlinear | Represents a nonlinear regression model. |
| One | Represents the results of a one-way analysis of variance (ANOVA). |
| One | Represents an analysis of variance (ANOVA) calculation. |
| Polynomial | Represents a polynomial regression model. |
| Regression | Represents information about a step in a stepwise regression. |
| Regularized | Represents a regularized (ridge or LASSO) regression model. |
| Simple | Represents a linear regression model. |
| Stats | Provides static methods for descriptive statistics and other statistical functions. |
| Stepwise | Specifies options for stepwise regression calculations. |
| Two | Represents a two-way within-subjects Analysis of Variance (ANOVA) model. |
| Window | Represents a sliding window on a variable or variable collection. |
Structures
| Cell | Represents a data cell in an AnovaModel. |
| Contingency | Represents a bin in a ContingencyTable. |
| Date | Represents an interval of real numbers. |
Enumerations
| Anova | Enumerates the possible types of rows in an AnovaTable. |
| Kernel | Enumerates the options for estimating the bandwidth in kernel density estimation. |
| Logistic | Enumerates the variants of logistic regression that can be represented by a LogisticRegressionModel. |
| Nearest | Enumerates the possible algorithms for computing the nearest correlation matrix. |
| Regression | Enumerates the operations that may be performed during a single step in stepwise regression. |
| Scale | Enumerates the possible ways to estimate the scale parameter in a generalized linear model. |
| Simple | Enumerates the different kinds of regression between two variables. |
| Stepwise | Enumerates the possible ways to define the threshold for the to-enter and to-remove values. |
| Stepwise | Enumerates the possible ways to perform a stepwise regression. |
| Sums | Enumerates the types of sums of squares available when computing an ANOVA table. |
| Test | Enumerates the choices when testing whether a number of samples have the same variance. |
| Test | Enumerates the choices when testing whether a sample follows a normal distribution. |