Statistics on sample¶
Sample¶
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Sample of real vectors. |
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Select partition of samples. |
Building distributions from samples¶
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Base class for probability distribution factories. |
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Results of distribution estimation. |
Result from likelihood estimation. |
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Distribution factory result for profile likelihood estimation. |
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Estimation result class for a GEV or GPD model depending on covariates. |
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Estimation result class for a non stationary GEV or GPD model. |
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Arcsine factory. |
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Bernoulli factory. |
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Beta factory. |
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Binomial factory. |
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Burr factory. |
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Chi factory. |
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Chi-Square factory. |
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Dirac factory. |
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Dirichlet factory. |
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Exponential factory. |
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Fisher-Snedecor factory. |
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Frechet factory. |
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Gamma factory. |
GeneralizedExtremeValue factory. |
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Generalized Pareto factory. |
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Geometric factory. |
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Gumbel factory. |
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Histogram factory. |
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Inverse Normal factory. |
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Non parametric continuous distribution estimation by kernel smoothing. |
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Laplace factory. |
Least squares factory. |
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Logistic factory. |
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Lognormal factory distribution. |
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Log Uniform factory. |
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Maximum likelihood factory. |
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Meixner Distribution factory. |
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Estimation by method of moments. |
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Multinomial factory. |
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Negative Binomial factory. |
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Normal factory. |
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Pareto factory. |
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Poisson factory. |
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Estimation by matching quantiles. |
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Rayleigh factory. |
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Rice factory. |
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Skellam factory. |
SmoothedUniform factory. |
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Student factory. |
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Trapezoidal factory. |
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Triangular factory. |
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Truncated Normal factory. |
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Uniform factory. |
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UserDefined factory. |
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VonMises factory. |
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WeibullMin factory. |
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WeibullMax factory. |
Building copulas from samples¶
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AliMikhailHaq copula factory. |
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BernsteinCopula copula factory. |
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Clayton Copula factory. |
Farlie Gumbel Morgenstern Copula factory. |
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Frank Copula factory. |
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Gumbel Copula factory. |
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Independent Copula factory. |
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Normal Copula factory. |
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Plackett Copula factory. |
Student copula factory. |
Sensitivity Analysis¶
Refer to Sensitivity analysis using Sobol’ indices.
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Correlation analysis methods. |
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ANalysis of COVAriance method (ANCOVA). |
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Fourier Amplitude Sensitivity Testing (FAST). |
Sensitivity analysis using rank-based method. |
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Sensitivity analysis. |
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Sensitivity analysis using Martinez method. |
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Sensitivity analysis using Saltelli method. |
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Sensitivity analysis using Jansen method. |
Sensitivity analysis using MauntzKucherenko method. |
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Experiment to computeSobol' indices. |
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Sobol indices computation using iterative sampling. |
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Sobol simulation result. |
HSIC Indices¶
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Base class of HSICStat. |
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Unbiased HSIC statistics. |
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Biased HSIC statistics. |
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Base class of HSIC estimators. |
Implement a HSIC estimator for conditional analysis. |
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Implement a HSIC estimator for global analysis. |
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Implement a HSIC estimator for target analysis. |
Statistical tests¶
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Test result data structure. |
Goodness-of-fit metrics & tests¶
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Compute the Akaike information criterion. |
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Compute the Akaike information criterion (with correction for small data). |
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Compute the Bayesian information criterion. |
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Perform a goodness-of-fit test for 1-d discrete distributions. |
Compute the unscaled Kolmogorov distance between a sample and a distribution. |
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Perform a Kolmogorov goodness-of-fit test for 1-d continuous distributions. |
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Perform a Lilliefors goodness-of-fit test for 1-d continuous distributions. |
Evaluate whether a sample follows a normal distribution. |
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Evaluate whether a sample follows a normal distribution. |
Graphical tests¶
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Draw 2-d projections of a multivariate sample. |
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Draw 2-d projections of a multivariate sample plus marginals. |
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Draw a parallel coordinates plot. |
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Draw an Henry plot. |
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Draw kendall plot. |
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Plot a 1D linear model. |
Plot a 1D linear model's residuals. |
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Draw a PP-plot. |
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Draw a QQ-plot. |
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Draw a CDF-plot. |
Draw the upper tail dependence function. |
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Draw the upper extremal dependence function. |
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Draw the lower tail dependence function. |
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Draw the lower extremal dependence function. |
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Validation of GeneralizedExtremeValue inference. |
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Validation of GeneralizedExtremeValue inference. |
Hypothesis tests¶
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Test whether two discrete samples are independent. |
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Nested likelihood model selection. |
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Test whether two discrete samples are independent. |
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Test whether two samples have no rank correlation. |
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Test whether two discrete samples are independent. |
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Test whether two sample have no rank correlation. |
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Test whether two discrete samples are independent. |
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Test whether two samples have no rank correlation. |
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Test whether two samples follows the same distribution. |
Linear model tests¶
Test the nullity of the linear regression model coefficients. |
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Test zero mean value of the residual of the linear regression model. |
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Test the homoskedasticity of the linear regression model residuals. |
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Test the homoskedasticity of the linear regression model residuals. |
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Test the autocorrelation of the linear regression model residuals. |
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Test whether two discrete samples are not linear. |
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Test whether two discrete samples are independent. |
Model selection¶
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Select the best model according to the Akaike information criterion. |
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Select the best model according to the Akaike information criterion with correction. |
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Select the best model according to the Bayesian information criterion. |
Select the best model according to the goodness-of-fit test. |
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Select the best model according to the Kolmogorov goodness-of-fit test. |
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Select the best model according to the Lilliefors goodness-of-fit test. |