statsmodels.sandbox.distributions.extras.pdf_moments statsmodels.sandbox.distributions.extras.pdf_moments(cnt) [source] Return the Gaussian expanded pdf function given the list of central moments (first one is mean). Changed so it works only if four arguments are given. Uses explicit formula, not loop. Notes This implements a Gram-Charlier expansion of the normal distribution where the first 2 moments coincide with those of the normal distribution but skew and kurtosis can deviate from it. I

statsmodels.sandbox.distributions.extras.NormExpan_gen class statsmodels.sandbox.distributions.extras.NormExpan_gen(args, **kwds) [source] Gram-Charlier Expansion of Normal distribution class follows scipy.stats.distributions pattern but with __init__ Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...])

statsmodels.sandbox.distributions.extras.pdf_moments_st statsmodels.sandbox.distributions.extras.pdf_moments_st(cnt) [source] Return the Gaussian expanded pdf function given the list of central moments (first one is mean). version of scipy.stats, any changes ? the scipy.stats version has a bug and returns normal distribution

statsmodels.sandbox.distributions.extras.mvstdnormcdf statsmodels.sandbox.distributions.extras.mvstdnormcdf(lower, upper, corrcoef, **kwds) [source] standardized multivariate normal cumulative distribution function This is a wrapper for scipy.stats.kde.mvn.mvndst which calculates a rectangular integral over a standardized multivariate normal distribution. This function assumes standardized scale, that is the variance in each dimension is one, but correlation can be arbitrary, covariance = co

statsmodels.sandbox.distributions.extras.ACSkewT_gen class statsmodels.sandbox.distributions.extras.ACSkewT_gen [source] univariate Skew-T distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...]) Calculate expected val

statsmodels.sandbox.distributions.extras.mvnormcdf statsmodels.sandbox.distributions.extras.mvnormcdf(upper, mu, cov, lower=None, **kwds) [source] multivariate normal cumulative distribution function This is a wrapper for scipy.stats.kde.mvn.mvndst which calculates a rectangular integral over a multivariate normal distribution. Parameters: lower, upper : array_like, 1d lower and upper integration limits with length equal to the number of dimensions of the multivariate normal distribution.

statsmodels.sandbox.descstats.descstats statsmodels.sandbox.descstats.descstats(data, cols=None, axis=0) [source] Prints descriptive statistics for one or multiple variables. Parameters: data: numpy array : x is the data v: list, optional : A list of the column number or field names (for a recarray) of variables. Default is all columns. axis: 1 or 0 : axis order of data. Default is 0 for column-ordered data. Examples >>> descstats(data.exog,v=['x_1','x_2','x_3'])

statsmodels.sandbox.descstats.sign_test statsmodels.sandbox.descstats.sign_test(samp, mu0=0) Signs test. Parameters: samp : array-like 1d array. The sample for which you want to perform the signs test. mu0 : float See Notes for the definition of the sign test. mu0 is 0 by default, but it is common to set it to the median. Returns: M, p-value : See also scipy.stats.wilcoxon Notes The signs test returns M = (N(+) - N(-))/2 where N(+) is the number of values above mu0, N(-) is the num

Sandbox This sandbox contains code that is for various resons not ready to be included in statsmodels proper. It contains modules from the old stats.models code that have not been tested, verified and updated to the new statsmodels structure: cox survival model, mixed effects model with repeated measures, generalized additive model and the formula framework. The sandbox also contains code that is currently being worked on until it fits the pattern of statsmodels or is sufficiently tested. All s

statsmodels.robust.norms.RobustNorm.rho RobustNorm.rho(z) [source] The robust criterion estimator function. Abstract method: -2 loglike used in M-estimator