statsmodels.sandbox.distributions.transformed.LogTransf_gen class statsmodels.sandbox.distributions.transformed.LogTransf_gen(kls, *args, **kwargs) [source] Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable 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_

statsmodels.sandbox.distributions.transformed.lognormalg statsmodels.sandbox.distributions.transformed.lognormalg = a class for non-linear monotonic transformation of a continuous random variable

statsmodels.sandbox.distributions.transformed.invdnormalg statsmodels.sandbox.distributions.transformed.invdnormalg = a class for non-linear monotonic transformation of a continuous random variable

statsmodels.sandbox.distributions.transformed.loggammaexpg statsmodels.sandbox.distributions.transformed.loggammaexpg = univariate distribution of a non-linear monotonic transformation of a random variable

statsmodels.sandbox.distributions.transformed.ExpTransf_gen class statsmodels.sandbox.distributions.transformed.ExpTransf_gen(kls, *args, **kwargs) [source] Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable 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_

statsmodels.sandbox.distributions.transformed.absnormalg statsmodels.sandbox.distributions.transformed.absnormalg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus,

statsmodels.sandbox.distributions.extras.SkewNorm_gen class statsmodels.sandbox.distributions.extras.SkewNorm_gen [source] univariate Skew-Normal 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 expec

statsmodels.sandbox.distributions.extras.SkewNorm2_gen class statsmodels.sandbox.distributions.extras.SkewNorm2_gen(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None) [source] univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern 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)

statsmodels.sandbox.distributions.extras.skewnorm2 statsmodels.sandbox.distributions.extras.skewnorm2 = univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern

statsmodels.sandbox.distributions.extras.pdf_mvsk statsmodels.sandbox.distributions.extras.pdf_mvsk(mvsk) [source] Return the Gaussian expanded pdf function given the list of 1st, 2nd moment and skew and Fisher (excess) kurtosis. Parameters: mvsk : list of mu, mc2, skew, kurt distribution is matched to these four moments Returns: pdffunc : function function that evaluates the pdf(x), where x is the non-standardized random variable. Notes Changed so it works only if four arguments are