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The discrete random variable is ``y = floor(x + offset)`` where x is the continuous random variable. Warning: not verified for all methods. add_scale : bool If True (default), then the scale of the base distribution is added as parameter for the discrete distribution. The scale parameter is in the last position. kwds : keyword arguments The extra keyword arguments are used delegated to the ``__init__`` of the super class. Their usage has not been checked, e.g. currently the support of the distribution is assumed to be all non-negative integers. Notes ----- `loc` argument is currently not supported, scale is not available for discrete distributions in scipy. The scale parameter of the underlying continuous distribution is the last shape parameter in this DiscretizedCount distribution if ``add_scale`` is True. The implementation was based mainly on [1]_ and [2]_. However, many new discrete distributions have been developed based on the approach that we use here. Note, that in many cases authors reparameterize the distribution, while this class inherits the parameterization from the underlying continuous distribution. References ---------- .. [1] Chakraborty, Subrata, and Dhrubajyoti Chakravarty. "Discrete gamma distributions: Properties and parameter estimations." Communications in Statistics-Theory and Methods 41, no. 18 (2012): 3301-3324. .. [2] Alzaatreh, Ayman, Carl Lee, and Felix Famoye. 2012. “On the Discrete Analogues of Continuous Distributions.” Statistical Methodology 9 (6): 589–603. c*>[[U] U5$r&)superr__new__)clsargskwds __class__s rrDiscretizedCount.__new__*s[#.s33rc >XlX lURUlX0lURbf[ URR S55UlU(a5URSURS-05 U=RS- slO)U(aURSS05 SUlOSUl[TU]("S0UD6 g)N,shapesz, srrrr ) distrd_offset _ctor_param add_scalerlensplitk_shapesupdater__init__)rrrrrrs rrDiscretizedCount.__init__/s   ,," << # 2 23 78DM Xu||e';<= "  XsO, ! !   4 rcB>[TU]5nURUS'U$)Nr)r_updated_ctor_paramr)rdicrs rr$DiscretizedCount._updated_ctor_paramDs"g)+zzG  rcHUR(a USnUSSnX4$SnX4$)Nrr)r)rrscales r _unpack_argsDiscretizedCount._unpack_argsIs6 >>HE9D{E{rN)r random_statec URU5up4Uc [USS5n[R"URR "X4UUS.6UR -5nU$)N_sizer)rrr)rgetattrrtruncrrvsr)rrrrrrvs r_rvsDiscretizedCount._rvsQs_''-  <4!,D XXdjjnndd2>@mm$% rcURnURS:waXR-nURU5up$UR"U/UQ7SU06UR"US-/UQ7SU06- nU$)Nrrr)rrrsfrrrrrrs rr)DiscretizedCount._pmfZsq  ==A MM!A''-  XXa ,$ ,e , XXa!e 0d 0% 01rcURnURU5up$URS:waXR-nUR"US-/UQ7SU06nU$Nrrr)rrrrTrs rrUDiscretizedCount._cdfesV ''-  ==A MM!A IIa!e 0d 0% 0rcURnURU5up$URS:waXR-nUR"US-/UQ7SU06nU$r)rrrrrs r_sfDiscretizedCount._sfmsV ''-  ==A MM!A HHQU /T / /rcURnURU5up$UR"U/UQ7SU06nURS:waXPR-n[R "US-5nU$Nrrg?)rrrYrrfloorrrrrrqcr]s rr_DiscretizedCount._ppfue ''-  YYq -4 -u - ==A mm#B HHR9% &rcURnURU5up$UR"U/UQ7SU06nURS:waXPR-n[R "US-5nU$r)rrisfrrrrs r_isfDiscretizedCount._isfr r)rrrrr)rT)r5r6r7r8r9rrrrrr)rUrr_r r: __classcell__rs@rrrsG,\4 !*  $$ rrcF^\rSrSrSrSU4SjjrSrS SjrSrSr U=r $) DiscretizedModeliajexperimental model to fit discretized distribution Count models based on discretized distributions can be used to model data that is under- or over-dispersed relative to Poisson or that has heavier tails. Parameters ---------- endog : array_like, 1-D Univariate data for fitting the distribution. exog : None Explanatory variables are not supported. The ``exog`` argument is only included for consistency in the signature across models. distr : DiscretizedCount instance (required) Instance of a DiscretizedCount distribution. See Also -------- DiscretizedCount Examples -------- >>> from scipy import stats >>> from statsmodels.distributions.discrete import ( DiscretizedCount, DiscretizedModel) >>> dd = DiscretizedCount(stats.gamma) >>> mod = DiscretizedModel(y, distr=dd) >>> res = mod.fit() >>> probs = res.predict(which="probs", k_max=5) c^>Ub [S5e[TU] XUS9 URR S5 [ U5UR - UlSUlUR Ul SUl UR Ul S[R"UR5-Ulg)Nexog is not supportedrrrg?) 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