Add SkewGeneralizedNormal distribution (generalized normal v2)#2025
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Adds the skew generalized normal distribution (the "version 2" / skew variant of the generalized normal), parameterized by location `loc`, scale `scale` (> 0) and shape `peak` (any nonzero real). It is the change-of-variables of a standard normal under y = (-1/peak) * log(1 - peak * (x - loc) / scale), giving a parameter-dependent half-line support whose mode sits at or near the support boundary (the gap shrinks like exp(-peak**2) as |peak| grows). Includes log_prob / cdf / log_cdf / survival / quantile / sampling, entropy, mean / variance / mode, and a support-respecting default event-space bijector built as a single Chain (valid for either sign of `peak`). Full unit tests validate against the closed-form v2 formulas and the peak -> 0 Normal limit, since no v2 reference exists in scipy (scipy.stats.gennorm is the symmetric v1). Registered in distributions `__init__`, BUILD, and hypothesis_testlib.
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Adds the skew generalized normal distribution (the "version 2" / skew variant of the generalized normal), parameterized by location
loc, scalescale(> 0) and shapepeak(any nonzero real). It is the change-of-variables of a standard normal undery = (-1/peak) * log(1 - peak * (x - loc) / scale), giving a parameter-dependent half-line support whose mode sits at or near the support boundary (the gap shrinks like exp(-peak**2) as |peak| grows).
Includes log_prob / cdf / log_cdf / survival / quantile / sampling, entropy, mean / variance / mode, and a support-respecting default event-space bijector built as a single Chain (valid for either sign of
peak). Full unit tests validate against the closed-form v2 formulas and the peak -> 0 Normal limit, since no v2 reference exists in scipy (scipy.stats.gennorm is the symmetric v1). Registered in distributions__init__, BUILD, and hypothesis_testlib.