code: def examples_transf():
      ##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal')
      ##print(lognormal.cdf(1))
      ##print(stats.lognorm.cdf(1,1))
      ##print(lognormal.stats())
      ##print(stats.lognorm.stats(1))
      ##print(lognormal.rvs(size=10))
  
      print("Results for lognormal")
      lognormalg = ExpTransf_gen(
          stats.norm, a=0, name="Log transformed normal general"
      )
      print(lognormalg.cdf(1))
      print(stats.lognorm.cdf(1, 1))
      print(lognormalg.stats())
      print(stats.lognorm.stats(1))
      print(lognormalg.rvs(size=5))
  
      ##print('Results for loggamma')
      ##loggammag = ExpTransf_gen(stats.gamma)
      ##print(loggammag._cdf(1,10))
      ##print(stats.loggamma.cdf(1,10))
  
      print("Results for expgamma")
      loggammaexpg = LogTransf_gen(stats.gamma)
      print(loggammaexpg._cdf(1, 10))
      print(stats.loggamma.cdf(1, 10))
      print(loggammaexpg._cdf(2, 15))
      print(stats.loggamma.cdf(2, 15))
  
      # this requires change in scipy.stats.distribution
      # print(loggammaexpg.cdf(1,10))
  
      print("Results for loglaplace")
      loglaplaceg = LogTransf_gen(stats.laplace)
      print(loglaplaceg._cdf(2))
      print(stats.loglaplace.cdf(2, 1))
      loglaplaceexpg = ExpTransf_gen(stats.laplace)
      print(loglaplaceexpg._cdf(2))
      stats.loglaplace.cdf(3, 3)
      # 0.98148148148148151
      loglaplaceexpg._cdf(3, 0, 1.0 / 3)
  
file_path: statsmodels/statsmodels/sandbox/distributions/examples/ex_extras.py
imports: import numpy
  from scipy import stats
  from statsmodels.sandbox.distributions.extras import SkewNorm_gen, skewnorm, ACSkewT_gen, NormExpan_gen, pdf_moments, ExpTransf_gen, LogTransf_gen
  from statsmodels.stats.moment_helpers import mc2mvsk, mnc2mc, mvsk2mnc
label_desc: Use image node_id 4 for calling a global function with example usage: examples_transf() without return types