Categories

• Beta Distribution
  Fit, evaluate, and generate random samples from beta distribution
  
• Birnbaum-Saunders Distribution
  Fit, evaluate, and generate random samples from Birnbaum-Saunders distribution
  
• Burr Type XII Distribution
  Fit, evaluate, and generate random samples from Burr Type XII distribution
  
• Chi-Square Distribution
  Evaluate and generate random samples from chi-square distribution
  
• Exponential Distribution
  Fit, evaluate, and generate random samples from exponential distribution
  
• Extreme Value Distribution
  Fit, evaluate, and generate random samples from extreme value distribution
  
• F Distribution
  Fit, evaluate, and generate random samples from F distribution
  
• Gamma Distribution
  Fit, evaluate, and generate random samples from gamma distribution
  
• Generalized Extreme Value Distribution
  Fit, evaluate, and generate random samples from generalized extreme value distribution
  
• Generalized Pareto Distribution
  Fit, evaluate, and generate random samples from generalized Pareto distribution
  
• Half-Normal Distribution
  Fit, evaluate, and generate random samples from half-normal distribution
  
• Inverse Gaussian Distribution
  Fit, evaluate, and generate random samples from inverse Gaussian distribution
  
• Kernel Distribution
  Fit a smoothed distribution based on a kernel function and evaluate the distribution
  
• Logistic Distribution
  Fit, evaluate, and generate random samples from logistic distribution
  
• Loglogistic Distribution
  Fit, evaluate, and generate random samples from loglogistic distribution
  
• Lognormal Distribution
  Fit, evaluate, generate random samples from lognormal distribution
  
• Loguniform Distribution
  Evaluate and generate random samples from loguniform distribution
  
• Pearson Distribution
  Evaluate Pearson distribution probability functions and generate random samples
  
• Nakagami Distribution
  Fit, evaluate, and generate random samples from Nakagami distribution
  
• Noncentral Chi-Square Distribution
  Evaluate and generate random samples from noncentral chi-square distribution
  
• Noncentral F Distribution
  Evaluate and generate random samples from noncentral F distribution
  
• Noncentral t Distribution
  Evaluate and generate random samples from noncentral t distribution
  
• Normal Distribution
  Fit, evaluate, and generate random samples from normal (Gaussian) distribution
  
• Piecewise Linear Distribution
  Evaluate and generate random samples from piecewise linear distribution
  
• Rayleigh Distribution
  Fit, evaluate, and generate random samples from Rayleigh distribution
  
• Rician Distribution
  Fit, evaluate, and generate random samples from Rician distribution
  
• Stable Distribution
  Fit, evaluate, and generate random samples from stable distribution
  
• Student's t Distribution
  Evaluate and generate random samples from Student’s t distribution
  
• t Location-Scale Distribution
  Fit, evaluate, and generate random samples from t location-scale distribution
  
• Triangular Distribution
  Evaluate and generate random samples from triangular distribution
  
• Uniform Distribution (Continuous)
  Evaluate and generate random samples from continuous uniform distribution
  
• Weibull Distribution
  Fit, evaluate, and generate random samples from Weibull distribution