stats

A Go package for working with probability distributions, statistical functions, and numerical methods.

Features

• Probability Mass Functions (PMF) for Binomial and Poisson distributions
• Probability Density Functions (PDF) for Normal, Student's t, and Exponential distributions
• Numerical integration and differentiation utilities
• Maximum Likelihood Estimation (MLE) for Normal and Poisson distributions
• Utility functions for combinations, factorials, normalization, and more

Main Functions

Probability Distributions

• NewBinomialPMF(numberOfTrials int, probSuccess float64) *PMF
  Create a binomial probability mass function.
• NewPoissonPMF(lambda float64, numEvents int) *PMF
  Create a Poisson probability mass function.
• NewNormalPDF(mean, stdDev, rangeMin, rangeMax float64) *PDF
  Create a normal probability density function.
• NewStudentTDistributionPDF(degreesOfFreedom, rangeMin, rangeMax float64) *PDF
  Create a Student's t-distribution PDF.
• NewExponentialPDF(lambda, rangeMin, rangeMax float64) *PDF
  Create an exponential probability density function.

Utility Functions

• Combination(totalItems, takenItems int) int
  Compute the number of combinations (n choose k).
• Factorial(n int) int
  Compute the factorial of n.
• Normalize(x, mean, stdDev float64) float64
  Compute the z-score for a value.
• Integrate(from, to, step float64, fx func(float64) float64) float64
  Numerically integrate a function over a range.
• Derivate(x, step float64, fx func(float64) float64) func(float64) float64
  Return a function that computes the numerical derivative.
• IntegrateUntilValue(from, toMaxValue, targetValue, step float64, fx func(float64) float64) float64
  Integrate until a target value is reached.

Maximum Likelihood Estimation (MLE)

• GetMaximumLikelihoodEstimation(data []float64) float64
  Compute the MLE (mean) for a normal distribution.
• GetMaximumLikelihoodEstimationPoisson(data []float64) float64
  Compute the MLE (mean) for the rate parameter (lambda) of a Poisson distribution.

Example Usage

// Binomial PMF
pmf := NewBinomialPMF(10, 0.5)

// Normal PDF
pdf := NewNormalPDF(0, 1, -3, 3)

// MLE for normal
mean := GetMaximumLikelihoodEstimation([]float64{1, 2, 3, 4, 5})

// MLE for Poisson
lambda := GetMaximumLikelihoodEstimationPoisson([]float64{2, 3, 4, 2, 3})

License

MIT