import mir.test: shouldApprox;

0.hypergeometricPMF(7, 4, 3).shouldApprox == 0.02857143;
1.hypergeometricPMF(7, 4, 3).shouldApprox == 0.3428571;
2.hypergeometricPMF(7, 4, 3).shouldApprox == 0.5142857;
3.hypergeometricPMF(7, 4, 3).shouldApprox == 0.1142857;

// can also provide a template argument to change output type
static assert(is(typeof(hypergeometricPMF!float(3, 7, 4, 3)) == float));

import mir.test: shouldApprox;
import mir.math.common: exp;

// Can approximate hypergeometric with binomial distribution
20.hypergeometricPMF!"approxBinomial"(750_000, 250_000, 50).shouldApprox == exp(hypergeometricLPMF(20, 750_000, 250_000, 50));
// Can approximate hypergeometric with poisson distribution
20.hypergeometricPMF!"approxPoisson"(100_000, 100, 5_000).shouldApprox == exp(hypergeometricLPMF(20, 100_000, 100, 5_000));
// Can approximate hypergeometric with normal distribution
3_500.hypergeometricPMF!"approxNormal"(10_000, 7_500, 5_000).shouldApprox == exp(hypergeometricLPMF(3_500, 10_000, 7_500, 5_000));
// Can approximate hypergeometric with normal distribution (with continuity correction)
3_500.hypergeometricPMF!"approxNormalContinuityCorrection"(10_000, 7_500, 5_000).shouldApprox == exp(hypergeometricLPMF(3_500, 10_000, 7_500, 5_000));