Normal distribution

Density, cumulative probability, quantiles, intervals and random samples of a normal distribution.

NormalDistribution(mean: m, std: s) is the equivalent of scipy.stats.norm(loc=m, scale=s).

Method	Meaning
`pdf`, `logPdf`	density at x
`cdf`	P(X ≤ x)
`sf`	P(X > x), precise in the upper tail
`ppf`	quantile: the x with cdf(x) = p
`isf`	inverse of `sf`
`interval`	central interval with the given probability
`sample`	random values, with an optional `Random` for a fixed seed

Normal distributionDensity, probabilities, quantiles and samples of a normal distribution, like scipy.stats.norm.

import 'dart:math' as math;

import 'package:scidart/numdart.dart';
import 'package:scidart/scidart.dart';

void main() {
  // adult height in cm
  var height = NormalDistribution(mean: 170, std: 8);

  print('density at 170: ${height.pdf(170)}');
  print('P(height < 180): ${height.cdf(180)}');
  print('P(height > 190): ${height.sf(190)}');
  print('95th percentile: ${height.ppf(0.95)}');
  print('central 95%: ${height.interval(0.95).toList()}');

  var s = height.sample(10000, random: math.Random(42));
  print(
      'sample mean: ${mean(s).toStringAsFixed(2)}, std: ${standardDeviation(s).toStringAsFixed(2)}');
}

Output

density at 170: 0.04986778505017909
P(height < 180): 0.8943502263331448
P(height > 190): 0.0062096653257761375
95th percentile: 183.1588290156118
central 95%: [154.32028812367957, 185.67971187632043]
sample mean: 169.93, std: 7.92

ppf stays accurate in the extreme tails: ppf(1e-300) and even ppf(5e-324) match SciPy.

Loading the interactive demo…