Better random number generation for .NET

RandN provides an alternative random number generation API to .NET's Random, which has numerous problems, including low performance, poor statistical quality, and limiting and inflexible API design. RandN solves all of these issues by providing modern RNG algorithms encapsulated within a carefully designed API that was inspired by the Rust crate, Rand. Think of RandN like NodaTime, but for random numbers.

GitHub
nuget

Why not just use Random?

While Random can work for simple use cases, it has inherit design problems that have not been fixed. A common error is instantiating Random instances in a loop - they'll almost certainly produce identical sequences since they're seeded from the current time. It's also very slow compared to other RNGs. Even more subtle is the statisical bias in the algorithm used. Performance and statistical quality can be improved by using other algorithms, but the underlying problem remains - the API is a footgun.

The problems with Random are elaborated on in this blog post, and there's plenty of other literature pointing out statistical bias and design issues. Suffice it to say, while Random works okay for simple use cases (and if you know how to avoid the shooting yourself in the foot) there's a lot of room for improvement in all areas.

Quick Start

Install the NuGet package, then you're ready to go.

using RandN;
using RandN.Distributions;

StandardRng rng = StandardRng.Create();

Bernoulli weightedCoin = Bernoulli.FromRatio(8, 10); // 8/10 chance
bool probablyTrue = weightedCoin.Sample(rng);

UniformInt32 d6 = Uniform.NewInclusive(1, 6);
int roll = d6.Sample(rng);

// Drop in replacement of Random is possible through a shim
Random random = RandN.Compat.RandomShim.Create(rng);
random.Next(42);

View on .NET Fiddle

RNG Algorithms

The following random number generators are included with RandN:

Distributions

These distributions are included with RandN: