for package:statistics

Simple for loop. Counts from start to end-1.

The discrete uniform distribution. There are two parametrizations of this distribution. First is the probability distribution on an inclusive interval {1, ..., n}. This is parametrized with n only, where p_1, ..., p_n = 1/n. (discreteUniform). The second parametrization is the uniform distribution on {a, ..., b} with probabilities p_a, ..., p_b = 1/(a-b+1). This is parametrized with a and b. (discreteUniformAB)

The discrete uniform distribution.

Construct discrete uniform distribution on support {1, ..., n}. Range n must be >0.

Construct discrete uniform distribution on support {a, ..., b}.

Transformations over distributions

Linear transformation applied to distribution.

LinearTransform μ σ _
x' = μ + σ·x

Variate distributed uniformly in the interval.

Uniform distribution from A to B

Low boundary of distribution

Upper boundary of distribution

Create uniform distribution.

Create uniform distribution.

Simple reverse-for loop. Counts from start-1 to end (which must be less than start).

O(n) Arithmetic mean. This uses Welford's algorithm to provide numerical stability, using a single pass over the sample data. Compared to mean, this loses a surprising amount of precision unless the inputs are very large.

Fourier-related transformations of mathematical functions. These functions are written for simplicity and correctness, not speed. If you need a fast FFT implementation for your application, you should strongly consider using a library of FFTW bindings instead.