Generate random numbers that adhere to Benford's Law

Newcomb works very much like Kernel.rand or SecureRandom.random_number. In order to fetch a random float between 0 (inclusive) and 1 (exclusive):

Newcomb.random_number # => 0.49552091973604767

To fetch a random positive integer, provide the upper (exclusive) limit:

Newcomb.random_number(100) # => 16

To fetch a random positive float, provide the upper (exclusive) limit as a float:

Newcomb.random_number(100.0) # => 21.895884449446473

To fetch a random integer within a range, provide a range with integer endpoints:

Newcomb.random_number(100..1000) # => 141

To fetch a random float within a range, provide a range with float (or mixed) endpoints:

Newcomb.random_number(100.0..1000.0) # => 203.90587157406662
Newcomb.random_number(100..1000.0) # => 424.9768102233391
Newcomb.random_number(100.0..1000) # => 628.7978615329862

Over a sufficiently large sample size, the distribution of Newcomb's random numbers will demonstrate Benford's Law.

While Benford's Law is named after American scientist Frank Benford, the principle was original discovered by Simon Newcomb over fifty years earlier. In addition to his numerous accomplishments in mathematics and astronomy, Newcomb had a fantastic beard.