The "stdlib-js/stats-base-dists-negative-binomial-pmf" project provides a JavaScript implementation of the probability mass function (PMF) for the negative binomial distribution, which calculates the probability of observing a given number of failures before a specified number of successes in a series of independent trials. It is designed for developers working on statistical computations or simulations in JavaScript who need to model count data with overdispersion or scenarios where the number of trials is not fixed.
Total donated
Undistributed
Share with your subscribers:
Recipients
How the donated funds are distributed
Support the dependencies of stdlib-js/stats-base-dists-negative-binomial-pmf
Euler's number.
Arbitrary constant `g` to be used in Lanczos approximation functions.
Natural logarithm of the maximum double-precision floating-point number.
Natural logarithm of the smallest normalized double-precision floating-point number.
Test if a double-precision floating-point numeric value is NaN.
Test if a finite double-precision floating-point number is a nonnegative integer.
Compute the absolute value of a double-precision floating-point number.
Natural exponential function.
Compute exp(x) - 1.
Calculate a scaled Lanczos sum for the approximation of the gamma function.
Evaluate the natural logarithm of a double-precision floating-point number.
Evaluate the natural logarithm of 1+x.
Return the maximum value.
Return the minimum value.
Exponential function.
Compute the principal square root of a double-precision floating-point number.
Constant function.
Define a non-enumerable read-only property.
Insert array element values and the result of a callback function into a format string and print the result.
Difference between one and the smallest value greater than one that can be represented as a double-precision floating-point number.
Double-precision floating-point negative infinity.
Double-precision floating-point positive infinity.
Create an array containing pseudorandom numbers drawn from a discrete uniform distribution.
Create an array containing pseudorandom numbers drawn from a continuous uniform distribution.
tap-producing test harness for node and browsers
Yet another JS code coverage tool that computes statement, line, function and branch coverage with module loader hooks to transparently add coverage when running tests. Supports all JS coverage use ca
Minimal TAP output formatter.
Benchmark harness.
Support the repos that depend on this repository
Top contributors
stdlib-bot
68 contributions
Recent events
Kivach works on the Obyte network, and therefore you can track all donations.
No events yet