# @stdlib/stats-base-dists-t-logcdf 0.1.0 • Public • Published

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# Logarithm of Cumulative Distribution Function

Evaluate the natural logarithm of the cumulative distribution function (CDF) for a Student's t distribution.

The cumulative distribution function (CDF) for a t distribution random variable is

where v > 0 is the degrees of freedom. In the definition, Beta( x; a, b ) denotes the lower incomplete beta function and Beta( a, b ) the beta function.

## Installation

npm install @stdlib/stats-base-dists-t-logcdf

## Usage

var logcdf = require( '@stdlib/stats-base-dists-t-logcdf' );

#### logcdf( x, v )

Evaluates the natural logarithm of the cumulative distribution function (CDF) for a Student's t distribution with degrees of freedom v.

var y = logcdf( 2.0, 0.1 );
// returns ~-0.493

y = logcdf( 1.0, 2.0 );
// returns ~-0.237

y = logcdf( -1.0, 4.0 );
// returns ~-1.677

If provided NaN as any argument, the function returns NaN.

var y = logcdf( NaN, 1.0 );
// returns NaN

y = logcdf( 0.0, NaN );
// returns NaN

If provided v <= 0, the function returns NaN.

var y = logcdf( 2.0, -1.0 );
// returns NaN

y = logcdf( 2.0, 0.0 );
// returns NaN

#### logcdf.factory( v )

Returns a function for evaluating the natural logarithm of the CDF of a Student's t distribution with degrees of freedom v.

var mylogcdf = logcdf.factory( 0.5 );
var y = mylogcdf( 3.0 );
// returns ~-0.203

y = mylogcdf( 1.0 );
// returns ~-0.358

## Notes

• In virtually all cases, using the logpdf or logcdf functions is preferable to manually computing the logarithm of the pdf or cdf, respectively, since the latter is prone to overflow and underflow.

## Examples

var randu = require( '@stdlib/random-base-randu' );
var logcdf = require( '@stdlib/stats-base-dists-t-logcdf' );

var v;
var x;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
x = (randu() * 6.0) - 3.0;
v = randu() * 10.0;
y = logcdf( x, v );
console.log( 'x: %d, v: %d, ln(F(x;v)): %d', x.toFixed( 4 ), v.toFixed( 4 ), y.toFixed( 4 ) );
}

## Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

#### Community ## Package Sidebar

### Install

npm i @stdlib/stats-base-dists-t-logcdf

### Repository

github.com/stdlib-js/stats-base-dists-t-logcdf

stdlib.io

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0.1.0

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