lgammaAsymp

Compute an n-th order asymptotic approximation to log Gamma function,
 using Bernoulli numbers <code>Bern(k)</code> for <code>k</code> in
 \(1, \ldots, 2n\).

distribution

math

Computations for approximations and alternatives for the 'DPQ'
(Density (pdf), Probability (cdf) and Quantile) functions for probability
distributions in R.
Primary focus is on (central and non-central) beta, gamma and related
distributions such as the chi-squared, F, and t.
--
For several distribution functions, provide functions implementing formulas from
Johnson, Kotz, and Kemp (1992) <doi:10.1002/bimj.4710360207> and
Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous
distributions respectively.
This is for the use of researchers in these numerical approximation
implementations, notably for my own use in order to improve standard
R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.

Martin Maechler

Density, Probability, Quantile ('DPQ') Computations

Morten Welinder

Wolfgang Viechtbauer

Ross Ihaka

Marius Hofert

R-core 

R Foundation 

lgammaAsymp function

<dl> <dt>x</dt>
<dd>numeric vector</dd> <dt>n</dt>
<dd>integer specifying the approximation order.</dd></dl>

Arguments

Author

Asymptotic Log Gamma Function — lgammaAsymp

<dl>

 <dt>x</dt>
<dd>numeric vector</dd>

 <dt>n</dt>
<dd>integer specifying the approximation order.</dd>

</dl>

Asymptotic Log Gamma Function

lgammaAsymp: Asymptotic Log Gamma Function

Description

Usage

Value

Arguments

Author

See Also

Examples