Are you familiar with the sos package? Consider the following:
library(sos)
op <- findFn('orthogonal polynomial') # 165 links in 35 pkgs
ops <- findFn('orthogonal polynomials')#158 links in 35 pkgs
op. <- op |ops # 194 links in 43 pkgs
save(op., file='orthopoly.rda')
summary(op.)
install.packages('orthopolynom')
gq <- findFn('gauss quadrature') # 160 links in 50 pkgs
gnq <- findFn("gaussian quadrature")#129 links in 42 pkgs
gq. <- gq|gnq # 219 links in 67 pkgs
summary(gq.)
The print method for "op." or "gq." opens a web browser with a
table of all the help pages matching the search term sorted to list
first the package with the most matches with links to all the help
pages. A vignette explains more including how to generate a spreadsheet
with a summary by package.
One more thing: As far as I know, the most general distribution
capabilities in R is the "distr" package and companions like "distrEx".
Unfortunately, I was not able to find anything for orthogonal
polynomials or quadrature built on those packages, though it should not
be too difficult to develop.
Hope this helps.
Spencer
On 10/10/2013 5:47 PM, Marino David wrote:
> Thanks so much for your response. BTW, do you know any Gauss
> quadrature R package can deal with the arbitary PDF?
>
> Thank you!
>
> David
>
>
> 2013/10/11 Spencer Graves <spencer.gra...@structuremonitoring.com
> <mailto:spencer.gra...@structuremonitoring.com>>
>
> p.s. Orthogonal polynomials can be defined for any probability
> distribution on the real line, discrete, continuous, or otherwise,
> as described in the Wikipedia article on "orthogonal polynomials".
>
>
> On 10/10/2013 5:02 PM, Marino David wrote:
>> Hi all,
>>
>> We know that Hermite polynomial is for
>> Gaussian, Laguerre polynomial for Exponential
>> distribution, Legendre polynomial for uniform
>> distribution, Jacobi polynomial for Beta distribution. Does anyone know
>> which kind of polynomial deals with the log-normal,
>
>
> * lognormal in X is normal for Z = log(X). Therefore, you'd
> use Hermite polynomials in Z.
>
>
>> StudentÂ’s t, Inverse
>> gamma and FisherÂ’s F distribution?
>
>
> * If X follows an F(d1, d2) distribution, then Z =
> d1*x/(x1*x+d2) follows a beta distribution. Use Jacobi
> polynomials on Z.
>
>
> * If X follows a student's t(df), then X^2 follows an F(1,
> df) distribution. Again, use Jacobi on the appropriate transform.
>
>
> * If X follows an inverse gamma, then 1/X follows a gamma
> distribution.
>
>
> Does this answer the question?
>
>
> Spencer
>> Thank you in advance!
>>
>> David
>>
>> [[alternative HTML version deleted]]
>>
>>
>>
>> ______________________________________________
>> R-help@r-project.org <mailto:R-help@r-project.org> mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting
>> guidehttp://www.R-project.org/posting-guide.html
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>
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