I've written a series of functions that evaluates an integral from -inf to a or b to +inf using equally spaced quadrature points along a normal distribution from -10 to +10 moving in increments of .01. These functions are working and give very good approximations, but I think they are computationally wasteful as I am evaluating the function at *many* points. 

Instead, I would prefer to use true Gaussan Quadrature and evaluate the integral at fewer points. I am working with the gauss.quad.prob function and have rewritten my functions to use the nodes and weights. But, I believe these are only for definite integrals and not for evaluating from -inf to a or b to +inf.

>From what I understand, I cannot use the tails (e.g., -4.85 or 4.85 below) to approximate inf. In fact, I've tried it and it doesn't work. Instead, I believe one needs special weights when the interest is to evaluate an integral from -inf to a etc.

I've read through the help and searched the archives and didn't see any relevant discussion on whether it is possible to use this function to get the weights for -inf to a. Only two hits come up in the archive and neither was relevant. I think the Laguerre quadrature in gauss.prob might be the right path, but I'm uncertain.

Is it possible to get the nodes and weights I need from R directly, or should I refer to a table that has derived these for the normal distribution from another source?  

thank you
Harold



> gauss.quad.prob(10, dist='normal') 
$nodes 
 [1] -4.8594628 -3.5818235 -2.4843258 -1.4659891 -0.4849357  0.4849357  1.4659891  2.4843258  3.5818235 
[10]  4.8594628

$weights 
 [1] 4.310653e-06 7.580709e-04 1.911158e-02 1.354837e-01 3.446423e-01 3.446423e-01 1.354837e-01 1.911158e-02 
 [9] 7.580709e-04 4.310653e-06

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