nanconv by Benjamin Kraus

Convolution in 1D or 2D ignoring NaNs and (optionally) correcting for edge effects. (conv, conv2, nan)

nanconv(a, k, varargin)

nanconvn by Benjamin Kraus

Convolution in ND ignoring NaNs and (optionally) correcting for edge effects. (conv, conv2, convn)

nanconvn(a, k, varargin)

Modern Pricing Method using Transforms by Kienitz Wetterau FinModelling

COS, CONV, Lewis Option Pricing Methods including Bermudan and American Options. (heston, bermudan options, cos)

CF(model,u,T,r,d,varargin)

CallPricingFFT(model,n,S,K,T,r,d,varargin)

CallPricingFFTi(model,n,S,K,T,r,d,imethod,varargin)

conv2fft_reuse by Felipe G. Nievinski

Reuse intermediary results in multiple related convolutions with the same data but different kernels (convolution, fft, ifft)

conv2fft (h, X, shape, Xfft)

dirup (dir)

filter2fft (h, X, shape, Xfft)

bernpdf by Peter Cotton

Distribution of the sum of independent non-identical Bernoulli random variables using convolution (statistics, probability, simulation)

bernpdf(p)

Separate Kernel in 1D kernels by Dirk-Jan Kroon

Decompose an arbitrary N dimensional filtering kernel into 1D kernels, for faster filtering (2d, gaussian, 3d)

[K1 KN ERR]=SeparateKernel(H)

FFT-based convolution by Bruno Luong

Discrete convolution using FFT method (conv, fft, convn)

convnfft(A, B, shape, dims, options)

convnfft_install

ndnanfilter.m by Carlos Adrian Vargas Aguilera

Filter/smoothing of multidimensional data with an specified window function, ignoring NaNs. (filter design, filter analysis, smooth)

ndnanfilter(X,HWIN,F,DIM,WINOPT,PADOPT,WNAN)

Movingslope by John D'Errico

Sliding window regression to compute slope estimates along a curve (slope, moving, sliding)

movingslope(vec,supportlength,modelorder,dt)

FFW Fastest Filtering in the West by Luigi Rosa

Fast 2D convolution - filtering (conv2, fft, conv)

[out]=detbestlength2(FFTrv,FFTiv,IFFTiv,size1,size2,isreal1...

[out]=fftolamopt2(a,b,opt,shape)

provatempo2.m

Fast Convolution by Stephen McGovern

Convolves two extremely large vectors in reasonable time. (conv, convolution, dft)

[y]=fconv(x, h)

A Faster Convolutional 1D Operator by Luigi Rosa

A faster conv for very large arrays. (conv, convolution, polynomial multiplica...)

prova.m