In the past week I have discovered a couple of sites with open source maths related VBA code that I was previously unaware of, and which deserve to be better known:

AlgLib:

“ALGLIB is a cross-platform numerical analysis and data processing library. ALGLIB aims to be highly portable: it supports several programming languages (C++, C# and other languages); it may be compiled with a wide variety of compilers and was tested under a wide variety of platforms. And it is free – ALGLIB is distributed under a GPL license (version 2 or later).”

All of the code is provided in VBA as well as various flavours of C and Fortran, and appears to be well documented and indexed. This makes it ideal for creating prototypes in VBA, and if desired converting to compiled dlls at a later date.

Axel Vogt

Axel’s home page sets new standards for minimal web site design, here it is in full:

**axalom**

various files for numerical or financial Math, free for download

But don’t be put off, there are many worthwhile files here, as well as some excellent articles on programming topics.

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Thx 🙂 BTW i like your site. Cheers, Axel

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Pingback: Installing AlgLib with Excel VBA « Newton Excel Bach, not (just) an Excel Blog

Hi,

I’ve just started with vba programming. By googling I found your blog very helpful and interesting.

I’m trying to implement a kalman filter algorithm within vba excel.

Would anybody like you to help on that.

The model I want to use is :

X(t+1) = A + B*X(t) + u(t) u ~ N(0,Q); N where stands for Normal Distribution.

Y(t) = C + D*S(t) + e(t) e ~ N(0,v)

X(t) is the state (non variable and Y(t) the measurement

X(t) is p x 1 matrix

A is p x 1 matrix

B is p x p matrix

u(t) p-multi-Normal Distribution, ie a realisation of u(t) is p x 1 matrix. Therefore Q is p x p covariance matrix.

Y(t) is n x 1 matrix, n is the number of observations.

C is n x 1 matrix,

D is n x p matrix,

e(t) is n x 1 matrix, a Normal Distribution, a realisation of e(t) is a real number

Here are some link where the algorithm can be red

http://www.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html

http://www.jstatsoft.org/v39/i02/paper

thank

Zoul

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