Some VBA maths resources

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 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:

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.

This entry was posted in Arrays, Excel, Maths, VBA and tagged , , . Bookmark the permalink.

4 Responses to Some VBA maths resources

  1. Pingback: Numric Libraries in VBA « Methods In Excel

  2. Axel Vogt says:

    Thx🙂 BTW i like your site. Cheers, Axel


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

  4. Zoul says:

    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



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