the toolbox contains MATLAB routines for computing recurrence plots and related problems as Mfiles
parameters in [ ] are optional
download:
CRP.m,
CRQA.m,
tt.m;
sorry, software is only available for members of the nonlinear dynamics
group
also visit
mathtools.net
to survey other useful toolboxes
still under construction,
routine  description  

recurrence plot, cross recurrence plot 
CRP(X [,Y [,M,T,E,A]]) creates a cross recurrence plot/ recurrence plot results could be stored into the workspace allows to change the parameters interactively by using a GUI the sourcedata X and testdata Y can be one or a twocoloumn vectors (then, in the first column have to be the time); if the testdata Y is not specified, a simple recurrence plot is created parameters: dimension M, delay T and the size of neighbourhood E; the flag A allows to switch between various methods of finding neighbours or CRPs, resp.: A=1 > Euclidean norm A=2 > Euclidean norm between normalized vectors A=3 > fixed amount of neighbours A=4 > distance coded matrix (global CRP) parameters not needed to specify current limitation: for higher speed in output the whole matrix of the recurrence plot is in the work space  this limits the application of long data series 

complexity measures based on (cross) recurrence plots 
CRQA(X [,Y [,W,M,T,E,L,A]]) recurrence quantification analysis (WEBBER) vs. time (away from mean diagonal) of crossrecurrence with the first vector X and the second Y Lmin is the minimal length for a line if W=[] then the whole plot will calculated B is the maximum forwarded (backwarded) time J=time scale Y(1)=RR of diagonalline Y(2)=DET Y(3)=L Y(4)=ENTR 

length of vertical lines in recurrence plots 
TT mean trapping time and its distribution.
A=TT(X) computes the mean of the length of the vertical line structures in a recurrence plot, so called trapping time TT. [A B]=TT(X) computes the TT and the distribution of the length of the vertical line structures, stored in B. 

estimation main diagonal path of a RP 
[X, Y]=TRACK(RP) estimates the path of the main diagonal line in a RP RP is a N x NMatrix 

heaviside function 
Y=H(X) heaviside function y=H(x) with y=1 for x>=0 and y=0 for x<0 X can be an onecolumn or onerow vector 

transformation to pretended distibutions 
Y=TRAFO(X [,A]) transformation of data X to a pretended distribution Y, where A=0 normal distribution (defeault), A=1 uniform distribution, A=2 exponential distribution X is an onecolumn or twocolumn vector (then, in the first column have to be the time) 

mutual information 
S=MI(X1, X2, T, [,A]) mutual information between the data in the vectors X1 and X2 (A=0 > without plot) X1 and X2 are onecolumn vectors 

auto mutual information 
M=autoMI(X, T) computes the auto mutual information of the vector X X is an onecolumn or twocolumn vector (then, in the first column have to be the time) vectors 

estimation entropy 
E=ENTROPY(X) estimates the entropy E of a vector X 

phase space diameter 
[Y Z]=PSS(X,M,T) computes the maximal (Y) and the averaged (Z) phase space diameter of data X, with the embedding parameters dimension M and lag T 

walsh transformation 
Y=WALSH(X) computes the walsh transformation s=walsh(x) where Y and X are onecolumn vectors 

walsh power spectrum 
Y=WALSHPOWER(X) computes the walsh power spectrum s^{2}=(walsh(x))^{2} where Y and X are onecolumn vectors 

etc.  