changes for ralg, oofun, handling NaNs

I have committed some changes related to

• ralg: change for matrix B rejuvenation criterium, now by default it performs check "cond(b) less than 1e5" each 250th iter or provided a stop criterion has triggered on. However, for large nVars evaluation of numpy.linalg.cond(b) takes a time (for nVars = 1000 I got ~5 seconds at my AMD 3800+ X2). So it would be good to get estimation of cond(b) instead of exact value (like MATLAB's condest for sparse matrices; however, b is dense).
  
• oofun: recursive 1st derivatives, appropriate example will be committed soon
  
• handling NaNs: if right-derivative (f(x+dx)-f(x))/dx is NaN then left-derivative (f(x)-f(x-dx))/dx will be used; it's valid for both oofun and classic style, however, it doesn't resolve all possible issues. Also, I intend to add double-side derivative possibility in future: (f(x+dx)-f(x-dx))/(2*dx) via an oofun field, for example c2 = oofun(..., useDoubleSideDerivative = True,...)
• some other (minor) ralg changes