Multi-sample P(X>=Y) calculation

Let `X_1`, `X_2`,... `X_n` be some arbitrary distributions (`distr` objects) with the mode close to zero. Let `Y_1`, `Y_2`,..., `Y_m` be normal distributions with known means and variances. This PR implements the calculation of `P(X' >= Y')`, where the random variable `X'` has the PDF `pdf[X'](t) ~ pdf[X_1](t)*pdf[X_2](t)*...pdf[X_n](t)` (i.e. the joint distribution of `X_i`s induced to the diagonal), and, similarlty, the PDF of `Y'` is proportional to `pdf[Y_1](t)*pdf[Y_2](t)*...pdf[Y_m](t)`.

It's implemented in `calcCumulativeProbXGreaterThanY()` and uses the approach similar to `calcProbXGreaterThanY()`. In particular, we use the fact that the induction of joint distribution of normal distributions to the diagonal would still be a normal distribution with the explicit formulas for the variance and mean, so we know the CDF `Y'`.

This routine could be used to test the significance of ratios when several replicate experiments are available. Let `X`s be the background distributions of ratios and `Y`s be the estimates of ratios in each replicate experiment. In this case `Y'` would be the distribution for the "true" ratio (that is the same in all biological replicates), and `X'` is the cumulative background distribution of ratios (how the "true" background noise ratio is distributed).