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...the same position in y # by locally compted mean of values in x # from all local x that also have continous 000 in their positions close to this x # in y vector #x # 0 1 2 3 4 5 6 7 8 9 #y # 0 0 0 1 0 0 0 1 1 1 # Value expected: #x_m # 1.5 1.5 1.5 3.0 5.0 5.0 5.0 7.0 8.0 9.0 #------------------------------------------------------- # author: me # replace_x_by_locallyMean_x_4_0s_in_y.f(x,y) #------------------------------------------------------- #initialisation Dff<-NULL index_Dff.start_0<-NULL index_Dff.stop_0&l...

In https://en.wikipedia.org/wiki/Pareto_distribution, it is clear what the parameters are for the pareto distribution: *xmin *the scale parameter and *a* the shape parameter. I am using rmutil to generate random deviates from a pareto distribution. It says in the documentation that the probabilty density of the pareto distribution The Pareto distribution has density f(y) = s (1 + y/(m

Hi everybody! I'm looking some way to do in R a polynomial fit, say like polyfit function of Octave/MATLAB. For who don't know, c = polyfit(x,y,m) finds the coefficients of a polynomial p(x) of degree m that fits the data, p(x[i]) to y[i], in a least squares sense. The result c is a vector of length m+1 containing the polynomial coefficients in descending powers: p(x) = c[1]*x^n +