pwmLC(x, threshold=NULL, nmom=5, sort=TRUE)i=1 of the betas vector.list is returned.pwm() returns if there is no censoring. Note that convention is the have a $\beta_0$, but this is placed in the first index i=1 of the betas vector.NA if there is no censoring. Note that convention is the have a $\beta_0$, but this is placed in the first index i=1 of the betas vector.numbelowthreshold/samplesizesapply(x,function(v) { if(v >= T) return(T); return(v)}) to reset the data vector x. By operating on the data in this fashion one can toy with various levels of the threshold for experimental purposes; this seemed a more natural way for general implementation. The code sets $n$=length(x) and $m$=n - length(x[x == T]), which also seems natural. The $\beta^A_r$ are computed by dispatching to pwm.lmoms, pwm2lmom, pwm, pwmRC