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...a probability density function for each variable function, and then use latin hypercube sampling on this pdf. So far, I have created a data frame consisting of the "y" output of density(functionX) for each of the different functions I wish to sample. [examples of functions include T1(t), WL1(T1,t), BE1(WL1,T1,t)] The dataframe consists of 512 rows/vectors for each function. I tried running res <- clhs(df, size = 500, iter = 2000, progress = FALSE, simple = TRUE) and it returned a single series of 500 samples, rather than a series of 500 samples per function. I ultimately need...

...he semi-variance is of course 6 orders of magnitude higher but the values are the same. So from my point of view it should be the same fit. Maybe there is a reason for this I do not get. Hopefully someone could reply to this question. Thanks, Sascha Bellaire! Parts of code, just multiplying wl1$PSI with 1000 changed it. #Calculate the extent of the current "sampling" design locations <- cbind(wl1$X,wl1$Y) extent <- max(dist(locations)) max.dist <- extent/2 data1 <- cbind(wl1$X,wl1$Y,wl1$PSI*1000) geodata1 <- as.geodata(data1, coords.col = 1:2, data.col = 3...

...to perfectly replicate Roodman's "naive model" (on page 17) regressing Log(Employment) on its own first and second lags as well as current and first lags of log(wages) and current/first/second lags of capital and output. Roodman uses the Stata command "regress n nL1 nL2 w wL1 k kL1 kL2 ys ysL1 ysL2 yr*" (n=employment, w=wages, k=capital, ys=output, yr*=year dummy variables, and nL1=first Lag of employment). I am unable to replicate other results. Specifically, I cannot even replicate the Least Squares Dummy Variable model with effects for both time and firm...