Dear rcpp-dlevel members, Hopefully, you may be able to shed some light on a problem that I have regarding ‘speeding up’ the R function nlme::gls which I use for fitting models with temporal autocorrelation. In a vignette by Doug Bates and Dirk Eddelbuettel ( https://cran.r-project.org/web/packages/RcppEigen/vignettes/RcppEigen-Introduction.pdf), I found that they used RcppEigen to solve some least-squares problems, but I wasn’t sure if this could be extended to generalised least squares?
The current function nlme::gls takes hours to execute and I was hoping to use the Rcpp framework to ameliorate this problem, but I have little experience in Rcpp or C++ programming and wasn’t sure if this had already been tackled. After extensive searching on the web, I haven’t found any implementation. Does anyone have any ideas or advice please? My model syntax in R is currently as follows: myfit <- gls(y~stim1+stim2+t+I(t^2)+I(t^3)+signal+stim1_signal,data=mydata, correlation=corAR1(form=~t)) Some other information, each time series has approximately 20,000 observations. Thank you in advance for any help. Paul
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