similar to: Parameter changes and segfault when calling C code through .Call

Hi, I get an error using RcppGSL: fatal error: gsl/gsl_vector.h:No such file or directory. What is the best way to install these files as they seem to be missing? Thanks, Oyvind -- View this message in context: http://r.789695.n4.nabble.com/Error-using-RcppGSL-tp3613535p3613535.html Sent from the R devel mailing list archive at Nabble.com.

I'd like to use some matrix algebra in my c code that is called from R. I need matrix multiplication, transposition, and Cholesky decomposition. I haven't come across any easy way to do this, but from browsing the web and R-devel a few options come to mind: 1. use F77_CALL() to call matrix multiplication functions from blas. This should be ok as long as I remember that c and FORTRAN store

Suppose I know the value of cumulative bivariate standard normal distribution. How can I solve correlation between variables? Pekka --------------------------------- [[alternative HTML version deleted]]

Hello, I'm quite new with R and so I would like to know if there is a command to calculate an integral. In particular I simulated a bivariate normal distribution using these simple lines: rbivnorm <- function(n, # sample size mux, # expected value of x muy, # expected value of Y sigmax, # standard deviation of

I have an upcoming JASA paper with an iid sampling algorithm for Bayesian Generalized Linear models (e.g., Logit, Poisson Regression, and Conditional Logit models with multivariate normal priors). At this point, I have implemented the algorithms in C and hope to make the functions and corresponding source code available through an R package. I have successfully created the code necessary to

Hi all, I am trying to use mle() to find a self-defined function. Here is my function: test <- function(a=0.1, b=0.1, c=0.001, e=0.2){ # omega is the known covariance matrix, Y is the response vector, X is the explanatory matrix odet = unlist(determinant(omega))[1] # do cholesky decomposition C = chol(omega) # transform data U = t(C)%*%Y WW=t(C)%*%X beta = lm(U~W)$coef Z=Y-X%*%beta

Hello I'm a population ecologist and use R for all my stats and modelling. Recently I have been using R to numerically solve integral projection models. This involves constructing several large matrices. The current code by Easterling (Size-specific sensitivity: Applying a new structured population model. Ecology, 2000, 81, 694-708) uses nested loops to construct the matrices. To speed up the