armadillo

I am new to C++ programming (using Rcpp for seamless integration into R ), and I would appreciate some advice on how to speed up some calculations. Consider the following example: testmat <- matrix(1:9, nrow=3) testvec <- 1:3 testmat*testvec # [,1] [,2] [,3] #[1,] 1 4 7 #[2,] 4 10 16 #[3,] 9 18 27 Here, R recycled testvec so that, loosely speaking, testvec "became" a matrix of the same dimensions as testmat for the purpose of this multiplication. Then the Hadamard product is returned. I wish to implement this behavior using Rcpp , that is I want that each element of the i -th row in the matrix

I am a noob in Linux environment. I am using ubuntu 13.10 and I want to install Armadillo . The readme file said that I should install three other libraries first, LAPACK, BLAS and ATLAS. I downloaded lapack-3.5.0, but I don't know how to install it(I only know apt-get install for installation). I found the installation steps inside the readme file as: LAPACK can be installed with make. Configuration have to be set in the make.inc file. A make.inc.example for a Linux machine running GNU compilers is given in the main directory. Some specific make.inc are also available in the INSTALL directory

I'm currently trying to parallelize an existing hierarchical MCMC sampling scheme. The majority of my (by now sequential) source code is written in RcppArmadillo, so I'd like to stick with this framework for parallelization, too. Before starting with parallelizing my code I have read a couple of blog posts on Rcpp/Openmp. In the majority of these blog posts (e.g. Drew Schmidt, wrathematics ) the authors warn about the issue of thread safety, R/Rcpp data structures and Openmp. The bottom line of all posts I have read so far is, R and Rcpp are not thread safe, don't call them from within an omp

Following on this question , I am trying to understand how to efficiently update a subset of a Rccp::NumericMatrix data type. I have the following scenario: Rcpp::NumericMatrix m of 5 x 5 that needs few rows and columns updated. It will be passed by reference to a function ( void return type) that will convert it to an arma::mat , and update the respective submat() . At this point I don't understand how to " apply " the changes that occurred inside the function to the m matrix that was passed to the function. The code looks like this: #include <iostream> #include <RcppArmadillo.h> // [[Rcpp:

Why does fastLm() return results when I run regressions with one observation? In the following, why aren't the lm() and fastLm() results equal? library(Rcpp) library(RcppArmadillo) library(data.table) set.seed(1) DT <- data.table(y = rnorm(5), x1 = rnorm(5), x2 = rnorm(5), my.key = 1:5) # y x1 x2 my.key # 1: -0.6264538 -0.8204684 1.5117812 1 # 2: 0.1836433 0.4874291 0.3898432 2 # 3: -0.8356286 0.7383247 -0.6212406 3 # 4: 1.5952808 0.5757814 -2.2146999 4 # 5: 0.3295078 -0.3053884 1.1249309 5 lm(y ~ 1 + x1 + x2, data = DT[my.key == 1]) # Coefficients: # (Intercept) x1 x2 # -0.6265 NA NA fastLm(X