Lasso solution with Rcpp: A self study - r

Question

I'm very new to Rcpp. I 'm trying to write a coordinate descent algorithm for lasso in Rcpp as a self study. The code return an error:
Mat::init(): requested size is not compatible with column vector layout
I also have some problems while writing the code.
no matching function for call to 'sign'
no matching function for call to 'ifelse'
pow(X.col(j),2) : no viable conversion
I write
(S1>0)-(S1<0) for (1) to compute the sign of S1,
a if(){}else{} statement for (2) and
X.col(j)%X.col(j) for (3).
Any suggestion, please?
Here is the code.
// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
using namespace Rcpp;
using namespace arma;
// [[Rcpp::export]]
mat betamat(NumericMatrix Xr, NumericVector yr, NumericVector lambdar, double tol=0.0000001) {
int N = Xr.nrow(), p = Xr.ncol(), l = lambdar.size();
mat X(Xr.begin(), N, p, false);
colvec y(yr.begin(), yr.size(), false);
vec lambda(lambdar.begin(), lambdar.size(),false);
colvec ols = solve(X,y);
mat betas = zeros<mat>(p,l);
//
bool converged = false;
for (int i = 0; i < l; ++i) {
colvec b = zeros<vec>(p);
colvec r = y-X*b;
while(converged == false){
colvec beta_old = betas;
for(int j = 0; j < p; ++j){
r = r + X.col(j)*b(j);
double xr = dot(X.col(j),r);
double S1 = xr/N;
double xx = sum(X.col(j)%X.col(j))/N;
b(j) =((S1>0)-(S1<0))*(abs(S1)-lambda(i))/xx;
if(b(j)>0){
b(j)=b(j);
}else{
b(j)=0;
}
r = r - X.col(j)*b(j);
}
converged = (sum(abs(betas - beta_old)) < tol);
}
betas.col(i) = b;
}
return betas;
}
In R, I'm calling this with
library(Rcpp)
sourceCpp("filename.cpp")
set.seed(1)
X <- matrix(rnorm(100*3),100)
y <- rnorm(100)
coefficients <- betamat(X,y,seq(0,1,0.0005))