Package check result: ERROR Check: examples, Result: ERROR Running examples in ‘DebiasInfer-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: DebiasProg > ### Title: The proposed debiasing (primal) program. > ### Aliases: DebiasProg > ### Keywords: debiasing primal program > > ### ** Examples > > ## No test: > require(MASS) Loading required package: MASS > require(glmnet) Loading required package: glmnet Loading required package: Matrix Loaded glmnet 4.1-10 > d = 1000 > n = 900 > > Sigma = array(0, dim = c(d,d)) + diag(d) > rho = 0.1 > for(i in 1:(d-1)){ + for(j in (i+1):d){ + if ((j < i+6) | (j > i+d-6)){ + Sigma[i,j] = rho + Sigma[j,i] = rho + } + } + } > sig = 1 > > ## Current query point > x_cur = rep(0, d) > x_cur[c(1, 2, 3, 7, 8)] = c(1, 1/2, 1/4, 1/2, 1/8) > x_cur = array(x_cur, dim = c(1,d)) > > ## True regression coefficient > s_beta = 5 > beta_0 = rep(0, d) > beta_0[1:s_beta] = sqrt(5) > > ## Generate the design matrix and outcomes > X_sim = mvrnorm(n, mu = rep(0, d), Sigma) > eps_err_sim = sig * rnorm(n) > Y_sim = drop(X_sim %*% beta_0) + eps_err_sim > > obs_prob = 1 / (1 + exp(-1 + X_sim[, 7] - X_sim[, 8])) > R_sim = rep(1, n) > R_sim[runif(n) >= obs_prob] = 0 > > ## Estimate the propensity scores via the Lasso-type generalized linear model > zeta = 5*sqrt(log(d)/n)/n > lr1 = glmnet(X_sim, R_sim, family = "binomial", alpha = 1, lambda = zeta, + standardize = TRUE, thresh=1e-6) > prop_score = drop(predict(lr1, newx = X_sim, type = "response")) > > ## Estimate the debiasing weights > w_obs = DebiasProg(X_sim, x_cur, Pi=diag(prop_score), gamma_n = 0.1) Error in (function (shape, nonneg = FALSE, nonpos = FALSE, complex = FALSE, : unused arguments (rows = 900, cols = 1) Calls: DebiasProg -> Variable -> do.call Execution halted