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: DebiasProgCV
  > ### Title: The proposed debiasing (primal) program with cross-validation.
  > ### Aliases: DebiasProgCV
  > ### Keywords: CV debiasing program with
  > 
  > ### ** 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 with the tuning parameter selected by cross-validations.
  >   deb_res = DebiasProgCV(X_sim, x_cur, prop_score, gamma_lst = c(0.1, 0.5, 1),
  +                          cv_fold = 5, cv_rule = '1se')
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.5 !
  
  Polishing not needed - no active set detected at optimal point
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.5 !
  
  Polishing not needed - no active set detected at optimal point
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.5 !
  
  Polishing not needed - no active set detected at optimal point
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.5 !
  
  Polishing not needed - no active set detected at optimal point
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.1 !
  
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 0.5 !
  
  Polishing not needed - no active set detected at optimal point
  Warning in value[[3L]](cond) :
    The 'CVXR' fails to solve this program! Returning 'NA'...
  The primal debiasing program for this fold of the data is not feasible when gamma/n= 1 !
  
  Warning in min(mean_dual_loss, na.rm = TRUE) :
    no non-missing arguments to min; returning Inf
  Error in `validate_shape()`:
  ! Shape dimensions must be positive.
  Backtrace:
      ▆
   1. └─DebiasInfer::DebiasProgCV(...)
   2.   └─DebiasInfer::DebiasProg(...)
   3.     └─CVXR (local) `Ops.CVXR::Expression`(...)
   4.       └─CVXR::Inequality(e1, e2)
   5.         └─CVXR::Variable(shape)
   6.           └─CVXR:::validate_shape(shape)
   7.             └─cli::cli_abort("Shape dimensions must be positive.")
   8.               └─rlang::abort(...)
  Execution halted