How to use gurobi for solving a particular problem

I have the following code in R

> library(robustbase)
> 
> library(mgcv)
Loading required package: nlme
This is mgcv 1.8-40. For overview type 'help("mgcv-package")'.
> library(MASS)
> 
> library(quadprog)
> 
> # We generate 100 values from N(0,1)
> set.seed(123)
> u <- rnorm(100)
> u[60] <- 15
> 
> # We now generate y(t)=μ+φ1y(t-1)+φ2y(t-2)+u(t)
> # Let μ=0.0, φ1=0.25, φ2=0.5
> A <- matrix(NA, nrow = 98, ncol = 3)
> Y <- numeric(100)
> y <- numeric(100)
> y[1] <- u[1]
> y[2] <- 0.25*y[1] + u[2]
> Y[1:2] <- c(y[1], y[2])
> 
> for (i in 3:100) {
+   y[i] <- 0.0 + 0.25*y[i-1] + 0.5*y[i-2] + u[i]
+   A[i-2,] <- c(y[i], y[i-1], y[i-2])
+   Y[i] <- y[i]
+ }
> 
> # Now we add AO outliers. We add in the 10th, 30th 
> # and 50nd observation the value 4.
> A[7,1] <- A[7,1]+4
> A[8,2] <- A[8,2]+4
> A[9,3] <- A[9,3]+4
> A[27,1] <- A[27,1]+4
> A[28,2] <- A[28,2]+4
> A[29,3] <- A[29,3]+4
> A[47,1] <- A[47,1]+4
> A[48,2] <- A[48,2]+4
> A[49,3] <- A[49,3]+4
> 
> #Y[10] <- Y[10]+4
> #Y[30] <- Y[30]+4
> #Y[50] <- Y[50]+4
> 
> ar100model<-A
> 
> Xmat<-ar100model[,1:3]
> 
> cov.mcd(Xmat,quantile.used=60)
$center
[1] 0.6538957 0.6561078 0.5844738

$cov
          [,1]      [,2]      [,3]
[1,] 1.5315770 0.5809420 0.7483602
[2,] 0.5809420 1.3235511 0.5575139
[3,] 0.7483602 0.5575139 1.3142478

$msg
[1] "0 singular samples of size 4 out of 2000"

$crit
[1] -1.635303

$best
 [1]  1  2  3  4  5 10 11 12 13 14 15 17 18 19 20 21 30 31 32 33 34 35 36 37 38
[26] 39 40 41 44 45 46 50 51 52 53 54 57 71 72 74 75 76 77 78 79 80 81 82 83 84
[51] 85 86 87 88 89 90 91 92 93 94

$n.obs
[1] 98

> MCD<-cov.mcd(Xmat,quantile.used=60)$best
> bestMCD<-Xmat[MCD,]
> bestMCD
              [,1]         [,2]         [,3]
 [1,]  1.185896391 -0.370296401 -0.560475647
 [2,]  0.181834289  1.185896391 -0.370296401
 [3,]  0.767694503  0.181834289  1.185896391
 [4,]  1.997905757  0.767694503  0.181834289
 [5,]  1.344239896  1.997905757  0.767694503
 [6,]  0.435555108  1.122959945 -0.409997411
 [7,]  1.071140200  0.435555108  1.122959945
 [8,]  0.596245320  1.071140200  0.435555108
 [9,]  0.128790295  0.596245320  1.071140200
[10,]  2.117233371  0.128790295  0.596245320
[11,]  1.091553969  2.117233371  0.128790295
[12,]  1.088354891 -0.635111979  1.091553969
[13,] -0.518258675  1.088354891 -0.635111979
[14,] -0.653210929 -0.518258675  1.088354891
[15,] -0.640406984 -0.653210929 -0.518258675
[16,] -1.512711659 -0.640406984 -0.653210929
[17,] -0.387569937 -0.493331874  0.061669028
[18,]  0.551567240 -0.387569937 -0.493331874
[19,]  0.822240329  0.551567240 -0.387569937
[20,]  1.302924784  0.822240329  0.551567240
[21,]  1.425491614  1.302924784  0.822240329
[22,]  1.561752949  1.425491614  1.302924784
[23,]  1.041272334  1.561752949  1.425491614
[24,]  0.735231894  1.041272334  1.561752949
[25,]  0.323973139  0.735231894  1.041272334
[26,] -0.246097747  0.323973139  0.735231894
[27,] -0.107455145 -0.246097747  0.323973139
[28,] -1.415309011 -0.107455145 -0.246097747
[29,] -0.007243569  0.940657778  1.761401140
[30,]  0.065633161 -0.007243569  0.940657778
[31,] -0.453868848  0.065633161 -0.007243569
[32,]  0.045992570  0.569107040 -0.135474869
[33,]  0.253181206  0.045992570  0.569107040
[34,]  1.454893871  0.253181206  0.045992570
[35,]  0.264543085  1.454893871  0.253181206
[36,]  2.310053311  0.264543085  1.454893871
[37,]  0.086844882  1.529898422 -0.838967934
[38,]  1.841855597 -0.036323867  1.690396079
[39,] -0.266898797  1.841855597 -0.036323867
[40,]  0.933670592  0.166194483 -0.266898797
[41,]  0.031741882  0.933670592  0.166194483
[42,] -0.745946946  0.031741882  0.933670592
[43,]  0.010687685 -0.745946946  0.031741882
[44,] -0.509192914  0.010687685 -0.745946946
[45,] -0.116190200 -0.509192914  0.010687685
[46,]  0.101636394 -0.116190200 -0.509192914
[47,] -0.403346033  0.101636394 -0.116190200
[48,]  0.594358237 -0.403346033  0.101636394
[49,] -0.273570019  0.594358237 -0.403346033
[50,]  0.560568578 -0.273570019  0.594358237
[51,]  1.100196148  0.560568578 -0.273570019
[52,]  0.990514817  1.100196148  0.560568578
[53,]  0.471795193  0.990514817  1.100196148
[54,]  1.762013825  0.471795193  0.990514817
[55,]  1.669904908  1.762013825  0.471795193
[56,]  1.846880099  1.669904908  1.762013825
[57,]  1.535404214  1.846880099  1.669904908
[58,]  0.679385027  1.535404214  1.846880099
[59,]  2.298200812  0.679385027  1.535404214
[60,]  0.313983129  2.298200812  0.679385027
>  Z<-bestMCD[,2:3]
>  W<-bestMCD[,1]
>  model<-lm(W~Z)
>  parameter_estimates<-coef(model)
>  #Calculate MDA of residuals
>  mda<-mean(abs(residuals))
>  mda
[1] 0.6772515
> c<-2
>  σ<-mda
> M<-10000
> library(gurobi)
Loading required package: slam
> env <- gurobi::gurobi_env()
Error: 'gurobi_env' is not an exported object from 'namespace:gurobi'
> T<-length(Z)
> T
[1] 120
> T<-length(bestMCD)
> T
[1] 180
> T<-length(Y)
> T
[1] 100
> # Length of the time series
> T <- length(Y)
> # Formulating the QMIP problem
> model <- list()
> model
list()
> 
> # Decision variables
> model$vars <- addVars(1:2*T, lb=0, ub=1, vtype="B")
Error in addVars(1:2 * T, lb = 0, ub = 1, vtype = "B") : 
  could not find function "addVars"
> # Decision variables
> model$vars <- gurobi::addVar(obj = rep(0, 2*T), lb = rep(0, 2*T), ub = rep(1, 2*T), vtype = rep("B", 2*T))
Error: 'addVar' is not an exported object from 'namespace:gurobi'

and I donot Know how to continue