cv.GLglmnetN: Cross validation for L1-Penalized Q-Leanring Given group...
In sambiostat/WAPL: What the Package Does (One Line, Title Case)

Cross validation for L1-Penalized Q-Leanring Given group information, the variable selection can be L1-lasso or group-lasso

cv.GLglmnetN(
  x,
  y,
  Yorig = NULL,
  A,
  group = c(rep(1:10, each = 5), 11, rep(12:21, each = 5)),
  pA,
  nfolds = 10,
  loss = "ls",
  ...
)

`x:`	n by p matrix of features
`y:`	weights vector of length n
`Yorig:`	original outcome vector of length n
`A:`	treatment vector of length n
`pentype:`	whether penalized Q-learning is used or not, default is "lasso"
`group:`	group number, vector of length (2p+1), have to be consective, in each individual is one group then set group=seq(1: (2p+1))
`loss:`	default is "ls" for least square loss
`pA:`	propensity score, vector of length n
`nfolds:`	number of cross validation fold, should be an integer >3