cv.aglm: Cross-validation for AGLM models
In kazuzowo/aglm-LVar: Accurate Generalized Linear Model (AGLM)

Cross-validation for AGLM models

cv.aglm(x, y, qualitative_vars_UD_only = NULL,
  qualitative_vars_both = NULL, qualitative_vars_OD_only = NULL,
  quantitative_vars = NULL, add_linear_columns = TRUE,
  add_OD_columns_of_qualitatives = TRUE,
  add_interaction_columns = TRUE, OD_type_of_quantitatives = "C",
  bins_list = NULL, bins_names = NULL, weights, offset = NULL,
  lambda = NULL, type.measure, nfolds = 10, foldid, grouped = TRUE,
  keep = FALSE, parallel = FALSE, standardize = TRUE,
  family = c("gaussian", "binomial", "poisson"), alpha = 1,
  nlambda = 100, lambda.min.ratio = NULL, intercept = TRUE,
  thresh = 1e-07, dfmax = NULL, pmax = NULL, exclude,
  penalty.factor = NULL, lower.limits = -Inf, upper.limits = Inf,
  maxit = 1e+05, type.gaussian = NULL, type.logistic = c("Newton",
  "modified.Newton"), standardize.response = FALSE)

`x`	An input matrix or data.frame to be fitted.
`y`	An integer or numeric vector which represents response variable.
`qualitative_vars_UD_only`	A list of indices or names for specifying which columns are qualitative and need only U-dummy representations.
`qualitative_vars_both`	A list of indices or names for specifying which columns are qualitative and need both U-dummy and O-dummy representations.
`qualitative_vars_OD_only`	A list of indices or names for specifying which columns are qualitative and need only O-dummy representations.
`quantitative_vars`	A list of indices or names for specyfying which columns are quantitative.
`add_linear_columns`	A boolean value which indicates whether this function uses linear effects or not.
`add_OD_columns_of_qualitatives`	A boolean value which indicates whether this function use O-dummy representations for qualitative and ordinal variables or not.
`add_interaction_columns`	A boolean value which indicates whether this function uses interaction effects or not.
`OD_type_of_quantitatives`	A character value which indicates how O-dummy matrices of quantitative values are constructed. Choose 'C'(default) or 'J'. 'C': Continuous-type dummies, which result continuous contribution curves. 'J': Jum-type dummies, which result contribution curves with jumps.
`bins_list`	A list of numeric vectors, each element of which is used as breaks when binning of a quantitative variable or a qualitative variable with order.
`bins_names`	A list of column name or column index, each name or index of which specifies which column of `x` is binned used with an element of `bins_list` in the same position.
`family`	Response type. Currently "gaussian", "binomial", and "poisson" are supported.
`...`	Other arguments are passed directly to backend (currently cv.glmnet() is used), and if not given, backend API's default values are used to call backend functions.