Beta regression Elastic-Net via GAMLSS (gamlss.lasso)

Uses gamlss.lasso::gnet() to fit ENet on the mean submodel of gamlss(dist = BE). The routine assumes complete cases and does not expose offsets or precision-model terms.

Usage

betareg_enet_gamlss(
  X,
  Y,
  method = c("IC", "CV"),
  ICpen = c("BIC", "AIC", "HQC"),
  alpha = 1,
  trace = FALSE
)

Arguments

X

Numeric matrix (n × p) of mean-submodel predictors.

Y

Numeric response in (0,1). Values are squeezed to (0,1) internally.

method

"IC" (information criterion) or "CV".

ICpen

Penalty for "IC" selection: "BIC", "AIC", or "HQC".

alpha

Elastic-net mixing (1 = LASSO, 0 = ridge).

trace

Logical; print stepwise trace.

Value

Named numeric vector of coefficients as in betareg_lasso_gamlss().

See also

gamlss.lasso::gnet(), gamlss::gamlss(), gamlss.dist::BE()