dot-fit_gau_em: EM algorithm for AD with missing data

In antedep: Antedependence Models for Longitudinal Data

EM algorithm for AD with missing data

Description

Implements the EM algorithm for fitting AD(p) models with missing data under MAR (Missing At Random) assumption.

Usage

.fit_gau_em(
  y,
  order,
  blocks = NULL,
  estimate_mu = TRUE,
  max_iter = 100,
  tol = 1e-06,
  verbose = FALSE,
  ...
)

Arguments

y	Data matrix (n_subjects x n_time), may contain NA
order	Antedependence order (0, 1, or 2)
blocks	Block membership vector (optional)
estimate_mu	Logical, whether to estimate mu (default TRUE)
max_iter	Maximum number of EM iterations (default 100)
tol	Convergence tolerance on log-likelihood (default 1e-6)
verbose	Logical, print iteration info (default FALSE)

Value

List with fitted model (same structure as fit_gau, plus EM info)

antedep documentation built on April 25, 2026, 1:06 a.m.