Fits Gaussian linear mixed models with a random intercept
when the response is subject to left, right, and/or interval
censoring, using the Expectation/Conditional Maximization Either
(ECME) algorithm of Liu and Rubin (1994) in the spirit of the
fast censored-response mixed-model algorithm of Vaida and Liu
(2009). Simultaneous estimation and variable selection is
supported through coordinate-descent penalized maximization with
Lasso, Adaptive Lasso, SCAD, MCP, Elastic Net, and Ridge
penalties (no penalty is also supported). The random intercept is
integrated out by Gauss-Hermite quadrature at every iteration, and
the two ECME conditional-maximization steps respectively maximize
the expected penalized complete-data objective (for the
regression coefficients) and the actual observed-data marginal
likelihood (for the variance components), which is the defining
feature of ECME relative to plain ECM/EM. The package provides a
single-fit engine, a sequential/parallel penalty-parameter grid
search with information-criterion or cross-validated selection,
data-dependent or user-supplied lambda grids, and an
Expectation-Maximization based treatment of a completely missing
(at random) response, sharing the same truncated-normal machinery
used for censoring. References: Liu and Rubin (1994)
"The ECME algorithm: A simple extension of EM and ECM with
faster monotone convergence"