Efficient algorithms for fitting regularization paths for
        linear or logistic regression models penalized by LEP.

Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.

Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.

Fits generalized linear mixed models for a single grouping factor under
    maximum likelihood approximating the integrals over the random effects with an 
    adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) 
    <doi:10.1080/10618600.1995.10474663>.

Allows users to fit a cosinor model using the 'glmmTMB' framework. 
    This extends on existing cosinor modeling packages, including 'cosinor' 
    and 'circacompare', by including a wide range of available link functions 
    and the capability to fit mixed models. The cosinor model is described by 
    Cornelissen (2014) <doi:10.1186/1742-4682-11-16>.

Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.

Fit generalized linear mixed models (GLMMs) with normal random
    effects using first-order Laplace, fully exponential Laplace (FEL) with
    mean-only corrections, and FEL with mean and covariance corrections in
    the E-step of an expectation-maximization (EM) algorithm. The current
    development version provides a matrix-based interface (y, X, Z) and
    supports binary logit and probit, and Poisson log-link models. An EM
    framework is used to update fixed effects, random effects, and a single
    variance component tau^2 for G = tau^2 I, with staged approximations
    (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A
    pseudo-likelihood engine glmmFEL_pl() implements the working-response /
    working-weights linearization approach of Wolfinger and O'Connell (1993)
    <doi:10.1080/00949659308811554>, and is adapted from the implementation
    used in the 'RealVAMS' package (Broatch, Green, and Karl (2018))
    <doi:10.32614/RJ-2018-033>. The FEL implementation follows Karl, Yang,
    and Lohr (2014) <doi:10.1016/j.csda.2013.11.019> and related work (e.g.,
    Tierney, Kass, and Kadane (1989) <doi:10.1080/01621459.1989.10478824>;
    Rizopoulos, Verbeke, and Lesaffre (2009)
    <doi:10.1111/j.1467-9868.2008.00704.x>; Steele (1996)
    <doi:10.2307/2532845>). Package code was drafted with assistance from
    generative AI tools.