Mixed Effects Regression for Linear, Non-Linear and User-Defined
Models
Description
Fits linear, non-linear, and user-defined mixed effects regression models following
the framework developed by Crowther (2017) . 'merlin' can fit multivariate
outcome models of any type, each of which could be repeatedly measured (longitudinal), with
any number of levels, and with any number of random effects at each level. Standard
distributions/models available include the Bernoulli, Gaussian, Poisson, beta,
negative-binomial, and time-to-event/survival models include the exponential,
Gompertz, Royston-Parmar, Weibull and general hazard model. 'merlin' provides a
flexible predictor syntax, allowing the user to define variables, random effects,
spline and fractional polynomial functions, functions of
other outcome models, and any interaction between each of them. Non-linear and
time-dependent effects are seamlessly incorporated into the predictor. 'merlin'
allows multivariate normal random effects, which are integrated out using Gaussian
quadrature or Monte-Carlo integration. Note, 'merlin' is based on the 'Stata' package
of the same name, described in Crowther (2018) .