mixed effects models in julia

We have hosted the application mixed effects models in julia in order to run this application in our online workstations with Wine or directly.

Quick description about mixed effects models in julia:

This package defines linear mixed models (LinearMixedModel) and generalized linear mixed models (GeneralizedLinearMixedModel). Users can use the abstraction for statistical model API to build, fit (fit/fit!), and query the fitted models. A mixed-effects model is a statistical model for a response variable as a function of one or more covariates. For a categorical covariate the coefficients associated with the levels of the covariate are sometimes called effects, as in "the effect of using Treatment 1 versus the placebo". If the potential levels of the covariate are fixed and reproducible, e.g. the levels for Sex could be "F" and "M", they are modeled with fixed-effects parameters. If the levels constitute a sample from a population, e.g. the Subject or the Item at a particular observation, they are modeled as random effects.

Features:

• A mixed-effects model contains both fixed-effects and random-effects terms
• With fixed-effects it is the coefficients themselves or combinations of coefficients that are of interest
• For random effects it is the variability of the effects over the population that is of interest
• For Windows, Linux, and macOS
• A mixed-effects model is a statistical model for a response variable as a function of one or more covariates
• Typical distribution forms are Bernoulli for binary data or Poisson for count data

Programming Language: Julia.
Categories: