The `multilevelcoda`

package implements Bayesian
multilevel models for compositional data in R, by combining the
principles of the two well-known analyses, Multilevel Modelling and
Compositional Data Analysis. Formula syntax is built using package brms
and is similar to package lme4, which allows for different modelling
options in a multilevel framework. The package also provides several
useful functions for post-hoc analyses and visualisation of final
results.

## Compositional Data
Analysis

Compositional data analysis (CoDA) is an analysis of compositional
and multivariate positive data. Compositional data are typically
expressed in amount, e.g., percentage, proportion, and often sum up to a
constant, usually 100% or one. These data are common in many fields:
ecology (e.g., relative abundances of species), geography (e.g.,
proportions of land use), biochemistry (e.g., fatty acid proportions),
nutritional epidemiology (e.g., intake of macronutrients like proteins,
fats and carbohydrates), and time-use epidemiology (e.g., time spent in
different sleep-wake behaviours during the 24-hour day).

## Multilevel Modelling
for Compositional Data

Compositional data can be non-independent and repeated measures data.
For example, sleep-wake behaviours are often measured across multiple
time points (e.g., across several consecutive days).

Therefore, we often use multilevel models to include both fixed
effects (regression coefficients that are identical for everyone) and
random effects (regression coefficients that vary randomly for each
person). In addition, we can also decompose these data into two sources
of variability: between-person (differences between individuals) and
within-person (differences within individuals).

In `multilevelcoda`

package, we implements Compositional
Multilevel Model to model compositional data in amultilevel framework.
`mulitlevelcoda`

includes functions to compute Isometric log
ratio (ILR) for between and within-person levels, fit Baysian multilevel
model, and conduct post-hoc analyses such as susbtitution models. See
below for vignettes: