Description of feature

In #325/#339, an attempt was made to add support for limma mixed models. However, I believe this approach is not ideal for the following reasons

• Limma supports only one random effect variable
• It requires to manually combine multiple experimental factors into a single variable, therefore making it cumbersome to use

Alternatively, I propose to add support for a linear mixed effects model that generalizes to an arbitrary number of random effects and allows to specify random effect variables using Wilkinson formulas, e.g. ~ timepoint + (1 | patient_id).

An obvious candidate would be DREAM which is a generalization of limma/voom for random effects. It's certainly a good choice for gene expression data as it also does the empirical bayes shrinkage of gene dispersions.

However, there's also other models that could be condidered, e.g. MRMM, or plain lme4 models.

Depends on #362 for the Wilkinson formulae
Mixed effects models are also a way of addressing technical replicates -> fixes #220

CC @apeltzer @tschwarzl @nschcolnicov @atrigila @alanmmobbs93