fit.models: A raw API for fixing linear regression coefficients.
In FIT: Transcriptomic Dynamics Models in Field Conditions

Note: use train() unless the user is willing to accept breaking API changes in the future.

1	fit.models(expression, weight, attribute, weather, recipe, models)

`expression`	An object that represents gene expression data. The object can be created from a dumped/saved dataframe of size `nsamples * ngenes` using `FIT::load.expression()`. (At the moment it is an instance of a hidden class IO$Attribute, but this may be subject to change.)
`weight`	A matrix of size `nsamples * ngenes` that during regression penalizes errors from each sample using the formula `sum_{s in samples} (weight_s) (error_s)^2`. Note that, unlike for `FIT::train()`, this argument is NOT optional.
`attribute`	An object that represents the attributes of microarray/RNA-seq data. The object can be created from a dumped/saved dataframe of size `nsamples * nattributes` using `FIT::load.attribute()`. (At the moment it is an instance of a hidden class IO$Attribute, but this may be subject to change.)
`weather`	An object that represents actual or hypothetical weather data with which the training of models are done. The object can be created from a dumped/saved dataframe of size `ntimepoints * nfactors` using `FIT::load.weather()`. (At the moment it is an instance of a hidden class IO$Weather, but this may be subject to change.)
`recipe`	An object that represents the training protocol of models. A recipe can be created using `FIT::make.recipe()`.
`models`	A collection of models being trained as is returnd by `FIT::optim()`.