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.)

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.

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.)

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.)

An object that represents the training protocol of models. A recipe can be created using FIT::make.recipe().

A collection of models being trained as is returnd by FIT::init().

At this moment, it must be a list (genes) of a list (envs) of models and must contain at least one model. (THIS MIGHT CHANGE IN A FUTURE.)

An optional number that specifies the maximal number of times that the parameter optimization is performed.

The user can control this parameter by using the opts argument for FIT::train().

An optional number that specifies the order of cross validation when optim method is 'lasso'. This is simply ignored when optim method is 'lm'.