pcb: boost principal components of outcomes
In patr1ckm/mvtboost: Tree Boosting for Multivariate Outcomes

boost principal components of outcomes

pcb(Y, X, n.trees = 100, shrinkage = 0.01, interaction.depth = 1,
  distribution = "gaussian", train.fraction = 1, bag.fraction = 1,
  cv.folds = 1, keep.data = FALSE, s = NULL, compress = FALSE,
  save.cv = FALSE, iter.details = TRUE, verbose = FALSE, mc.cores = 1,
  ...)

`Y`	vector, matrix, or data.frame for outcome variables with no missing values. To easily compare influences across outcomes and for numerical stability, outcome variables should be scaled to have unit variance.
`X`	vector, matrix, or data.frame of predictors. For best performance, continuous predictors should be scaled to have unit variance. Categorical variables should converted to factors.
`n.trees`	maximum number of trees to be included in the model. Each individual tree is grown until a minimum number observations in each node is reached.
`shrinkage`	a constant multiplier for the predictions from each tree to ensure a slow learning rate. Default is .01. Small shrinkage values may require a large number of trees to provide adequate fit.
`interaction.depth`	fixed depth of trees to be included in the model. A tree depth of 1 corresponds to fitting stumps (main effects only), higher tree depths capture higher order interactions (e.g. 2 implies a model with up to 2-way interactions)
`distribution`	Character vector specifying the distribution of all outcomes. Default is "gaussian" see ?gbm for further details.
`train.fraction`	proportion of the sample used for training the multivariate additive model. If both `cv.folds` and `train.fraction` are specified, the CV is carried out within the training set.
`bag.fraction`	proportion of the training sample used to fit univariate trees for each response at each iteration. Default: 1
`cv.folds`	number of cross validation folds. Default: 1. Runs k + 1 models, where the k models are run in parallel and the final model is run on the entire sample. If larger than 1, the number of trees that minimize the multivariate MSE averaged over k-folds is reported in `object$best.trees`
`keep.data`	a logical variable indicating whether to keep the data stored with the object.
`s`	vector of indices denoting observations to be used for the training sample. If `s` is given, `train.fraction` is ignored.
`compress`	`TRUE/FALSE`. Compress output results list using bzip2 (approx 10% of original size). Default is `FALSE`.
`save.cv`	`TRUE/FALSE`. Save all k-fold cross-validation models. Default is `FALSE`.
`iter.details`	`TRUE/FALSE`. Return training, test, and cross-validation error at each iteration. Default is `FALSE`.
`verbose`	If `TRUE`, will print out progress and performance indicators for each model. Default is `FALSE`.
`mc.cores`	Number of cores for cross validation.
`...`	additional arguments passed to `gbm`. These include `distribution`, `weights`, `var.monotone`, `n.minobsinnode`, `keep.data`, `verbose`, `class.stratify.cv`. Note that other `distribution` arguments have not been tested.