The caroline R library contains dozens of functions useful
 for: database migration (dbWriteTable2), database style joins &
 aggregation (nerge, groupBy, & bestBy), data structure
 conversion (nv, tab2df), legend table making (sstable & leghead),
 automatic legend positioning for scatter and box plots (),  
 plot annotation (labsegs & mvlabs), data visualization 
 (pies, sparge, confound.grid & raPlot), character string manipulation (m & pad),
 file I/O (write.delim), batch scripting, data exploration, and more.
 The package's greatest contributions lie in the database style merge, 
 aggregation and interface functions as well as in it's extensive
 use and propagation of row, column and vector names in most functions.

Mainly used to build tables that are commonly presented for
    bio-medical/health research, such as basic characteristic tables or
    descriptive statistics.

Sending functions to remote processes can be wasteful of
    resources because they carry their environments with them. With the
    carrier package, it is easy to create functions that are isolated from
    their environment. These isolated functions, also called crates, print
    at the console with their total size and can be easily tested locally
    before being sent to a remote.

Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.