Tools that allow developers to write functions for
    cross-validation with minimal programming effort and assist
    users with model selection.

Tools for performing cross-validation (CV). The main function is a 
    general purpose wrapper that performs k-fold CV for any tuning parameter in 
    any supervised learning method. The package also has a function that 
    computes the loss incurred by a set of predictions for a variety of loss 
    functions and model families.

An object-oriented modeling language for disciplined
    convex programming (DCP) as described in Fu, Narasimhan, and Boyd
    (2020, <doi:10.18637/jss.v094.i14>). It allows the user to
    formulate convex optimization problems in a natural way following
    mathematical convention and DCP rules. The system analyzes the
    problem, verifies its convexity, converts it into a canonical
    form, and hands it off to an appropriate solver to obtain the
    solution. Interfaces to solvers on CRAN and elsewhere are
    provided, both commercial and open source.

The 'Corpus Workbench' ('CWB', <https://cwb.sourceforge.io/>) offers a classic and mature
 approach for working with large, linguistically and structurally annotated corpora. The 'CWB'
 is memory efficient and its design makes running queries fast, see Evert (2011)
 <https://eprints.lancs.ac.uk/id/eprint/62721>. The 'cwbtools' package offers
 pure 'R' tools to create indexed corpus files as well as high-level wrappers for the original 'C'
 implementation of 'CWB' as exposed by the 'RcppCWB' package
 (<https://CRAN.R-project.org/package=RcppCWB>). Additional functionality to add and
 modify annotations of corpora from within 'R' makes working with 'CWB' indexed corpora
 much more flexible and convenient. The 'cwbtools' package in combination with the 'R' packages
 'RcppCWB' (<https://CRAN.R-project.org/package=RcppCWB>) and 'polmineR'
 (<https://CRAN.R-project.org/package=polmineR>) offers a lightweight infrastructure
 to support the combination of quantitative and qualitative approaches for working
 with textual data.