Writing interfaces to command line software is cumbersome. 
    'cmdfun' provides a framework for building function calls to seamlessly 
    interface with shell commands by allowing lazy evaluation of command line arguments. 
    'cmdfun' also provides methods for handling user-specific paths to tool installs or secrets like API keys. 
    Its focus is to equally serve package builders who wish to wrap command line software, and to help analysts stay inside 
    R when they might usually leave to execute non-R software.

Provides functions for implementing cmenet - a bi-level variable selection method for conditional main effects (see Mak and Wu (2018) <doi:10.1080/01621459.2018.1448828>). CMEs are reparametrized interaction effects which capture the conditional impact of a factor at a fixed level of another factor. Compared to traditional two-factor interactions, CMEs can quantify more interpretable interaction effects in many problems. The current implementation performs variable selection on only binary CMEs; we are working on an extension for the continuous setting.

Collective matrix factorization (CMF) finds joint low-rank
	representations for a collection of matrices with shared row or column
	entities. This code learns a variational Bayesian approximation for CMF,
	supporting multiple likelihood potentials and missing data, while
	identifying both factors shared by multiple matrices and factors private
	for each matrix. For further details on the method see
	Klami et al. (2014) <arXiv:1312.5921>.
	The package can also be used to learn Bayesian canonical correlation
	analysis (CCA) and group factor analysis (GFA) models, both of which are
	special cases of CMF. This is likely to be useful for people looking for
	CCA and GFA solutions supporting missing data and non-Gaussian likelihoods.
	See Klami et al. (2013) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf>
	and	Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html>
	for details on Bayesian CCA and GFA, respectively.