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.

Collective matrix factorization (a.k.a. multi-view or multi-way factorization,
	Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a (potentially very sparse
	or having many missing values) matrix 'X' as the product of two low-dimensional matrices,
	optionally aided with secondary information matrices about rows and/or columns of 'X',
	which are also factorized using the same latent components.
	The intended usage is for recommender systems, dimensionality reduction, and missing value imputation.
	Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce
	different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky,
	(2008) <doi:10.1109/ICDM.2008.22>), the 'weighted-lambda-regularization' model,
	(Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>),
	or the enhanced model with 'implicit features' (Rendle, Zhang,
	Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based
	procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009)
	<doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver
	(Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative
	coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>),
	providing efficient methods for sparse and dense data, and mixtures thereof.
	Supports L1 and L2 regularization in the main models,
	offers alternative most-popular and content-based models, and implements functionality
	for cold-start recommendations and imputation of 2D data.