c(
person(given = "Arto", family = "Klami", email = "arto.klami@cs.helsinki.fi",
role = "aut"),
person(given = "Lauri", family = "Väre", role = "aut"),
person(given = "Felix", family = "Held", email = "felix.held@gmail.com",
role = c("ctb", "cre")))
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 (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns 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.

Calibrated Model Fusion Approach to Combine Surrogate Markers
c(person("Xuan", "Wang", role = "aut"),
person("Layla", "Parast", role = "cre", email = "parast@austin.utexas.edu"))
Uses a calibrated model fusion approach to optimally combine multiple surrogate markers. Specifically, two initial estimates of optimal composite scores of the markers are obtained; the optimal calibrated combination of the two estimated scores is then constructed which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained (PTE) by the final combined score. The primary function, pte.estimate.multiple(), estimates the PTE of the identified combination of multiple surrogate markers. Details are described in Wang et al (2022) <doi:10.1111/biom.13677>.