person(given="Patrick", family="Bachmann",    email = "pbachma@ethz.ch", role = c("cre","aut")),
Suggests: covr, knitr, rmarkdown, testthat

Title: Commercial Modular Aero-Propulsion System Simulation Data Set
Authors@R: c(person("Morteza", "Amini", role = c("aut", "cre","cph"),
person("Afarin", "Bayat", role = "aut"))
Description: Contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.

Author: Michela Cameletti and Valeria Caviezel
Description: Calculation and plot of the stepwise Cronbach-Mesbah Curve

Title: Reusable Assisting Functions for Child Mortality Estimation
Description: Provide helper functions for UNICEF child mortality estimation.
Imports: ggplot2 (>= 3.0.0), ggforce (>= 0.2.1.9000), here, scales,
readr, readxl, xlsx, usethis, lubridate, stringr
Author: Yang Liu [aut, cre] (<https://orcid.org/0000-0001-6557-6439>)

Imports: ggplot2 (>= 3.0.0), here, readxl, xlsx, usethis

Description: 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

person(given = "Arto", family = "Klami", email = "arto.klami@cs.helsinki.fi",
person(given = "Lauri", family = "Väre", role = "aut"),
person(given = "Felix", family = "Held", email = "felix.held@chalmers.se",
entities. This code learns a variational Bayesian approximation for CMF,
for each matrix. For further details on the method see
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) <http://www.jmlr.org/papers/v14/klami13a.html> and
Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html>
for details on Bayesian CCA and GFA, respectively.