Authors@R: c(person("Torsten", "Hothorn", role = c("cre", "aut"),
person("Peter", "Buehlmann", role = "aut",
person("Matthias", "Schmid", role = "aut",
person("Benjamin", "Hofner", role = "aut",
person("Fabian", "Otto-Sobotka", role = "ctb",
person("Fabian", "Scheipl", role = "ctb",
Models and algorithms are described in \doi{10.1214/07-STS242},
a hands-on tutorial is available from \doi{10.1007/s00180-012-0382-5}.
The package allows user-specified loss functions and base-learners.
Author: Torsten Hothorn [cre, aut] (<https://orcid.org/0000-0001-8301-0471>),

Imports: Matrix, survival (>= 3.2-10), splines, lattice, nnls,
quadprog, utils, graphics, grDevices, partykit (>= 1.2-1)

Models and algorithms are described in <doi:10.1214/07-STS242>,
a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>.

Title: Converting an Mbox File into a Tibble
Description: Importing and converting an mbox file into a tibble object.
Imports: reticulate, readr, tibble, magrittr, purrr, dplyr
Suggests: knitr, rmarkdown, webshot, testthat, covr

Imports: reticulate, tibble, magrittr, purrr, dplyr, lubridate

Title: Reading, Extracting, and Converting an Mbox File into a Tibble
person("Soyoung", "Choi", role = "aut", email = "sxc940@psu.edu", comment = c(ORCID = "0000-0002-0998-3352")))

Imports: reticulate, tibble, magrittr, purrr, dplyr, lubridate,

Description: Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). The algorithm implements a penalty term to minimize the differences between the total sub-annual flows and the annual flow. The method is described in Nguyen et al (2020) <DOI:10.1002/essoar.10504791.1>.
Imports: data.table, dplR, MASS, Matrix, Rfast, stats

Title: Model-Based Response Dimension Reduction
Maintainer: Jae Keun Yoo <peter.yoo@ewha.ac.kr>
Description: Functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses.  The response dimension reduction is theoretically founded in Yoo and Cook (2008) <doi:10.1016/j.csda.2008.07.029>. Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) <doi:10.1080/02331888.2017.1410152> and Yoo (2019) <doi:10.1016/j.jkss.2019.02.001>. The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace.