Imports: shiny, dygraphs, DT, git2r (>= 0.18.0), dplyr (>= 0.7.2),
methods, stringr (>= 1.3.1), rlang (>= 0.2.1), diffobj (>=
0.7), crayon (>= 1.3.4), rprojroot, htmltools, rmarkdown,
Suggests: testthat, knitr, ggplot2, data.tree, xpose, lubridate,
pmxTools, Hmisc, rsample, renv, desc, purrr, digest, covr,

Title: Fast and Versatile Non-Negative Matrix Factorization
Description: This is a package for Non-Negative Linear Models (NNLM). It implements
fast sequential coordinate descent algorithms for non-negative linear regression
and non-negative matrix factorization. It supports mean square error and Kullback-Leibler divergence loss.
Many other features are also implemented, including missing value imputation, domain knowledge integration,
designable W and H matrices and multiple forms of regularizations.
Authors@R: c(person("Xihui", "Lin", email = "xihuil.silence@gmail.com", role = c("aut", "cre")),
person("Paul C Boutros", role = "aut", email = "Paul.Boutros@oicr.on.ca"))
Suggests: testthat, knitr, rmarkdown, mice, missForest, ISOpureR

and non-negative matrix factorization (NMF). It supports mean square error and Kullback-Leibler divergence loss.

Authors@R: c(person("Xihui", "Lin", email = "ericxihuilin@gmail.com", role = c("aut", "cre")),

Authors@R: c(person("Xihui", "Lin", email = "ericxihuilin@outlook.com", role = c("aut", "cre")),

Title: Nearest Neighbor Based Multiple Imputation for Survival Data
Author: Di Ran, Chiu-Hsieh Hsu, Mandi Yu
Description: Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, Long et al. (2012) <doi:10.5705/ss.2010.069>, Hsu et al. (2014) <doi:10.1080/10543406.2014.888444>, and Hsu and Yu (2017) <arXiv:1710.04721>. Note that the current version can only impute for a situation with one missing covariate.
Imports: stats, graphics, parallel, survival

Description: Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, and Hsu and Yu (2018) <doi: 10.1177/0962280218772592>. Note that the current version can only impute for a situation with one missing covariate.