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 (NMF). 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.

c(person("Xihui", "Lin", email = "ericxihuilin@gmail.com", role = c("aut", "cre")),
person("Paul C Boutros", role = "aut", email = "Paul.Boutros@oicr.on.ca"))

c(person("Xihui", "Lin", email = "ericxihuilin@outlook.com", role = c("aut", "cre")),
person("Paul C Boutros", role = "aut", email = "Paul.Boutros@oicr.on.ca"))

Nearest Neighbor Based Multiple Imputation for Survival Data
with Missing Covariates
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.

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.

Nonlinear nonparametric statistics using partial moments.

Nonlinear nonparametric statistics using partial moments.  Partial moments are the elements of variance and asymptotically approximate the area of f(x).  These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.