Conditional Multivariate t Distribution, Expectation
Maximization Algorithm, and Its Stochastic Variants
c(person("Paul", "Kinyanjui", role=c("aut", "cre"),
email="kinyanjui.access@gmail.com"),
person("Cox", "Tamba", role="aut",
email="cox.tamba@egerton.ac.ke"),
person("Justin", "Okenye", role="aut",
email="justinoke69@gmail.com"),
person("Luke", "Orawo", role="ctb",
email="orawo2000@yahoo.com"))
Computes conditional multivariate t probabilities, random deviates, and densities. It can also be used to create missing values at random in a dataset, resulting in a missing at random (MAR) mechanism. Inbuilt in the package are the Expectation-Maximization (EM), Monte Carlo EM, and Stochastic EM algorithms for imputation of missing values in datasets assuming the multivariate t distribution. See Kinyanjui, Tamba, Orawo, and Okenye (2020)<doi:10.3233/mas-200493>, and Kinyanjui, Tamba, and Okenye(2021)<http://www.ceser.in/ceserp/index.php/ijamas/article/view/6726/0> for more details.
Paul Kinyanjui [aut, cre],
Cox Tamba [aut],
Justin Okenye [aut],
Luke Orawo [ctb]

Condition Number Regularized Covariance Estimation
Sang-Yun Oh <sangoh@stanford.edu>, Bala Rajaratnam
<brajarat@stanford.edu>, Joong-Ho Won <wonj@korea.ac.kr>

Sang-Yun Oh <syoh@lbl.gov>, Joong-Ho Won <wonj@stats.snu.ac.kr>

Censored Data Imputation for Direct Modeling
c(
person("Yizhuo", "Wang", role=c("aut", "cre"), email="ywang70@mdanderson.org", comment = c(ORCID = "0000-0002-1870-0019")),
person("Ziyi", "Li", role=c("aut")),
person("Xuelin", "Huang", role=c("aut")),
person("Christopher", "Flowers", role=c("ctb"))
)
Impute the survival times for censored observations based on their conditional survival distributions derived from the Kaplan-Meier estimator. 'CondiS' can replace the censored observations with the best approximations from the statistical model, allowing for direct application of machine learning-based methods. When covariates are available, 'CondiS' is extended by incorporating the covariate information through machine learning-based regression modeling ('CondiS_X'), which can further improve the imputed survival time.
caret, survival, kernlab, purrr, tidyverse, survminer
Yizhuo Wang [aut, cre] (<https://orcid.org/0000-0002-1870-0019>),
Ziyi Li [aut],
Xuelin Huang [aut],
Christopher Flowers [ctb]