Multiple-Instance Learning with Support Vector Machines
c(
person("Sean", "Kent", , "skent259@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-8697-9069")),
person("Yifei", "Liou", role = "aut")
)
Weakly supervised (WS), multiple instance (MI) data lives in
numerous interesting applications such as drug discovery, object
detection, and tumor prediction on whole slide images. The 'mildsvm'
package provides an easy way to learn from this data by training
Support Vector Machine (SVM)-based classifiers. It also contains
helpful functions for building and printing multiple instance data
frames. The core methods from 'mildsvm' come from the following
references: Kent and Yu (2022) <arXiv:2206.14704>; Xiao, Liu, and Hao
(2018) <doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012)
<https://proceedings.neurips.cc/paper/2012/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper.pdf>;
Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews
et al. (2003)
<https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>.
Many functions use the 'Gurobi' optimization back-end to improve the
optimization problem speed; the 'gurobi' R package and associated
software can be downloaded from <https://www.gurobi.com> after
obtaining a license.
dplyr, e1071, kernlab, magrittr, mvtnorm, pillar, pROC, purrr,
rlang, stats, tibble, tidyr, utils
Sean Kent [aut, cre] (<https://orcid.org/0000-0001-8697-9069>),
Yifei Liou [aut]

Multiple Imputation for Multivariate Binary Outcome
Multiple imputation for multivariate binary outcome by using Gibbs' Sampler on all potential profiles.

Mixed Logistic Regression for Genome-Wide Analysis Studies
(GWAS)
Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Milet et al 2020 <doi:10.1101/2020.01.17.910109>.

Multiple-Instance Logistic Regression with LASSO Penalty
The multiple instance data set consists of many independent subjects
(called bags) and each subject is composed of several components (called instances).
The outcomes of such data set are binary or multinomial, and, we can only observe
the subject-level outcomes. For example, in manufactory processes, a subject is labeled
as "defective" if at least one of its own components is defective, and otherwise, is
labeled as "non-defective".  The milr package focuses on the predictive model for the
multiple instance data set with binary outcomes and performs the maximum likelihood
estimation with the Expectation-Maximization algorithm under the framework of logistic
regression.  Moreover, the LASSO penalty is attached to the likelihood function for
simultaneous parameter estimation and variable selection.
c(person("Ping-Yang", "Chen", , "pychen.ping@gmail.com", c("aut", "cre")),
person("ChingChuan", "Chen", , "celestial0230@stat.sinica.edu.tw", "aut"),
person("Chun-Hao", "Yang", , "chunhaoyang@ufl.edu", "aut"),
person("Sheng-Mao", "Chang", , "smchang@mail.ncku.edu.tw", "aut"))
Ping-Yang Chen [aut, cre],
ChingChuan Chen [aut],
Chun-Hao Yang [aut],
Sheng-Mao Chang [aut]
assertthat, pipeR (>= 0.5), numDeriv, purrr (>= 0.2.0), Rcpp
(>= 0.12.0), utils
'RcppExports.R' 'milr-package.R' 'DGP.R' 'softmax.R' 'milr.R'