Title: User-Friendly R Package for Supervised Machine Learning
Description: An interface to build machine learning models for classification
and regression problems. 'mikropml' implements the ML pipeline described
by Topçuoğlu et al. (2020) <doi:10.1128/mBio.00434-20> with reasonable
default options for data preprocessing, hyperparameter tuning,
cross-validation, testing, model evaluation, and interpretation steps.
See the website <http://www.schlosslab.org/mikropml/> for more information,
Imports: caret, dplyr, e1071, glmnet, kernlab, MLmetrics, randomForest,
Suggests: doFuture, foreach, future, future.apply, ggplot2, knitr,
purrr, rmarkdown, rpart, testthat, tidyr
Author: Begüm Topçuoğlu [aut] (<https://orcid.org/0000-0003-3140-537X>),
Kelly Sovacool [aut, cre] (<https://orcid.org/0000-0003-3283-829X>),

progress, progressr, purrr, rmarkdown, testthat, tidyr

See the website <https://www.schlosslab.org/mikropml/> for more information,

Description: An interface to build machine learning models for
classification and regression problems. 'mikropml' implements the ML
pipeline described by Topçuoğlu et al. (2020)
<doi:10.1128/mBio.00434-20> with reasonable default options for data
preprocessing, hyperparameter tuning, cross-validation, testing, model
evaluation, and interpretation steps.  See the website

Title: Multiple-Instance Learning with Support Vector Machines
person("Sean", "Kent", , "skent259@gmail.com", role = c("aut", "cre"),
Description: 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
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
Imports: dplyr, e1071, kernlab, magrittr, mvtnorm, pillar, pROC, purrr,
Suggests: covr, gurobi, Matrix, testthat
Author: Sean Kent [aut, cre] (<https://orcid.org/0000-0001-8697-9069>),