Imports: caret (>= 6.0.78), data.table, DBI, doParallel, dplyr, e1071,
ggplot2, grpreg, lme4, methods, odbc, pROC, R6, ranger (>=
Packaged: 2017-12-21 05:16:46 UTC; michael.levy

Packaged: 2018-02-14 17:59:28 UTC; michael.levy

Imports: broom, caret (>= 6.0.78), cowplot, data.table, dbplyr, dplyr,
e1071, ggplot2, kknn, lubridate, methods, MLmetrics, purrr,
ranger (>= 0.8.0), recipes (>= 0.1.2), rlang, stringr, tibble,
Suggests: DBI, odbc, testthat, lintr, covr
Packaged: 2018-04-20 22:22:51 UTC; michael.levy

e1071, ggplot2, glmnet, lubridate, MLmetrics, purrr, ranger (>=
0.8.0), recipes (>= 0.1.3), rlang, stringr, tibble, tidyr,
Packaged: 2018-07-01 04:09:59 UTC; michael.levy

Imports: broom, caret (>= 6.0.78), cowplot, data.table, dbplyr, dplyr
(>= 0.7.6), e1071, ggplot2, glmnet, lubridate, MLmetrics,
purrr, ranger (>= 0.8.0), recipes (>= 0.1.3), rlang, ROCR,
Packaged: 2018-09-01 17:11:35 UTC; michael.levy

person("Mike", "Mastanduno", email = "michael.mastanduno@healthcatalyst.com", role = c("aut", "cre")),
person("Rex", "Sumsion", email = "rex.sumsion@healthcatalyst.com", role = "aut"))
Imports: caret (>= 6.0.81), cowplot, data.table, dbplyr, dplyr (>=
0.7.6), e1071, forcats, generics, ggplot2, glmnet, lubridate,
MLmetrics, purrr, ranger (>= 0.8.0), recipes (>= 0.1.3.9002),
rlang, ROCR, stringr, tibble, tidyr, xgboost
Packaged: 2018-12-12 23:01:15 UTC; michael.mastanduno

Imports: caret (>= 6.0.81), cowplot, data.table, dplyr (>= 0.7.6),
e1071, generics, ggplot2, glmnet, lubridate, MLmetrics, purrr,
Suggests: covr, DBI, dbplyr, lintr, odbc, testthat
Packaged: 2020-02-28 17:13:57 UTC; michael.mastanduno

Imports: caret (>= 6.0.81), cowplot, data.table, dplyr (>= 1.0.0),
stringr, tibble (>= 3.0.0), tidyr, xgboost
Packaged: 2020-08-05 20:24:47 UTC; michael.mastanduno

Description: Aims to make machine learning in healthcare as easy as possible.
You can develop customized, reliable, high-performance machine learning models with minimal code.
Models are created with automatic preprocessing, hyperparameter tuning, and
and 'glm' Friedman J, Hastie T, Tibshirani R. (2010) <doi:10.18637/jss.v033.i01>)
put into production. Additionally, there are tools to help understand how a model makes its
predictions, select prediction threshholds for operational use, and evaluate model performance over time.
Code uses 'tidyverse' syntax and most methods have an associated visualization.
Packaged: 2022-09-01 15:48:34 UTC; michael.mastanduno