Packaged: 2022-08-18 04:28:27 UTC; eafreeman

Title: Create Model Matrix and Save the Transforming Parameters
Description: The model.matrix() function in R is  convenient for transforming training dataset for modeling.
But it does not save any parameter used in transformation, so it is hard to apply the same transformation
to test dataset or new dataset. This package is created to solve the problem.

Title: Rapid Calculation of Model Metrics
Authors@R: person("Tyler", "Hunt", email = "thunt@snapfinance.com", role = c("aut", "cre"))
Description: Collection of metrics for evaluating models written in C++ using 'Rcpp'.
Packaged: 2016-08-16 22:47:34 UTC; snapadmin

Packaged: 2016-08-26 15:27:50 UTC; snapadmin

Packaged: 2018-08-10 15:22:16 UTC; tylerhunt

Description: Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.

Packaged: 2018-11-03 16:37:41 UTC; tylerhunt

person("Badri", "N Pattanaik", email = "pattanaik.badri@gmail.com", role = c("aut")),
person("Krishna Harsha", "K H", email = "khkrishnaharsha123@gmail.com", role = "cre"))
Description: Provides two important functions for producing Gain chart and Lift chart for any classification model.
Maintainer: Krishna Harsha K H <khkrishnaharsha123@gmail.com>

Title: Classes and Methods for Training and Using Binary Prediction
Depends: R (>= 2.10), ClassDiscovery, ClassComparison, oompaBase
Imports: methods, stats, class, rpart, TailRank, e1071, randomForest,
Description: Defines classes and methods to learn models and use them
to predict binary outcomes.  These are generic tools, but we also
include specific examples for many common classifiers.

Packaged: 2019-05-06 16:54:44 UTC; Kevin Coombes

Title: Armadillo Client for the Armadillo Service
Description: A set of functions to be able to manage data shared on a
'MOLGENIS Armadillo' storage server ('MinIO').
Imports: rlang, httr, urltools, xml2, aws.iam, aws.s3, MolgenisAuth,
Suggests: knitr, testthat, mockery, datasets, tibble, rmarkdown,