Threshing and Reaping for Principal Components (Thresher)
Defines the classes used to identify outliers (threshing) and compute the number of significant principal components and number of clusters (reaping) in a joint application of PCA and hierarchical clustering. See Wang et al., 2018, <doi:10.1186/s12859-017-1998-9>.

Scale Invariant Probabilistic Neural Networks (spnn)
Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.

Composable Preprocessing Operators and Pipelines for Machine Learning (mlrCPO)
Toolset that enriches ‘mlr’ with a diverse set of preprocessing operators. Composable Preprocessing Operators (‘CPO’s) are first-class R objects that can be applied to data.frames and ‘mlr’ ‘Task’s to modify data, can be attached to ‘mlr’ ‘Learner’s to add preprocessing to machine learning algorithms, and can be composed to form preprocessing pipelines.

An Implementation of the ‘DoubleClick for Publishers’ API (rdfp)
An implementation of Google’s ‘DoubleClick for Publishers’ (DFP) API <https://…/start>. This package is automatically compiled from the API WSDLs (Web Service Description Language) files to dictate how the API is structured. Theoretically, all API actions are possible using this package; however, care must be taken to format the inputs correctly and parse the outputs correctly as well. Please see Google’s DFP API reference and this package’s website <https://…/> for more information, documentation, and examples.

Abstractions for Promise-Based Asynchronous Programming (promises)
Provides fundamental abstractions for doing asynchronous programming in R using promises. Asynchronous programming is useful for allowing a single R process to orchestrate multiple tasks in the background while also attending to something else. Semantics are similar to ‘JavaScript’ promises, but with a syntax that is idiomatic R.