Computation of the Apollonius diagram of given 2D points and
    its dual the Apollonius graph, also known as the additively weighted
    Voronoï diagram, and which is a generalization of the classical Voronoï 
    diagram. For references, see the bibliography in the CGAL documentation 
    at <https://doc.cgal.org/latest/Apollonius_graph_2/citelist.html>.

Programmatic interface to the NASA Application for Extracting and Exploring Analysis Ready Samples services (AppEEARS; <https://appeears.earthdatacloud.nasa.gov/>).
 The package provides easy access to analysis ready earth observation data in R.

Calculating predictive model performance measures adjusted for predictor distributions using density ratio method (Sugiyama et al., (2012, ISBN:9781139035613)). L1 and L2 error for continuous outcome and C-statistics for binomial outcome are computed.

Collect  your data on digital marketing campaigns from Apple Search Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

A modeling package compiling applicability domain methods in
    R. It combines different methods to measure the amount of
    extrapolation new samples can have from the training set. See Gadaleta
    et al (2016) <doi:10.4018/IJQSPR.2016010102> for an overview of
    applicability domains.

A few functions and several data set for the Springer book 'Applied Predictive Modeling'.

Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.

Convert populations into integer number of seats for legislative 
    bodies. Implements apportionment methods used historically and currently in the
    United States for reapportionment after the Census, as described in 
    <https://www.census.gov/history/www/reference/apportionment/methods_of_apportionment.html>.

With 'appRiori', users upload the research variables and the app guides them to the best set of comparisons fitting the hypotheses, for both main and interaction effects. Through a graphical explanation and empirical examples on reproducible data, it is shown that it is possible to understand both the logic behind the planned comparisons and the way to interpret them when a model is tested.