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
    <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'.

Simple and transparent parsing of genotype/dosage data
    from an input Variant Call Format (VCF) file, matching of genotype
    coordinates to the component Single Nucleotide Polymorphisms (SNPs)
    of an existing polygenic score (PGS), and application of SNP weights
    to dosages for the calculation of a polygenic score for each individual
    in accordance with the additive weighted sum of dosages model. Methods
    are designed in reference to best practices described by
    Collister, Liu, and Clifton (2022) <doi:10.3389/fgene.2022.818574>.

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>.