Amyloid Propensity Prediction Neural Network
Carlos Família, Sarah R. Dennison, Alexandre Quintas, David A. Phoenix
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.

Approximate and Exact Optimal Transport Methods
R and C++ functions to perform exact and
approximate optimal transport. All C++ methods are linkable
to other R packages via their header files.

Bayesian prediction of complex computer codes
Performs Bayesian prediction of complex computer codes
when fast approximations are available: M. C. Kennedy and A.
O'Hagan 2000, Biometrika 87(1):1-13

Performs Bayesian prediction of complex computer codes
when fast approximations are available: M. C. Kennedy and A. O'Hagan
2000, Biometrika 87(1):1-13

Bayesian Prediction of Complex Computer Codes
Performs Bayesian prediction of complex computer codes when fast approximations are available.  It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O'Hagan (2000), Biometrika 87(1):1.

Approximately Optimal Fine Balance Matching with Multiple Groups
Tools for constructing a matched design with multiple comparison groups.
Further specifications of refined covariate balance restriction and exact match on
covariate can be imposed. Matches are approximately optimal in  the sense that the
cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992)
<doi:10.1016/0377-2217(92)90078-N>.

Tools for constructing a matched design with multiple comparison groups.
Further specifications of refined covariate balance restriction and exact match on
covariate can be imposed. Matches are approximately optimal in  the sense that the
cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992)
<doi:10.1016/0377-2217(92)90078-N>, Karmakar, Small and Rosenbaum (2019)
<doi:10.1080/10618600.2019.1584900>.