This package provides methods that simplify the setup of S3 generic functions and S3 methods. Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers. For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible. The method setMethodS3() is a good start for those who in the future may want to migrate to S4.

This package provides a set of functions used to automate commonly used methods in regression analysis. This includes plotting interactions, and calculating simple slopes, standardized coefficients, regions of significance (Johnson & Neyman, 1936; cf. Spiller et al., 2012), etc.

This package provides an implementation of the Ensemble Slice Sampling method. Features:

• fast & Robust Bayesian Inference

• efficient Markov Chain Monte Carlo (MCMC)

• black-box inference, no hand-tuning

• excellent performance in terms of autocorrelation time and convergence rate

• scale to multiple CPUs without any extra effort

• automated Convergence diagnostics

This package provides an integration of Eigen in R using a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems.

This package provides a library for Probabilistic Graphical Models. It can be used for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

This package provides a tbl_df class that offers better checking and printing capabilities than traditional data frames.

ROCR is a flexible tool for creating cutoff-parameterized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parameterization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism.

This package implements a Dynamic Nested Sampling for computing Bayesian posteriors and evidences.

This package embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine (version 3.8.8.2) is included.

The package allows one to compose general HTTP requests and provides convenient functions to fetch URIs, GET and POST forms, etc. and process the results returned by the Web server. This provides a great deal of control over the HTTP/FTP/... connection and the form of the request while providing a higher-level interface than is available just using R socket connections. Additionally, the underlying implementation is robust and extensive, supporting FTP/FTPS/TFTP (uploads and downloads), SSL/HTTPS, telnet, dict, ldap, and also supports cookies, redirects, authentication, etc.

This package provides a pure R implementation of the t-SNE algorithm.

This package provides a collection of (mostly simple) functions for generating and manipulating colors in R.

Chaospy is a numerical toolbox for performing uncertainty quantification using polynomial chaos expansions, advanced Monte Carlo methods implemented in Python. It also include a full suite of tools for doing low-discrepancy sampling, quadrature creation, polynomial manipulations, and a lot more.

The R6 package allows the creation of classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. These classes allow public and private members, and they support inheritance, even when the classes are defined in different packages.

rchitect provides access to R functionality from Python. Its main use is as the driver for radian, the R console.

This package displays a progress bar in the R console for long running computations taking place in C++ code, and support for interrupting those computations even in multithreaded code, typically using OpenMP.

This package provides the Breiman and Cutler's random forests algorithm, based on a forest of trees using random inputs, for classification and regression.

This package provides simple utility functions that are shared across several packages maintained by the Tanay lab.

Mixedpower uses pilotdata and a linear mixed model fitted with lme4 to simulate new data sets. Power is computed separate for every effect in the model output as the relation of significant simulations to all simulations. More conservative simulations as a protection against a bias in the pilotdata are available as well as methods for plotting the results.

MAPIE allows you to easily estimate prediction intervals (or prediction sets) using your favourite scikit-learn-compatible model for single-output regression or multi-class classification settings.

Prediction intervals output by MAPIE encompass both aleatoric and epistemic uncertainties and are backed by strong theoretical guarantees thanks to conformal prediction methods intervals.

This package provides utility functions useful when programming and developing R packages.

This package provides a toolbox for working with base types, core R features like the condition system, and core Tidyverse features like tidy evaluation.

This package is a model building aid for nonlinear mixed-effects (population) model analysis using NONMEM, facilitating data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison. The methods are described in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) <doi:10.1016/s0169-2607(98)00067-4>.

This package provides R functions implementing a standard unit testing framework, with additional code inspection and report generation tools.