This package provides a flexible approach to Bayesian optimization / model based optimization building on the bbotk package. The mlr3mbo is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using mlr3mbo for hyperparameter optimization of machine learning models within the mlr3 ecosystem is straightforward via mlr3tuning.

This package implements affinity propagation clustering introduced by Frey and Dueck (2007). The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results.

Dichromat collapses red-green or green-blue distinctions to simulate the effects of different types of color-blindness.

This package contains a set of functions that extend the cancor function. These functions provide new numerical and graphical outputs. It also includes a regularized extension of the canonical correlation analysis to deal with datasets with more variables than observations.

Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, and specialized tools for downstream analyses. grandR provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.

This is a package for developers to check user-supplied function arguments. It is designed to be simple, fast and customizable. Error messages follow the tidyverse style guide.

This package lets you generate random colors, possibly with a given hue or a given luminosity.

This package provides an interface to Amazon Web Services application integration services, including Simple Queue Service (SQS) message queue, Simple Notification Service (SNS) publish/subscribe messaging, and more.

Aster models (Geyer, Wagenius, and Shaw, 2007, <doi:10.1093/biomet/asm030>; Shaw, Geyer, Wagenius, Hangelbroek, and Etterson, 2008, <doi:10.1086/588063>; Geyer, Ridley, Latta, Etterson, and Shaw, 2013, <doi:10.1214/13-AOAS653>) are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e.2g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, life table analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e.g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e.g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). There are also random effects versions of these models.

This package provides functions for reading ontologies into R as lists and manipulating sets of ontological terms.

This package provides a fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.

This package provides a Wrapper around the SVDLIBC library for (truncated) singular value decomposition of a sparse matrix. Currently, only sparse real matrices in Matrix package format are supported.

R-tgb provides Bayesian nonstationary regression and treed Gaussian processes. In addition, it provides visualization functions, tree drawing, sensitivity analysis, multi-resolution models, and sequential experimental design tools, including ALM, ALC, and expected improvement for optimizing noisy black-box functions.

Function-oriented Make-like declarative pipelines for statistics and data science are supported in the targets R package. As an extension to targets, the tarchetypes package provides convenient user-side functions to make targets easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the drake R package by Will Landau (2018) <doi:10.21105/joss.00550>.

Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. The package provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of ggplot2 and tidy data.

This package implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.

This package lets you record test suite HTTP requests and replay them during future runs. It works by hooking into the webmockr R package for matching HTTP requests by various rules, and then caching real HTTP responses on disk in cassettes. Subsequent HTTP requests matching any previous requests in the same cassette use a cached HTTP response.

This package provides mosaic plots for the ggplot2 framework. Mosaic plot functionality is provided in a single ggplot2 layer by calling the geom mosaic.

This package is for genomic regions processing using command line tools such as BEDTools, BEDOPS and Tabix. These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging. The bedr package's API enhances access to these tools as well as offers additional utilities for genomic regions processing.

This package provides a computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available.

mlr3tuning implements methods for hyperparameter tuning, e.g. Grid Search, Random Search, or Simulated Annealing. Various termination criteria can be set and combined. The class AutoTuner provides a convenient way to perform nested resampling in combination with mlr3.

This is a supplement to the maps package providing the larger and/or higher-resolution databases.

This package provides convenience functions for data preparation and modeling often used in analytical customer relationship management (aCRM).

This is software accompanying the book 'Applied Smoothing Techniques for Data Analysis---The Kernel Approach with S-Plus Illustrations', Oxford University Press. It provides smoothing methods for nonparametric regression and density estimation