RGBDS (Rednex Game Boy Development System) is an assembler/linker package for the Game Boy and Game Boy Color. It consists of:

• rgbasm (assembler)

• rgblink (linker)

• rgbfix (checksum/header fixer)

• rgbgfx (PNG-to-Game Boy graphics converter)

RGBDS (Rednex Game Boy Development System) is an assembler/linker package for the Game Boy and Game Boy Color. It consists of:

• rgbasm (assembler)

• rgblink (linker)

• rgbfix (checksum/header fixer)

• rgbgfx (PNG-to-Game Boy graphics converter)

RGBDS (Rednex Game Boy Development System) is an assembler/linker package for the Game Boy and Game Boy Color. It consists of:

• rgbasm (assembler)

• rgblink (linker)

• rgbfix (checksum/header fixer)

• rgbgfx (PNG-to-Game Boy graphics converter)

RGBDS (Rednex Game Boy Development System) is an assembler/linker package for the Game Boy and Game Boy Color. It consists of:

• rgbasm (assembler)

• rgblink (linker)

• rgbfix (checksum/header fixer)

• rgbgfx (PNG-to-Game Boy graphics converter)

RGBDS (Rednex Game Boy Development System) is an assembler/linker package for the Game Boy and Game Boy Color. It consists of:

• rgbasm (assembler)

• rgblink (linker)

• rgbfix (checksum/header fixer)

• rgbgfx (PNG-to-Game Boy graphics converter)

This package provides a collection of pre-optimized space-filling designs, for up to ten parameters, is contained here. Functions are provided to access designs described by Husslage et al (2011) and Wang and Fang (2005). The design types included are Audze-Eglais, MaxiMin, and uniform.

The mia package implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.

RSeQC provides a number of modules that can comprehensively evaluate high throughput sequence data, especially RNA-seq data. Some basic modules inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, etc.

This package provides basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.

This package offers a flexible, feature-rich yet light-weight logging framework based on R6 classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, JSON, (rotating) files, memory buffers, and databases, as well as email and push notifications.

This is a package for ratios of count data such as obtained from RNA-seq are modelled using Bayesian statistics to derive posteriors for effects sizes. This approach is described in Erhard & Zimmer (2015) <doi:10.1093/nar/gkv696> and Erhard (2018) <doi:10.1093/bioinformatics/bty471>.

RHash is a console utility for calculation and verification of magnet links and a wide range of hash sums like CRC32, MD4, MD5, SHA1, SHA256, SHA512, SHA3, AICH, ED2K, Tiger, DC++ TTH, BitTorrent BTIH, GOST R 34.11-94, RIPEMD-160, HAS-160, EDON-R, Whirlpool and Snefru.

This package provides bitmapped vectors of booleans (no NAs), coercion from and to logicals, integers and integer subscripts, fast boolean operators and fast summary statistics. With bit class vectors of true binary booleans, TRUE and FALSE can be stored with 1 bit only.

This package contains a list of functional time series, sliced functional time series, and functional data sets. Functional time series is a special type of functional data observed over time. Sliced functional time series is a special type of functional time series with a time variable observed over time.

This package contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.

This package performs augmented backward elimination and checks the stability of the obtained model. Augmented backward elimination combines significance or information based criteria with the change in estimate to either select the optimal model for prediction purposes or to serve as a tool to obtain a practically sound, highly interpretable model.

The data consist of microarrays from 128 different individuals with acute lymphoblastic leukemia (ALL). A number of additional covariates are available. The data have been normalized (using rma) and it is the jointly normalized data that are available here. The data are presented in the form of an exprSet object.

This package provides a comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on.

High dimensional interaction search by brute force requires a quadratic computational cost in the number of variables. The xyz algorithm provably finds strong interactions in almost linear time. For details of the algorithm see: G. Thanei, N. Meinshausen and R. Shah (2016). The xyz algorithm for fast interaction search in high-dimensional data.

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This package provides miscellaneous functions to help customize ggplot2 objects. High-level functions are provided to post-process ggplot2 layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom geom, and helper functions to enforce symmetric scales or add tags to facetted plots.

This package provides a suite of functions to help ease the use of the d3.js visualization library in R. These helpers include htmltools::htmlDependency functions, hierarchy builders, and conversion tools for partykit, igraph, table, and data.frame R objects into the JSON format that the d3.js library expects.

This package is an implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. It includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

This package implements various procedures for finding multiple change-points. Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change-points as well as other summary information.