Lightning Fast Serialization of Data Frames

Multithreaded serialization of compressed data frames using the 'fst' format. The 'fst' format allows for full random access of stored data and a wide range of compression settings using the LZ4 and ZSTD compressors.

Classes for Relational Data

Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.

Database Interface and 'MySQL' Driver for R

Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code ported from S-PLUS. A modern 'MySQL' client written in 'C++' is available from the 'RMariaDB' package.

Manipulation of Linear Systems of (in)Equalities

Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities.

Various R Programming Tools for Data Manipulation

Various R programming tools for data manipulation, including medical unit conversions, combining objects, character vector operations, factor manipulation, obtaining information about R objects, generating fixed-width format files, extracting components of date & time objects, operations on columns of data frames, matrix operations, operations on vectors, operations on data frames, value of last evaluated expression, and a resample() wrapper for sample() that ensures consistent behavior for both scalar and vector arguments.

Reproduce Statistical Analyses and Meta-Analyses

Includes data analysis and meta-analysis functions (e.g., to calculate effect sizes and 95% Confidence Intervals (CI) on Standardised Effect Sizes (d) for AB/BA cross-over repeated-measures experimental designs), data presentation functions (e.g., density curve overlaid on histogram),and the data sets analyzed in different research papers in software engineering (e.g., related to software defect prediction or multi- site experiment concerning the extent to which structured abstracts were clearer and more complete than conventional abstracts) to streamline reproducible research in software engineering.

Combine Multidimensional Arrays

Combine multidimensional arrays into a single array. This is a generalization of 'cbind' and 'rbind'. Works with vectors, matrices, and higher-dimensional arrays (aka tensors). Also provides functions 'adrop', 'asub', and 'afill' for manipulating, extracting and replacing data in arrays.

Data Analysis Using Regression and Multilevel/Hierarchical Models

Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

Manipulate and Visualize VCF Data

Facilitates easy manipulation of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices of data. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file (*.vcf.gz). It also may be converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and familiar R software.

Analyses of Phylogenetics and Evolution

Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.