This package implements an R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory).

This package helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. The package is developed, tested and used at the Functional Genomics Center Zurich, where it is used mainly for prototyping, teaching, and having fun with proteomics data. But it can also be used to do data analysis for small scale data sets.

This package provides functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by Lee et al. (2013) <doi:10.1016/j.ajhg.2013.05.010>. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.

This package provides a command line parser to be used with Rscript to write shebang scripts that gracefully accept positional and optional arguments and automatically generate usage notices.

This package offers methods for estimating statistical changes in time series. These are used for identifying nearby critical transitions.

This package provides functions related to L-moments: computation of L-moments and trimmed L-moments of distributions and data samples; parameter estimation; L-moment ratio diagram; plot vs. quantiles of an extreme-value distribution.

The TOML configuration format specifies an excellent format suitable for both human editing as well as the common uses of a machine-readable format. This package provides Rcpp bindings to a TOML parser.

Thisp package enables you to track and report code coverage for your package and (optionally) upload the results to a coverage service. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code.

This package provides an implementation of both the exact and approximation methods for computing the cumulative distribution function (CDF) of the Poisson binomial distribution. It also provides the probability mass function (PMF), quantile function, and random number generation for the Poisson binomial distribution.

This package provides an R interface to the libgit2 library, which is a pure C implementation of the Git core methods.

This package provides a collection of tools to evaluate probability density functions, cumulative distribution functions, quantile functions and random numbers for truncated random variables. These functions are provided to also compute the expected value and variance. Q-Q plots can be produced. All the probability functions in the stats, stats4 and evd packages are automatically available for truncation.

This package is an implementation of a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data. In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. This package single-cell analysis via expression recovery (SAVER) implements an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes.

This package provides a comprehensive toolbox for analysing Spatial Point Patterns. It is focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. It also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. It supports spatial covariate data such as pixel images and contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.

This package provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). This package supports RDF by implementing an R interface to the Redland RDF C library. In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes.

This package is a flexible and comprehensive R toolbox for model-based optimization. It implements Efficient Global Optimization Algorithm for single- and multi-objective optimization. It supports mixed parameters. The machine learning toolbox mlr offers regression learners. It provides various infill criteria and features batch proposal, parallel execution, visualization, and logging. Its modular implementation allows easy customization by the user.

This package provides tools for generating random assignments for common experimental designs and random samples for common sampling designs.

This package estimates optimal cutpoints for binary classification metrics. It also validates performance using bootstrapping. Some methods for more robust cutpoint estimation are supported, e.g. a parametric method assuming normal distributions, bootstrapped cutpoints, and smoothing of the metric values per cutpoint using Generalized Additive Models. Various plotting functions are included.

This package provides functions to impute using random forest. It operates under full conditional specifications (multivariate imputation by chained equations).

Inspired by the the futile.logger R package and logging Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.

Plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics.

This package provides a collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.

This package provides a smooth mapping of multidimensional points into low-dimensional space defined by a self-organizing map. It is designed to work with FlowSOM and flow-cytometry use-cases.

This package provides wrappers on regexpr and gregexpr to return the match results in tidy data frames.

This package provides building blocks for the design and analysis of multiobjective optimization algorithms.