This package includes HTML functions and methods to write in an HTML file. Thus, making HTML reports is easy. It includes a function that allows redirection on the fly, which appears to be very useful for teaching purposes, as the student can keep a copy of the produced output to keep all that they did during the course. The package comes with a vignette describing how to write HTML reports for statistical analysis. Finally, a driver for Sweave parses HTML flat files containing R code and to automatically write the corresponding outputs (tables and graphs).

This package provides an Rstudio add-in that delivers a graphical interface for editing ggplot2 theme elements.

This package provides a collection of ggplot2 color palettes inspired by plots in scientific journals, data visualization libraries, science fiction movies, and TV shows.

This package provides functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, and more.

This package provides helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking.

This package provides analytic derivatives and information matrices for fitted linear mixed effects (lme) models and generalized least squares (gls) models estimated using lme() (from package nlme) and gls() (from package nlme), respectively. The package includes functions for estimating the sampling variance-covariance of variance component parameters using the inverse Fisher information. The variance components include the parameters of the random effects structure (for lme models), the variance structure, and the correlation structure. The expected and average forms of the Fisher information matrix are used in the calculations, and models estimated by full maximum likelihood or restricted maximum likelihood are supported. The package also includes a function for estimating standardized mean difference effect sizes based on fitted lme or gls models.

This package provides an implementation of multilayered visualizations for enhanced graphical representation of functional analysis data. It combines and integrates omics data derived from expression and functional annotation enrichment analyses. Its plotting functions have been developed with an hierarchical structure in mind: starting from a general overview to identify the most enriched categories (modified bar plot, bubble plot) to a more detailed one displaying different types of relevant information for the molecules in a given set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).

This package provides tools for the analysis of complex survey samples. The provided features include: summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples; variances by Taylor series linearisation or replicate weights; post-stratification, calibration, and raking; two-phase subsampling designs; graphics; PPS sampling without replacement; principal components, and factor analysis.

Tree based algorithms can be improved by introducing boosting frameworks. LightGBM is one such framework, based on Ke, Guolin et al. (2017). This package offers an R interface to work with it. It is designed to be distributed and efficient with the following goals:

1. Faster training speed and higher efficiency;

2. lower memory usage;

3. better accuracy;

4. parallel learning supported; and

5. capable of handling large-scale data.

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 to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The lubridate package has a consistent and memorable syntax that makes working with dates easy and fun.

This is package for regression modeling using rules with added instance-based corrections.

The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.

Written in C++ using Rcpp, this package provides a collection of metrics for evaluating models.

This package contains data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively.

This package provides a variety of simple fish stock assessment methods.

This package provides efficient routines for manipulation of date-time objects while accounting for time-zones and daylight saving times. The package includes utilities for updating of date-time components (year, month, day etc.), modification of time-zones, rounding of date-times, period addition and subtraction etc. Parts of the CCTZ source code, released under the Apache 2.0 License, are included in this package.

This package provides a collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree.

This package provides functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.

This package provides basic I/O tools for streaming and data parsing.

This package provides a set of predicates and assertions for checking the types of variables. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.

This package provides tools to query and print information about the current R session. It is similar to utils::sessionInfo(), but includes more information about packages, and where they were installed from.

This package computes Hartigan's dip test statistic for unimodality, multimodality and provides a test with simulation based p-values, where the original public code has been corrected.