Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.
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GET /api/packages?search=hello&page=1&limit=20
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This package provides utilities to help set and record the setting of the seed and the uniform and normal generators used when a random experiment is run. The utilities can be used in other functions that do random experiments to simplify recording and/or setting all the necessary information for reproducibility. See the vignette and reference manual for examples.
This package provides an R interface to the libgit2 library, which is a pure C implementation of the Git core methods.
This package implements multiple imputation for multivariate panel or clustered data.
This package lets you determine the significance of pre-defined sets of genes with respect to an outcome variable, such as a group indicator, a quantitative variable or a survival time.
This package provides some very simple method functions for confidence interval calculation and to distill pertinent information from a potentially complex object; primarily used in common with the packages extRemes and SpatialVx.
This package provides more controls on the option values such as validation and filtering on the values, making options invisible or private.
This R package provides functions to create formattable vectors and data frames. Formattable vectors are printed with text formatting, and formattable data frames are printed with multiple types of formatting in HTML to improve the readability of data presented in tabular form rendered in web pages.
This package provides a system for organizing column names in data. It is aimed at supporting a prefix-based and suffix-based column naming scheme. It extends dplyr functionality to add ordering by function and more explicit renaming.
This package provides tools for exploratory data analysis and data visualization of biological sequence (DNA and protein) data. It also includes utilities for sequence data management under the ACNUC system.
Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors.
The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.
This package provides a ggplot2 extension for easy plotting of half-half geom combinations. Think half boxplot and half jitterplot, or half violinplot and half dotplot.
Bindings to tesseract: an optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.
This package provides a fast and user-friendly implementation of nonparametric estimators for censored event history (survival) analysis with the Kaplan-Meier and Aalen-Johansen methods.
This package provides a cross-platform Perl-based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX) files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains or can be specified by the user.
This package implements tools for manipulation of digital images and the Propagation Separation approach by Polzehl and Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1> for smoothing digital images, see Polzehl and Tabelow (2007) <DOI:10.18637/jss.v019.i01>.
This package provides various themes, palettes, and other functions that are used to customise ggplots to look like they were made in GraphPad Prism. The Prism-look is achieved with theme_prism() and scale_fill|colour_prism(), axes can be changed with custom guides like guide_prism_minor(), and significance indicators added with add_pvalue().
This package provides ggplot2 geoms filled with various patterns. It includes a patterned version of every ggplot2 geom that has a region that can be filled with a pattern. It provides a suite of ggplot2 aesthetics and scales for controlling pattern appearances. It supports over a dozen builtin patterns (every pattern implemented by gridpattern) as well as allowing custom user-defined patterns.
This package provides a unified parallelization framework for multiple backends. This package is designed for internal package and interactive usage. The main operation is parallel mapping over lists. It supports local, multicore, mpi and BatchJobs mode. It allows tagging of the parallel operation with a level name that can be later selected by the user to switch on parallel execution for exactly this operation.
This package allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model. Flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. Afpt can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions.
This package includes functions for processing GeoJson objects relying on RFC 7946. The geojson encoding is based on json11, a tiny JSON library for C++11. Furthermore, the source code is exported in R through the Rcpp and RcppArmadillo packages.
This package implements data manipulation methods such as cast, aggregate, and merge/join for Matrix and Matrix-like objects.
UpSet plots are an improvement over Venn Diagram for set overlap visualizations. Striving to bring the best of the UpSetR and ggplot2, this package offers a way to create complex overlap visualisations, using simple and familiar tools.
This package provides a differential evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that
do not sacrifice simplicity of design,
are essentially tuning-free, and
can be efficiently implemented directly in the R language.