Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 1989 packages in 0.41 seconds

ipADMIXTURE — by Chainarong Amornbunchornvej, 5 years ago

Iterative Pruning Population Admixture Inference Framework

A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) .

nloptr — by Aymeric Stamm, 5 months ago

R Interface to NLopt

Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See < https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more information on the available algorithms. Building from included sources requires 'CMake'. On Linux and 'macOS', if a suitable system build of NLopt (2.7.0 or later) is found, it is used; otherwise, it is built from included sources via 'CMake'. On Windows, NLopt is obtained through 'rwinlib' for 'R <= 4.1.x' or grabbed from the appropriate toolchain for 'R >= 4.2.0'.

DiagrammeR — by Richard Iannone, 10 months ago

Graph/Network Visualization

Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.

metagear — by Marc J. Lajeunesse, 4 years ago

Comprehensive Research Synthesis Tools for Systematic Reviews and Meta-Analysis

Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 .

mvtnorm — by Torsten Hothorn, 18 days ago

Multivariate Normal and t Distributions

Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package.

tidyverse — by Hadley Wickham, 2 years ago

Easily Install and Load the 'Tidyverse'

The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step. Learn more about the 'tidyverse' at < https://www.tidyverse.org>.

glmnet — by Trevor Hastie, a year ago

Lasso and Elastic-Net Regularized Generalized Linear Models

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see and . There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.

countrycode — by Vincent Arel-Bundock, 8 months ago

Convert Country Names and Country Codes

Standardize country names, convert them into one of 40 different coding schemes, convert between coding schemes, and assign region descriptors.

mitools — by Thomas Lumley, 6 years ago

Tools for Multiple Imputation of Missing Data

Tools to perform analyses and combine results from multiple-imputation datasets.

ca — by Oleg Nenadic, 5 years ago

Simple, Multiple and Joint Correspondence Analysis

Computation and visualization of simple, multiple and joint correspondence analysis.