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

Found 110 packages in 0.03 seconds

FuncDiv — by Gavin Douglas, 3 years ago

Compute Contributional Diversity Metrics

Compute alpha and beta contributional diversity metrics, which is intended for linking taxonomic and functional microbiome data. See 'GitHub' repository for the tutorial: < https://github.com/gavinmdouglas/FuncDiv/wiki>. Citation: Gavin M. Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023) .

Microsoft365R — by Hong Ooi, 8 months ago

Interface to the 'Microsoft 365' Suite of Cloud Services

An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders.

DeSciDe — by Cameron Douglas, 2 months ago

Tool for Unbiased Literature Searching and Gene List Curation

Designed for genomic and proteomic data analysis, enabling unbiased PubMed searching, protein interaction network visualization, and comprehensive data summarization. This package aims to help users identify novel targets within their data sets based on protein network interactions and publication precedence of target's association with research context based on literature precedence. Methods in this package are described in detail in: Douglas (Year) . Key functionalities of this package also leverage methodologies from previous works, such as: - Szklarczyk et al. (2023) - Winter (2017) .

shiny.benchmark — by Douglas Azevedo, 3 years ago

Benchmark the Performance of 'shiny' Applications

Compare performance between different versions of a 'shiny' application based on 'git' references.

MVQuickGraphs — by Douglas Whitaker, 6 years ago

Quick Multivariate Graphs

Functions used for graphing in multivariate contexts. These functions are designed to support produce reasonable graphs with minimal input of graphing parameters. The motivation for these functions was to support students learning multivariate concepts and R - there may be other functions and packages better-suited to practical data analysis. For details about the ellipse methods see Johnson and Wichern (2007, ISBN:9780131877153).

marg — by Alessandra R. Brazzale, 5 months ago

Approximate Marginal Inference for Regression-Scale Models

Implements likelihood inference based on higher order approximations for linear nonnormal regression models.

rmdshower — by Doug Ashton, 8 years ago

'R' 'Markdown' Format for 'shower' Presentations

'R' 'Markdown' format for 'shower' presentations, see < https://github.com/shower/shower>.

SIPDIBGE — by Gabriel Assuncao, 2 years ago

Collection of Household Survey Packages Conducted by IBGE

Provides access to packages developed for downloading, reading and analyzing microdata from household surveys in Integrated System of Household Surveys - SIPD conducted by Brazilian Institute of Geography and Statistics - IBGE. More information can be obtained from the official website < https://www.ibge.gov.br/>.

SpoMAG — by Douglas Terra Machado, 5 months ago

Probability of Sporulation Potential in MAGs

Implements an ensemble machine learning approach to predict the sporulation potential of metagenome-assembled genomes (MAGs) from uncultivated Firmicutes based on the presence/absence of sporulation-associated genes.

parameters — by Daniel Lüdecke, 2 months ago

Processing of Model Parameters

Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).