Showing 175 of total 175 results (show query)

hadley

reshape:Flexibly Reshape Data

Flexibly restructure and aggregate data using just two functions: melt and cast.

Maintained by Hadley Wickham. Last updated 3 years ago.

53.3 match 9.83 score 21k scripts 231 dependents

trinker

textshape:Tools for Reshaping Text

Tools that can be used to reshape and restructure text data.

Maintained by Tyler Rinker. Last updated 12 months ago.

data-reshapingmanipulationsentence-boundary-detectiontext-datatext-formatingtidy

13.4 match 50 stars 9.18 score 266 scripts 34 dependents

hadley

reshape2:Flexibly Reshape Data: A Reboot of the Reshape Package

Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast').

Maintained by Hadley Wickham. Last updated 4 years ago.

cpp

5.0 match 210 stars 17.19 score 94k scripts 2.0k dependents

cran

CornerstoneR:Collection of Scripts for Interface Between 'Cornerstone' and 'R'

Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'. . The desktop application 'Cornerstone' (<https://www.camline.com/en/products/cornerstone/cornerstone-core.html>) is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster. The engineers incorporate intensified hands-on statistics into their projects. They benefit from an intuitive and uniquely designed graphical Workmap concept: you design experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon, immediately, interactively, and without any programming. . While 'Cornerstone's' interface to the statistical programming language 'R' has been available since version 6.0, the latest interface with 'R' is even much more efficient. 'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the standard 'Cornerstone' GUI. Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R' routines, immediately and without the need to deal with programming code. Additionally, your 'R' programming team develops corresponding 'R' packages detached from 'Cornerstone' in their favorite 'R' environment. . Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel (<https://www.youtube.com/watch?v=HEQHwq_laXU>) (available in German).

Maintained by Gerrith Djaja. Last updated 5 years ago.

9.3 match 3.54 score

t-kalinowski

listarrays:A Toolbox for Working with R Arrays in a Functional Programming Style

A toolbox for R arrays. Flexibly split, bind, reshape, modify, subset and name arrays.

Maintained by Tomasz Kalinowski. Last updated 11 months ago.

6.2 match 15 stars 4.54 score 23 scripts

emf-creaf

meteoland:Landscape Meteorology Tools

Functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].

Maintained by Miquel De Cáceres. Last updated 2 months ago.

cpp

3.1 match 10 stars 7.95 score 92 scripts 2 dependents

henrikbengtsson

R.utils:Various Programming Utilities

Utility functions useful when programming and developing R packages.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

1.8 match 63 stars 13.74 score 5.7k scripts 814 dependents

john-d-fox

RcmdrMisc:R Commander Miscellaneous Functions

Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.

Maintained by John Fox. Last updated 1 years ago.

3.4 match 1 stars 7.00 score 432 scripts 42 dependents

jienagu

forestry:Reshape Data Tree

'forestry' a series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree.

Maintained by Jiena McLellan. Last updated 5 years ago.

3.9 match 21 stars 5.66 score 44 scripts

cran

matlab:'MATLAB' Emulation Package

Emulate 'MATLAB' code using 'R'.

Maintained by P. Roebuck. Last updated 9 months ago.

5.0 match 4.09 score 19 dependents

thomaschln

kgraph:Knowledge Graphs Constructions and Visualizations

Knowledge graphs enable to efficiently visualize and gain insights into large-scale data analysis results, as p-values from multiple studies or embedding data matrices. The usual workflow is a user providing a data frame of association studies results and specifying target nodes, e.g. phenotypes, to visualize. The knowledge graph then shows all the features which are significantly associated with the phenotype, with the edges being proportional to the association scores. As the user adds several target nodes and grouping information about the nodes such as biological pathways, the construction of such graphs soon becomes complex. The 'kgraph' package aims to enable users to easily build such knowledge graphs, and provides two main features: first, to enable building a knowledge graph based on a data frame of concepts relationships, be it p-values or cosine similarities; second, to enable determining an appropriate cut-off on cosine similarities from a complete embedding matrix, to enable the building of a knowledge graph directly from an embedding matrix. The 'kgraph' package provides several display, layout and cut-off options, and has already proven useful to researchers to enable them to visualize large sets of p-value associations with various phenotypes, and to quickly be able to visualize embedding results. Two example datasets are provided to demonstrate these behaviors, and several live 'shiny' applications are hosted by the CELEHS laboratory and Parse Health, as the KESER Mental Health application <https://keser-mental-health.parse-health.org/> based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.

Maintained by Thomas Charlon. Last updated 25 days ago.

3.5 match 4.85 score

john-d-fox

Rcmdr:R Commander

A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

Maintained by John Fox. Last updated 5 months ago.

1.8 match 4 stars 9.49 score 636 scripts 38 dependents

eltebioinformatics

mulea:Enrichment Analysis Using Multiple Ontologies and False Discovery Rate

Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Maintained by Tamas Stirling. Last updated 3 months ago.

annotationdifferentialexpressiongeneexpressiongenesetenrichmentgographandnetworkmultiplecomparisonpathwaysreactomesoftwaretranscriptionvisualizationenrichmentenrichment-analysisfunctional-enrichment-analysisgene-set-enrichmentontologiestranscriptomicscpp

2.3 match 28 stars 7.36 score 34 scripts

usaid-oha-si

gophr:Utility functions related to working with the MER Structured Dataset

This packages contains a number of functions for working with the PEPFAR MSD.

Maintained by Aaron Chafetz. Last updated 4 months ago.

2.3 match 1 stars 6.21 score 182 scripts 1 dependents

beckerbenj

eatGADS:Data Management of Large Hierarchical Data

Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases.

Maintained by Benjamin Becker. Last updated 23 days ago.

1.5 match 1 stars 7.36 score 34 scripts 1 dependents

fberding

aifeducation:Artificial Intelligence for Education

In social and educational settings, the use of Artificial Intelligence (AI) is a challenging task. Relevant data is often only available in handwritten forms, or the use of data is restricted by privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences, data is often unbalanced in terms of frequencies. To support educators as well as educational and social researchers in using the potentials of AI for their work, this package provides a unified interface for neural nets in 'PyTorch' to deal with natural language problems. In addition, the package ships with a shiny app, providing a graphical user interface. This allows the usage of AI for people without skills in writing python/R scripts. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al. (2020) <doi:10.48550/arXiv.2001.06001>) and imbalanced data via the creation of synthetic cases (e.g. Bunkhumpornpat et al. (2012) <doi:10.1007/s10489-011-0287-y>). Performance evaluation of AI is connected to measures from content analysis which educational and social researchers are generally more familiar with (e.g. Berding & Pargmann (2022) <doi:10.30819/5581>, Gwet (2014) <ISBN:978-0-9708062-8-4>, Krippendorff (2019) <doi:10.4135/9781071878781>). Estimation of energy consumption and CO2 emissions during model training is done with the 'python' library 'codecarbon'. Finally, all objects created with this package allow to share trained AI models with other people.

Maintained by Berding Florian. Last updated 1 months ago.

cpp

2.0 match 4.48 score 8 scripts

sccmckenzie

sift:Intelligently Peruse Data

Facilitate extraction of key information from common datasets.

Maintained by Scott McKenzie. Last updated 4 years ago.

cpp

1.9 match 4.30 score 4 scripts

jimlemon

prettyR:Pretty Descriptive Stats

Functions for conventionally formatting descriptive stats, reshaping data frames and formatting R output as HTML.

Maintained by Jim Lemon. Last updated 6 years ago.

2.3 match 3.13 score 207 scripts 1 dependents

scholaempirica

reschola:The Schola Empirica Package

A collection of utilies, themes and templates for data analysis at Schola Empirica.

Maintained by Jan Netík. Last updated 5 months ago.

1.3 match 4 stars 4.83 score 14 scripts

usaid-oha-si

Wavelength:Wavelength

USAID OHA Office. Munging of mission weekly HFR data.

Maintained by Aaron Chafetz. Last updated 2 years ago.

1.8 match 3 stars 3.39 score 55 scripts

schochastics

RFPNG:Very Fast PNG Image Reader/Writer for 24/32bpp Images

Wraps 'fpng', a very fast C++ .PNG image reader/writer for 24/32bpp images

Maintained by David Schoch. Last updated 1 years ago.

cpp

2.7 match 3 stars 2.18 score 2 scripts