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r-gregmisc

gdata:Various R Programming Tools for Data Manipulation

Various R programming tools for data manipulation, including medical unit conversions, combining objects, character vector operations, factor manipulation, obtaining information about R objects, generating fixed-width format files, extracting components of date & time objects, operations on columns of data frames, matrix operations, operations on vectors, operations on data frames, value of last evaluated expression, and a resample() wrapper for sample() that ensures consistent behavior for both scalar and vector arguments.

Maintained by Arni Magnusson. Last updated 3 months ago.

9 stars 13.62 score 4.5k scripts 124 dependents

bioc

ShortRead:FASTQ input and manipulation

This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

dataimportsequencingqualitycontrolbioconductor-packagecore-packagezlibcpp

8 stars 12.08 score 1.8k scripts 49 dependents

laresbernardo

lares:Analytics & Machine Learning Sidekick

Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.

Maintained by Bernardo Lares. Last updated 1 months ago.

analyticsapiautomationautomldata-sciencedescriptive-statisticsh2omachine-learningmarketingmmmpredictive-modelingpuzzlerlanguagerobynvisualization

233 stars 9.92 score 185 scripts 1 dependents

andyliaw-mrk

locfit:Local Regression, Likelihood and Density Estimation

Local regression, likelihood and density estimation methods as described in the 1999 book by Loader.

Maintained by Andy Liaw. Last updated 25 days ago.

1 stars 9.40 score 428 scripts 606 dependents

jaredlander

useful:A Collection of Handy, Useful Functions

A set of little functions that have been found useful to do little odds and ends such as plotting the results of K-means clustering, substituting special text characters, viewing parts of a data.frame, constructing formulas from text and building design and response matrices.

Maintained by Jared P. Lander. Last updated 1 years ago.

8 stars 7.75 score 748 scripts 6 dependents

kwb-r

kwb.utils:General Utility Functions Developed at KWB

This package contains some small helper functions that aim at improving the quality of code developed at Kompetenzzentrum Wasser gGmbH (KWB).

Maintained by Hauke Sonnenberg. Last updated 1 years ago.

8 stars 7.33 score 12 scripts 78 dependents

predictiveecology

NetLogoR:Build and Run Spatially Explicit Agent-Based Models

Build and run spatially explicit agent-based models using only the R platform. 'NetLogoR' follows the same framework as the 'NetLogo' software (Wilensky (1999) <http://ccl.northwestern.edu/netlogo/>) and is a translation in R of the structure and functions of 'NetLogo'. 'NetLogoR' provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed 'NetLogo' framework, coupled with the versatility, power and massive resources of the R software. Examples of two models from the NetLogo software repository (Ants <http://ccl.northwestern.edu/netlogo/models/Ants>) and Wolf-Sheep-Predation (<http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation>), and a third, Butterfly, from Railsback and Grimm (2012) <https://www.railsback-grimm-abm-book.com/>, all written using 'NetLogoR' are available. The 'NetLogo' code of the original version of these models is provided alongside. A programming guide inspired from the 'NetLogo' Programming Guide (<https://ccl.northwestern.edu/netlogo/docs/programming.html>) and a dictionary of 'NetLogo' primitives (<https://ccl.northwestern.edu/netlogo/docs/dictionary.html>) equivalences are also available. NOTE: To increment 'time', these functions can use a for loop or can be integrated with a discrete event simulator, such as 'SpaDES' (<https://cran.r-project.org/package=SpaDES>). The suggested package 'fastshp' can be installed with 'install.packages("fastshp", repos = ("<https://rforge.net>"), type = "source")'.

Maintained by Eliot J B McIntire. Last updated 4 months ago.

40 stars 6.96 score 19 scripts

markheckmann

OpenRepGrid:Tools to Analyze Repertory Grid Data

Analyze repertory grids, a qualitative-quantitative data collection technique devised by George A. Kelly in the 1950s. Today, grids are used across various domains ranging from clinical psychology to marketing. The package contains functions to quantitatively analyze and visualize repertory grid data (e.g. 'Fransella', 'Bell', & 'Bannister', 2004, ISBN: 978-0-470-09080-0). The package is part of the The package is part of the <https://openrepgrid.org/> project.

Maintained by Mark Heckmann. Last updated 28 days ago.

repertory-gridrepgrid

19 stars 6.69 score 156 scripts

usepa

micromap:Linked Micromap Plots

This group of functions simplifies the creation of linked micromap plots. Please see <https://www.jstatsoft.org/v63/i02/> for additional details.

Maintained by Marcus W. Beck. Last updated 5 months ago.

ord

10 stars 6.20 score 53 scripts

oliver-wyman-actuarial

easyr:Helpful Functions from Oliver Wyman Actuarial Consulting

Makes difficult operations easy. Includes these types of functions: shorthand, type conversion, data wrangling, and work flow. Also includes some helpful data objects: NA strings, U.S. state list, color blind charting colors. Built and shared by Oliver Wyman Actuarial Consulting. Accepting proposed contributions through GitHub.

Maintained by Bryce Chamberlain. Last updated 1 years ago.

20 stars 4.86 score 18 scripts

kleinomicslab

mrbin:Metabolomics Data Analysis Functions

A collection of functions for processing and analyzing metabolite data. The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models.

Maintained by Matthias Klein. Last updated 18 days ago.

artificial-neural-networksfeature-extractionmetabolomicsnmr

2 stars 4.00 score 4 scripts

langenbergstefan

sound:A Sound Interface for R

Basic functions for dealing with wav files and sound samples.

Maintained by Stefan Langenberg. Last updated 1 years ago.

3.79 score 124 scripts

cran

tmcn:A Text Mining Toolkit for Chinese

A Text mining toolkit for Chinese, which includes facilities for Chinese string processing, Chinese NLP supporting, encoding detecting and converting. Moreover, it provides some functions to support 'tm' package in Chinese.

Maintained by Jian Li. Last updated 6 years ago.

1 stars 2.38 score 5 dependents

matt-dray

choosethis:Demos of 'cli' Clickable Links

Two sketches: play a text adventure in the console by following the prompts, and perform a data summary using a clickabel interface. Options are presented to the user as clickable links in supported terminals, such as RStudio's console.

Maintained by Matt Dray. Last updated 2 years ago.

cli

2 stars 2.00 score

jmcurran

multimix:Fit Mixture Models Using the Expectation Maximisation (EM) Algorithm

A set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1--22) to take a finite mixture model approach to clustering. The package is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. The method is described in Hunt, L. and Jorgensen, M. (1999) <doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) <doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429--440.

Maintained by James Curran. Last updated 2 years ago.

cpp

2.00 score

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