Showing 8 of total 8 results (show query)
easystats
datawizard:Easy Data Wrangling and Statistical Transformations
A lightweight package to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. It is also the data wrangling backend for packages in 'easystats' ecosystem. References: Patil et al. (2022) <doi:10.21105/joss.04684>.
Maintained by Etienne Bacher. Last updated 3 days ago.
datadplyrhacktoberfestjanitormanipulationreshapetidyrwrangling
223 stars 14.77 score 436 scripts 120 dependents
r-lib
slider:Sliding Window Functions
Provides type-stable rolling window functions over any R data type. Cumulative and expanding windows are also supported. For more advanced usage, an index can be used as a secondary vector that defines how sliding windows are to be created.
Maintained by Davis Vaughan. Last updated 2 months ago.
302 stars 13.99 score 848 scripts 99 dependents
hneth
unikn:Graphical Elements of the University of Konstanz's Corporate Design
Define and use graphical elements of corporate design manuals in R. The 'unikn' package provides color functions (by defining dedicated colors and color palettes, and commands for finding, changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text decoration functions is based on the corporate design of the University of Konstanz <https://www.uni-konstanz.de/>, but can be adapted and extended for other purposes or institutions.
Maintained by Hansjoerg Neth. Last updated 4 months ago.
brandingcolorcolor-palettecolorschemecorporate-designpalettetext-decorationuniversity-colorsvisual-identity
39 stars 8.77 score 156 scripts 2 dependents
christophergandrud
DataCombine:Tools for Easily Combining and Cleaning Data Sets
Tools for combining and cleaning data sets, particularly with grouped and time series data. This includes functions for merging data while reporting duplicates, filling in columns with values of a column in another data frame, and creating continuous time data for interupted time series.
Maintained by Christopher Gandrud. Last updated 5 years ago.
55 stars 8.63 score 864 scripts 3 dependents
mt1022
cubar:Codon Usage Bias Analysis
A suite of functions for rapid and flexible analysis of codon usage bias. It provides in-depth analysis at the codon level, including relative synonymous codon usage (RSCU), tRNA weight calculations, machine learning predictions for optimal or preferred codons, and visualization of codon-anticodon pairing. Additionally, it can calculate various gene- specific codon indices such as codon adaptation index (CAI), effective number of codons (ENC), fraction of optimal codons (Fop), tRNA adaptation index (tAI), mean codon stabilization coefficients (CSCg), and GC contents (GC/GC3s/GC4d). It also supports both standard and non-standard genetic code tables found in NCBI, as well as custom genetic code tables.
Maintained by Hong Zhang. Last updated 4 months ago.
bioinformaticscodon-usagemachine-learningsequence-analysis
6 stars 5.82 score 8 scripts
eth-mds
ricu:Intensive Care Unit Data with R
Focused on (but not exclusive to) data sets hosted on PhysioNet (<https://physionet.org>), 'ricu' provides utilities for download, setup and access of intensive care unit (ICU) data sets. In addition to functions for running arbitrary queries against available data sets, a system for defining clinical concepts and encoding their representations in tabular ICU data is presented.
Maintained by Nicolas Bennett. Last updated 10 months ago.
39 stars 5.65 score 77 scripts
cran
SLIDE:Single Cell Linkage by Distance Estimation is SLIDE
This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.
Maintained by Arjun Panda. Last updated 7 years ago.
3.00 score
djpedregal
UComp:Automatic Univariate Time Series Modelling of many Kinds
Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) <doi:10.1017/CBO9781107049994>. Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>. Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>. Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>. Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>. Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.
Maintained by Diego J. Pedregal. Last updated 30 days ago.
1 stars 1.70 score 1 scripts