R-universe search: topic:correlation

Showing 17 of total 17 results (show query)

easystats

effectsize:Indices of Effect Size

Provide utilities to work with indices of effect size for a wide variety of models and hypothesis tests (see list of supported models using the function 'insight::supported_models()'), allowing computation of and conversion between indices such as Cohen's d, r, odds, etc. References: Ben-Shachar et al. (2020) <doi:10.21105/joss.02815>.

Maintained by Mattan S. Ben-Shachar. Last updated 2 months ago.

anova cohens-d compute conversion correlation effect-size effectsize hacktoberfest hedges-g interpretation standardization standardized statistics

344 stars 16.38 score 1.8k scripts 29 dependents

easystats

correlation:Methods for Correlation Analysis

Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. Part of the 'easystats' ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.

Maintained by Brenton M. Wiernik. Last updated 28 days ago.

bayesian bayesian-correlations biserial cor correlation correlation-analysis correlations easystats gamma gaussian-graphical-models hacktoberfest matrix multilevel-correlations outliers partial partial-correlations regression robust spearman

439 stars 14.23 score 672 scripts 10 dependents

indrajeetpatil

statsExpressions:Tidy Dataframes and Expressions with Statistical Details

Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.

Maintained by Indrajeet Patil. Last updated 1 months ago.

bayesian-inference bayesian-statistics contingency-table correlation effectsize meta-analysis parametric robust robust-statistics statistical-details statistical-tests tidy

312 stars 10.92 score 146 scripts 2 dependents

neuropsychology

psycho:Efficient and Publishing-Oriented Workflow for Psychological Science

The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.

Maintained by Dominique Makowski. Last updated 4 years ago.

apa apa6 bayesian correlation format interpretation mixed-models neuroscience psycho psychology rstanarm statistics

149 stars 10.86 score 628 scripts 5 dependents

bioc

ANCOMBC:Microbiome differential abudance and correlation analyses with bias correction

ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators.

Maintained by Huang Lin. Last updated 15 days ago.

differentialexpression microbiome normalization sequencing software ancom ancombc ancombc2 correlation differential-abundance-analysis secom

120 stars 10.79 score 406 scripts 1 dependents

business-science

correlationfunnel:Speed Up Exploratory Data Analysis (EDA) with the Correlation Funnel

Speeds up exploratory data analysis (EDA) by providing a succinct workflow and interactive visualization tools for understanding which features have relationships to target (response). Uses binary correlation analysis to determine relationship. Default correlation method is the Pearson method. Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.

Maintained by Matt Dancho. Last updated 1 years ago.

correlation exploratory-analysis exploratory-data-analysis exploratory-data-visualizations tidyverse

137 stars 7.20 score 115 scripts

tanaylab

tgstat:Amos Tanay's Group High Performance Statistical Utilities

A collection of high performance utilities to compute distance, correlation, auto correlation, clustering and other tasks. Contains graph clustering algorithm described in "MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions" (Yael Baran, Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi, Elad Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay, 2019 <doi:10.1186/s13059-019-1812-2>).

Maintained by Aviezer Lifshitz. Last updated 6 months ago.

algorithms-implemented correlation knn statistics openblas cpp

8 stars 6.06 score 24 scripts 1 dependents

nelson-gon

manymodelr:Build and Tune Several Models

Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.

Maintained by Nelson Gonzabato. Last updated 12 days ago.

analysis-of-variance anova correlation correlation-coefficient generalized-linear-models gradient-boosting-decision-trees knn-classification linear-models linear-regression machine-learning missing-values models r-programming random-forest-algorithm regression-models

2 stars 5.78 score 50 scripts

fbertran

SelectBoost:A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets

An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

Maintained by Frederic Bertrand. Last updated 2 years ago.

confidence correlation correlation-structure modelling precision recall selection-algorithm

7 stars 5.61 score 13 scripts 1 dependents

okgreece

DescriptiveStats.OBeu:Descriptive Statistics 'OpenBudgets.eu'

Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface.

Maintained by Kleanthis Koupidis. Last updated 4 years ago.

boxplot correlation descriptive-statistics estimate frequencies obeu open-budgets openbudgets

1 stars 5.40 score 28 scripts 1 dependents

majianthu

copent:Estimating Copula Entropy and Transfer Entropy

The nonparametric methods for estimating copula entropy, transfer entropy, and the statistics for multivariate normality test and two-sample test are implemented. The methods for estimating transfer entropy and the statistics for multivariate normality test and two-sample test are based on the method for estimating copula entropy. The method for change point detection with copula entropy based two-sample test is also implemented. Please refer to Ma and Sun (2011) <doi:10.1016/S1007-0214(11)70008-6>, Ma (2019) <doi:10.48550/arXiv.1910.04375>, Ma (2022) <doi:10.48550/arXiv.2206.05956>, Ma (2023) <doi:10.48550/arXiv.2307.07247>, and Ma (2024) <doi:10.48550/arXiv.2403.07892> for more information.

Maintained by MA Jian. Last updated 10 months ago.

causal-discovery causality change-point-detection conditional-independence-test conditional-mutual-information copula copula-entropy correlation entropy granger-causality information-theory mutual-information mutualinf normality-test transfer-entropy two-sample-test variable-selection

41 stars 5.15 score 23 scripts 1 dependents

stscl

cisp:A Correlation Indicator Based on Spatial Patterns

Use the spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns.

Maintained by Wenbo Lv. Last updated 2 months ago.

association correlation geoinformatics spatial-patterns

5 stars 5.10 score 2 scripts

bioc

jazzPanda:Finding spatially relevant marker genes in image based spatial transcriptomics data

This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.

Maintained by Melody Jin. Last updated 30 days ago.

spatial geneexpression differentialexpression statisticalmethod transcriptomics correlation linear-models marker-genes spatial-transcriptomics

2 stars 5.00 score

moseleybioinformaticslab

visualizationQualityControl:Development of visualization methods for quality control

Provides utilities useful quality control of high-throughput -omics datasets.

Maintained by Robert M Flight. Last updated 1 years ago.

bioinformatics correlation quality-control visualization

10 stars 4.78 score 30 scripts

moseleybioinformaticslab

ICIKendallTau:Calculates information-content-informed Kendall-tau

Provides functions for calculating information-content-informed Kendall-tau. This version of Kendall-tau allows for the inclusion of missing values.

Maintained by Robert M Flight. Last updated 5 months ago.

correlation missingness cpp

6 stars 4.56 score 15 scripts

rameshram96

visvaR:Shiny-Based Statistical Solutions for Agricultural Research

Visualize Variance is an intuitive 'shiny' applications tailored for agricultural research data analysis, including one-way and two-way analysis of variance, correlation, and other essential statistical tools. Users can easily upload their datasets, perform analyses, and download the results as a well-formatted document, streamlining the process of data analysis and reporting in agricultural research.The experimental design methods are based on classical work by Fisher (1925) and Scheffe (1959). The correlation visualization approaches follow methods developed by Wei & Simko (2021) and Friendly (2002) <doi:10.1198/000313002533>.

Maintained by Ramesh Ramasamy. Last updated 5 months ago.

agriculture anova-analysis correlation exper experimental experimental-design

3.54 score 6 scripts

tmsalab

iccbeta:Multilevel Model Intraclass Correlation for Slope Heterogeneity

A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>. This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.

Maintained by Steven Andrew Culpepper. Last updated 5 years ago.

armadillo correlation intraclass-correlation rcpp rcpparmadillo openblas cpp

2 stars 3.00 score 5 scripts