R-universe search: exports:corr

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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

kdpeterson51

mbir:Magnitude-Based Inferences

Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of 'mbir' is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.

Maintained by Kyle Peterson. Last updated 6 years ago.

1 stars 2.48 score 15 scripts

zhangmaotai

CNPS:Nonparametric Statistics

We unify various nonparametric hypothesis testing problems in a framework of permutation testing, enabling hypothesis testing on multi-sample, multidimensional data and contingency tables. Most of the functions available in the R environment to implement permutation tests are single functions constructed for specific test problems; to facilitate the use of the package, the package encapsulates similar tests in a categorized manner, greatly improving ease of use. We will all provide functions for self-selected permutation scoring methods and self-selected p-value calculation methods (asymptotic, exact, and sampling). For two-sample tests, we will provide mean tests and estimate drift sizes; we will provide tests on variance; we will provide paired-sample tests; we will provide correlation coefficient tests under three measures. For multi-sample problems, we will provide both ordinary and ordered alternative test problems. For multidimensional data, we will implement multivariate means (including ordered alternatives) and multivariate pairwise tests based on four statistics; the components with significant differences are also calculated. For contingency tables, we will perform permutation chi-square test or ordered alternative.

Maintained by JiaSheng Zhang. Last updated 4 years ago.

1.00 score