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kassambara
rstatix:Pipe-Friendly Framework for Basic Statistical Tests
Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix. Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 'Cramer V' for the association between categorical variables. The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.
Maintained by Alboukadel Kassambara. Last updated 2 years ago.
458 stars 15.27 score 11k scripts 432 dependents
verasls
lvmisc:Veras Miscellaneous
Contains a collection of useful functions for basic data computation and manipulation, wrapper functions for generating 'ggplot2' graphics, including statistical model diagnostic plots, methods for computing statistical models quality measures (such as AIC, BIC, r squared, root mean squared error) and general utilities.
Maintained by Lucas Veras. Last updated 1 years ago.
6 stars 5.40 score 14 scripts 1 dependents
tesselle
nexus:Sourcing Archaeological Materials by Chemical Composition
Exploration and analysis of compositional data in the framework of Aitchison (1986, ISBN: 978-94-010-8324-9). This package provides tools for chemical fingerprinting and source tracking of ancient materials.
Maintained by Nicolas Frerebeau. Last updated 25 days ago.
archaeologyarchaeological-sciencearchaeometrycompositional-dataprovenance-studies
5.21 score 26 scripts 1 dependents
tobiasschoch
wbacon:Weighted BACON Algorithms
The BACON algorithms are methods for multivariate outlier nomination (detection) and robust linear regression by Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>. The extension to weighted problems is due to Beguin and Hulliger (2008) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616>; see also <doi:10.21105/joss.03238>.
Maintained by Tobias Schoch. Last updated 7 months ago.
outlieroutlier-detectionrobust-regressionstatisticsopenblasopenmp
2 stars 4.00 score 8 scripts