Other packages > Find by keyword >

esvis  

Visualization and Estimation of Effect Sizes
View on CRAN: Click here


Download and install esvis package within the R console
Install from CRAN:
install.packages("esvis")

Install from Github:
library("remotes")
install_github("cran/esvis")

Install by package version:
library("remotes")
install_version("esvis", "0.3.1")



Attach the package and use:
library("esvis")
Maintained by
Daniel Anderson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-08-13
Latest Update: 2020-04-30
Description:
A variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; ), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; ), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified.
How to cite:
Daniel Anderson (2017). esvis: Visualization and Estimation of Effect Sizes. R package version 0.3.1, https://cran.r-project.org/web/packages/esvis. Accessed 26 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:36), 0.1.0 (2017-08-13 11:51), 0.2.0 (2018-04-09 20:02), 0.3.0 (2020-02-28 18:10), 0.3.1 (2020-05-01 01:20)
Other packages that cited esvis R package
View esvis citation profile
Other R packages that esvis depends, imports, suggests or enhances
Complete documentation for esvis
Functions, R codes and Examples using the esvis R package
Some associated functions: auc . benchmarks . binned_es . binned_plot . coh . coh_d . descrip_stats . ecdf_fun . ecdf_plot . esvis-package . hedg . hedg_g . pac . pac_compare . paired_ecdf . pp_plot . psd . seda . star . tpac . tpac_compare . v . 
Some associated R codes: benchmarks.R . binned_es_plot.R . ecdf_plot.R . es_calcs.R . esvis-package.R . pp_plot.R . seda.R . star.R . utils.R .  Full esvis package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

leafgl  
High-Performance 'WebGl' Rendering for Package 'leaflet'
Provides bindings to the 'Leaflet.glify' JavaScript library which extends the 'leaflet' JavaScript l ...
Download / Learn more Package Citations See dependency  
clinUtils  
General Utility Functions for Analysis of Clinical Data
Utility functions to facilitate the import, the reporting and analysis of clinical data. Example ...
Download / Learn more Package Citations See dependency  
PCADSC  
Tools for Principal Component Analysis-Based Data Structure Comparisons
A suite of non-parametric, visual tools for assessing differences in data structures for two datase ...
Download / Learn more Package Citations See dependency  
SelvarMix  
Regularization for Variable Selection in Model-Based Clustering and Discriminant Analysis
Performs a regularization approach to variable selection in themodel-based clustering and classifica ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
ncodeR  
Techniques for Automated Classifiers
A set of techniques that can be used to develop, validate, and implement automated classifiers. A po ...
Download / Learn more Package Citations See dependency  

28,332

R Packages

239,283

Dependencies

75,113

Author Associations

28,333

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA