Researchers are often interested in determining if there is a negligible relationship among variables. For example, is the correlation between anxiety and well-being negligible, or is the difference in the means of males and females on IQ negligible. There are a collection of tests within the **negligible** package for evaluating the presence of a negligible association within the framework of 'negligible effect testing' or 'equivalence testing'.

**The negligible package can be installed via CRAN or github (the github version will generally be the most up-to-date, but also most prone to bugs)**

*Install the Package from CRAN:*

install.packages("negligible")

*Install the Package from github:*

1) Install the devtools package (if necessary). In R, paste the following into the console:

install.packages('devtools')

2) Load the devtools package and install from the Github source code:

library('devtools')

install_github('cribbie/negligible')

3) Load the newly installed package by calling

library(negligible)

**Examples of functions within the negligible package (clicking on available links will provide the vignette for that function):**

*neg.cat:* Testing for the presence of a negligible association between two categorical variables. What is negligible is quantified in terms of Cramer's V.

*neg.cfi*: This function performs one of six equivalence tests for the CFI fit index. These tests allow a researcher to evaluate if model fit is negligibly different from CFI = 1. These inferential results can supplement the descriptive results for model fit.

*neg.cor*: A negligible association test based on Pearson's *r* and resampling.

*neg.esm*: Evaluates whether substantial mediation (negligible direct effect) is present via negligible effect (equivalence) testing and David Kenny's method for assessing full mediation.

neg.indvars: This function evaluates whether the difference in the population variances of J independent groups can be considered negligible (i.e., the population variances can be considered equivalent).

*neg.intcont*: This function evaluates whether the interaction between two continuous predictor variables is negligible.

neg.normal - This function evaluates whether the difference between a target distribution and a theoretical normal distribution is negligible.

*neg.paired*: This function evaluates whether the difference in the means of 2 dependent populations can be considered negligible (i.e., the population means can be considered equivalent).

*neg.reg*: This function evaluates whether a certain predictor variable in a multiple regression model can be considered statistically and practically negligible.

*neg.rmsea*: This function performs one of four negligible effects tests based on the RMSEA model fit index. These tests allow a researcher to evaluate if model fit is negligibly different from RMSEA = 0. These inferential results can supplement the descriptive results for model fit.

*neg.semfit* - This function combines neg.cfi, neg.rmsea and neg.srmr for

SEM model fit into a single function.

neg.srmr - This function performs one of several negligible effects tests based on the SRMR model fit index. These tests allow a researcher to evaluate if model fit is negligibly different from SRMR = 0. These inferential results can supplement the descriptive results for model fit.

*neg.twocors*: This function evaluates whether the difference between two correlation coefficients can be considered statistically and practically negligible. The correlations can be independent (e.g., same variables, different groups) or dependent [e.g., r(x,y) vs r(x,z)].

*neg.twoindmeans*: This function allows researchers to test whether the difference between the means of two independent populations is negligible.