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Simplified statistical analysis with plain-English interpretation for R
statease is an R package that runs a wide range of statistical analyses and tells you in plain English what the results mean. No more copy-pasting output into interpretation guides. One function call gives you the full picture.
install.packages("statease")For the development version from GitHub:
# install.packages("devtools")
devtools::install_github("DevWebWacky/statease")Try statease directly in your browser without installing R:
đ Launch statease Shiny App
| Function | What it does |
|---|---|
analyze() |
Master function - auto-detects and runs the right test |
describe() |
Descriptive statistics with interpretation |
ttest_interpret() |
T-tests with Cohenâs d and CI interpretation |
anova_interpret() |
One-way ANOVA with Tukey post-hoc and eta squared |
anova2_interpret() |
Two-way ANOVA with Type II/III SS |
manova_interpret() |
MANOVA with Pillaiâs trace and follow-up ANOVAs |
chisq_interpret() |
Chi-square test with Cramerâs V effect size |
fisher_interpret() |
Fisherâs Exact Test with Odds Ratio |
mcnemar_interpret() |
McNemarâs Test for paired categorical data |
cor_interpret() |
Correlation analysis (Pearson, Spearman, Kendall) |
reg_interpret() |
Simple linear regression with diagnostics |
mlr_interpret() |
Multiple linear regression with diagnostics |
logistic_interpret() |
Logistic regression with odds ratios |
mannwhitney_interpret() |
Mann-Whitney U test (non-parametric) |
wilcoxon_interpret() |
Wilcoxon Signed Rank test (non-parametric) |
kruskal_interpret() |
Kruskal-Wallis test with post-hoc comparisons |
friedman_interpret() |
Friedman Test with Kendallâs W |
check_assumptions() |
Automated assumption checking before analysis |
power_interpret() |
Statistical power analysis and sample size calculation |
interpret_p() |
Standalone p-value interpreter |
library(statease)
# Descriptive statistics
analyze(x = c(23, 45, 12, 67, 34), var_name = "Exam Scores")
# Independent samples t-test (auto-detected)
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
var_name = "Scores")
# Check assumptions first
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
check = TRUE)
# Non-parametric alternative (auto-detected)
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
nonparam = TRUE, var_name = "Scores")
# Correlation (auto-detected)
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
var1_name = "Exam Score", var2_name = "Study Hours")
# Chi-square (auto-detected)
analyze(
x = c("Yes","No","Yes","Yes","No"),
y = c("Male","Female","Male","Female","Male")
)
# One-way ANOVA (auto-detected)
df <- data.frame(
score = c(23,45,12,67,34,89,56,43,78,90,11,34),
group = rep(c("A","B","C"), each = 4)
)
analyze(formula = score ~ group, data = df)
# Two-way ANOVA (auto-detected)
df2 <- data.frame(
score = c(23,45,12,67,34,89,56,43,78,90,11,34),
method = rep(c("Online","Traditional"), each = 6),
gender = rep(c("Male","Female"), times = 6)
)
analyze(formula = score ~ method * gender, data = df2)
# Simple linear regression (auto-detected)
df3 <- data.frame(
exam_score = c(23,45,12,67,34,89,56,43,78,90),
study_hours = c(2,5,1,7,3,9,6,4,8,10)
)
analyze(formula = exam_score ~ study_hours, data = df3)
# Power analysis
analyze(test_type = "ttest.two", effect_size = 0.5)
# Interpret any p-value
interpret_p(0.03, context = "treatment vs control group")Most R output gives you numbers. statease gives you numbers + meaning. Perfect for: - Students learning statistics - Researchers who want fast readable output - Educators teaching statistical concepts
fisher_interpret() for Fisherâs Exact Testmcnemar_interpret() for McNemarâs Testfriedman_interpret() for Friedman Testcheck_assumptions() for automated assumption
checkingpower_interpret() for power analysis and sample
sizerun_app() for point-and-click
analysisanalyze() with check and
test_type argumentsmlr_interpret() for multiple linear
regressionlogistic_interpret() for logistic regressionmanova_interpret() for MANOVAmannwhitney_interpret() for Mann-Whitney U
testwilcoxon_interpret() for Wilcoxon Signed Rank
testkruskal_interpret() for Kruskal-Wallis testanalyze() with nonparam
argumentchisq_interpret() for chi-square testscor_interpret() for correlation analysisreg_interpret() for simple linear regressionanova2_interpret() for two-way ANOVAanalyze() to auto-detect all new testsdescribe(), ttest_interpret(),
anova_interpret(), interpret_p(),
analyze()MIT
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.