The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
Health stats visible at Monitor.