Description

Statistical Tools Designed for End Users.

Description

The statistical tools in this package do one of four things: 1) Enhance basic statistical functions with more flexible inputs, smarter defaults, and richer, clearer, and ready-to-use output (e.g., t.test2()) 2) Produce publication-ready commonly needed figures with one line of code (e.g., plot_cdf()) 3) Implement novel analytical tools developed by the authors (e.g., twolines()) 4) Deliver niche functions of high value to the authors that are not easily available elsewhere (e.g., clear(), convert_to_sql(), resize_images()).

README.md

cran.r-project.org

statuser

Stat Tools for End Users

Basic and custom statistical tools designed with end users in mind. Functions have optimized defaults, produce decluttered and informative output that is self-explanatory, and generate publication-ready results in one line of code.

Installation

# CRAN (when available)
install.packages("statuser")

# Or pin a version with groundhog
groundhog::groundhog.library("statuser", date)

Overview

Same organization as help(statuser):

Basic Stats (improved)

lm2() — like lm(), with robust SE and much more informative output
t.test2() — like t.test(), decluttered, and more informative output
table2() — like table(), showing variable names, and with proportions & chi2 built in
desc_var() — descriptive statistics for variables (optional, by group(s))

Custom tools from papers by Uri Simonsohn

twolines() — two-lines test for U-shapes (Simonsohn 2018)
interprobe() — probe and visualize nonlinear interactions (Simonsohn 2024; Montealegre & Simonsohn 2026)
stimulus.plot() — stimulus plots for matched/treated-stimulus designs (Simonsohn, Montealegre, & Evangelidis 2025)
stimulus.beeswarm() — beeswarm plots for compared-stimulus designs (Simonsohn, Montealegre, & Evangelidis 2025)

Graphing

scatter.gam() — scatter plot for x & y, with fitted GAM line y = f(x)
plot_cdf() — empirical cumulative distribution functions (optional, by group)
plot_density() — density functions (optional, by group)
plot_freq() — frequency of observed values (optional, by group)
plot_means() — barplot of means with confidence intervals and (optionally) tests; see also stimulus.plot() for per-stimulus plots
plot_gam() — fitted GAM values for a focal predictor
text2() — like text() with horizontal alignment and background color

Formatting

format_pvalue() — format p-values for display
var_labels() — get/set variable labels (as base-R "label" attributes)
message2() — print colored messages to console
resize_images() — resize images (SVG, PDF, EPS, JPG, PNG, etc.) to PNG with specified width

Miscellaneous

clear() — clear environment, console, and all graphics devices
list2() — like list(), but unnamed objects are automatically named
convert_to_sql() — convert CSV files to SQL INSERT statements

Examples

Basic Stats (improved)

lm2(): like lm(), with robust SE and much more informative output

Uses estimatr for robust and clustered errors. Notes via lm2_notes() after printing.

lm2(mpg ~ wt + hp, data = mtcars)
lm2(mpg ~ wt + hp, data = mtcars, se_type = "HC2")
lm2_notes()

t.test2(): like t.test(), decluttered, and more informative output

men <- rnorm(100, mean = 5, sd = 1)
women <- rnorm(100, mean = 4.8, sd = 1)
t.test2(men, women)

data <- data.frame(y = rnorm(100), group = rep(c("A", "B"), 50))
t.test2(y ~ group, data = data)

table2(): like table(), showing variable names, with proportions & chi2 built in

df <- data.frame(
  group = c("A", "A", "B", "B", "A"),
  status = c("X", "Y", "X", "Y", "X")
)
table2(df$group, df$status)
table2(df$group, df$status, prop = "row", chi = TRUE)

desc_var(): descriptive statistics for variables (optional, by group(s))

df <- data.frame(score = rnorm(100), condition = rep(c("Control", "Treatment"), 50))
desc_var(score ~ condition, data = df)

Custom tools from papers by Uri Simonsohn

twolines(): two-lines test for U-shapes (Simonsohn 2018)

Implements the two-lines test (Simonsohn, 2018).

Reference: Simonsohn, U. (2018). Two lines: A valid alternative to the invalid testing of U-shaped relationships with quadratic regressions. Advances in Methods and Practices in Psychological Science, 1(4), 538–555. https://doi.org/10.1177/2515245918805755

set.seed(123)
x <- rnorm(100)
y <- -x^2 + rnorm(100)
data <- data.frame(x = x, y = y)
twolines(y ~ x, data = data)

# With covariates, suppress Robin Hood details, or save PNG
twolines(y ~ x + z, data = data, quiet = TRUE)
twolines(y ~ x, data = data, pngfile = "twolines_plot.png")

interprobe(): probe and visualize nonlinear interactions

Estimates or accepts a model, then plots simple slopes and Johnson–Neyman curves. Default engine is GAM; also supports linear models, supplied fits, and lm2().

References: Simonsohn, U. (2024). AMPPS. https://doi.org/10.1177/25152459231207787

interprobe(x = "predictor", z = "moderator", y = "outcome", data = df)
interprobe(x = "predictor", z = "moderator", y = "outcome", data = df, model = "linear")

stimulus.plot(): stimulus plots for matched/treated-stimulus designs

Reference: Simonsohn, U., Montealegre, A., & Evangelidis, I. (2025). JPSP, 129(1), 71–90. https://doi.org/10.1037/pspa0000449

stimulus.plot(
  data = df, dv = "score", stimulus = "item", condition = "cond",
  plot.type = "means", watermark = FALSE
)

stimulus.plot(
  data = df, dv = "score", stimulus = "item", condition = "cond",
  plot.type = "effects", participant = "id", simtot = 500, seed = 2024
)

clear_stimulus_cache()

stimulus.beeswarm(): beeswarm plots for compared-stimulus designs

stimulus.beeswarm(
  data = df, dv = "score", stimulus = "stim_id", condition = "cond",
  simtot = 500, watermark = FALSE
)

Graphing

scatter.gam(): scatter plot for x & y, with fitted GAM line

x <- rnorm(100)
y <- 2 * x + rnorm(100)
scatter.gam(x, y)
scatter.gam(y ~ x, data = data.frame(x, y), data.dots = TRUE)

plot_cdf(): ECDFs by group

y <- rnorm(100)
x <- rep(c("A", "B"), 50)
plot_cdf(y ~ x)
plot_cdf(y ~ x, col = c("red", "blue"), order = c("B", "A"))

plot_density(): Densities by group

plot_density(y ~ x)
plot_density(y ~ x, col = c("red", "blue"), show.means = FALSE, order = -1)

plot_freq(): Frequency plot without binning

plot_freq(c(1, 1, 2, 2, 2, 5, 5))

df <- data.frame(
  value = c(1, 1, 2, 2, 2, 5, 5),
  group = c("A", "A", "A", "B", "B", "A", "B")
)
plot_freq(value ~ group, data = df, order = c("B", "A"))
plot_freq(value ~ group, data = df, freq = FALSE, legend.title = "Treatment")

plot_means(): barplot of means with CIs and (optionally) tests

Up to three grouping factors; optional clustered SEs and custom comparisons. See stimulus.plot() for per-stimulus plots.

df <- data.frame(y = rnorm(100), group = rep(c("A", "B"), 50))
plot_means(y ~ group, data = df, quiet = TRUE)

df2 <- data.frame(
  y = rnorm(200),
  x1 = rep(c("A", "B"), 100),
  x2 = rep(c("X", "Y"), each = 100)
)
plot_means(y ~ x1 + x2, data = df2)

plot_gam(): GAM partial effect with optional distribution panel

library(mgcv)
model <- gam(mpg ~ s(hp) + s(wt) + factor(cyl), data = mtcars)
plot_gam(model, "hp", plot2 = "auto")
plot_gam(model, "hp", plot2 = "freq", quantile.others = 25)

text2(): like text() with horizontal alignment and background color

plot(1:10, 1:10, type = "n")
text2(2, 8, "Left", align = "left", bg = "lightblue")
text2(5, 8, "Center", align = "center", bg = "lightgreen")
text2(8, 8, "Right", align = "right", bg = "lightyellow")
text2(5, 5, "Red text", col = "red", bg = "white")

Formatting

format_pvalue(): format p-values for display

format_pvalue(0.05)
format_pvalue(0.0001, include_p = TRUE)

var_labels(): get/set variable labels

df <- data.frame(x = 1:3, y = 4:6)
var_labels(df) <- c("Variable X", "Variable Y")
var_labels(df)

message2(): print colored messages to console

message2("Note", col = "cyan", font = 2)

resize_images(): resize images to PNG at a set width

resize_images("path/to/image.svg", width = 800)
resize_images("path/to/images", width = c(800, 1200, 600))

Miscellaneous

clear(): clear environment, console, and graphics devices

clear()  # first run may prompt for one-time permission

list2(): like list(), auto-named objects

list2(x, y, z)

convert_to_sql(): CSV to SQL INSERT statements

convert_to_sql("data.csv", "data.sql")

Dependencies

Imports:mgcv, marginaleffects, rsvg, magick, sandwich, lmtest, lmerTest, digest, beeswarm, utils

Suggested (optional features):estimatr (lm2()), testthat, crayon, quantreg, broom, modelsummary

Author

Uri Simonsohn — [email protected] — https://github.com/urisohn/statuser

License

GPL-3

Version

CRAN release: 0.3.0 (stimulus plots, lm2_notes(), Cohen's d in t.test2(), and related fixes). Development version may be ahead of CRAN.

r-statuser

statuser

Installation

Overview

Basic Stats (improved)

Custom tools from papers by Uri Simonsohn

Graphing

Formatting

Miscellaneous

Examples

Basic Stats (improved)

Custom tools from papers by Uri Simonsohn

Graphing

Formatting

Miscellaneous

Dependencies

Author

License

Version

Version

License

Status

Source

Homepage

Platforms (79)

statuser

Installation

Overview

Basic Stats (improved)

Custom tools from papers by Uri Simonsohn

Graphing

Formatting

Miscellaneous

Examples

Basic Stats (improved)

Custom tools from papers by Uri Simonsohn

Graphing

Formatting

Miscellaneous

Dependencies

Author

License

Version

Version

License

Status

Source

Homepage

Platforms79 (79)

Platforms (79)