Publication Ready Features

Create journal‑style baseline summaries with clear grouping and consistent statistics.

Summarizes numeric variables as mean (SD) or median [IQR] and categorical variables as n (%), with optional group comparisons.

Table 1a serves as a summary and overview of the categorical variables in your dataset.

Its main goal is to provide a clear, at-a-glance understanding of how your sample responded to a set of questions, all of which share the same answer choices

Descriptive statistics for continuous/numerical variables across the full sample.

Reports central tendency (mean, median), spread (SD, IQR, min, max), and normality test p-values to characterize the distribution of each numerical variable.

Group comparisons of numerical and categorical variables across a binary or multi-level grouping variable.

Reports means (SD) for continuous variables and frequencies (%) for categorical variables, with group-level and total columns. Automatically selects appropriate tests — parametric/non-parametric tests for numerical variables and chi-square test for categorical variables — and displays p-values per variable.

Factors associated with a continuous outcome variable using correlation, group comparison.

For continuous predictors, reports Pearson correlation coefficient (r) and p-value. For binary grouping variables, reports Mean (SD) and Median (IQR) per group with t-test p-values. For multi-level categorical predictors, reports Mean (SD) and Median (IQR) per category with one-way ANOVA p-value. Combines all predictor types into a single unified table.

Correlation matrix displaying pairwise relationships between numerical variables with significance indicators.

Reports Mean and SD for each variable alongside a lower-triangular correlation matrix. Displays Pearson (or Spearman) correlation coefficients with significance flagged by asterisks (* p < .05, ** p < .01, *** p < .001). Variables appear as both rows and columns with diagonal dashes and upper triangle left blank to avoid redundancy.

Publication-grade regression tables for linear, logistic, and Cox regression models.

Reports coefficients (B), standard errors (SE), and p-values for all predictors including reference-coded categorical variables. For logistic regression displays Odds Ratios (OR) with 95% CI; for linear regression displays Beta coefficients; for Cox regression displays Hazard Ratios (HR). Includes model fit statistics — Log-Likelihood, AIC, Pseudo R² for logistic; R², F-statistic for linear; and concordance index for Cox models. Classifier metrics (Accuracy, Precision, Recall, F1, AUC) are appended for logistic regression.

AI‑assisted interpretation that converts coefficients into plain‑language insights.

Explains direction, magnitude, significance, and practical implications with cautionary notes.

Assess covariate balance using Standardized Mean Differences.

Computes SMD for continuous and categorical variables with thresholds for small, moderate, and large imbalances.

Multiple comparisons after ANOVA with control for family‑wise error.

Includes Tukey, Bonferroni, and Holm adjustments with clear pairwise difference reporting.

Automatically drafts a Methods section aligned with your analyses.

Describes data selection, tests used, model specifications, assumptions, and software versions.

A more granular methods engine that tailors language to specific tests and model diagnostics.

Adds assumption checks, justification of test choices, and reporting standards for advanced designs.

Instantly reformat tables, figures, and text to APA style.

Applies typography, captioning, in‑text citation formatting, and number rounding consistent with APA.

Collect, organize, and format references alongside your manuscript.

Supports importing identifiers and exporting formatted bibliographies with inline citations.

Build standard study flow diagrams with precise counts and reasons.

Templates for randomized trials, observational studies, and systematic reviews with export options.

Generate clear, standardized captions for charts, images, and flow diagrams.

Enforces journal‑ready structure with panel labels, abbreviations, and data source notes.

Prepare figures with DPI conversion, background cleanup, and precise sizing.

Includes 300 DPI conversion, transparent backgrounds, and canvas sizing for consistent submission standards.

Convert images to 300 DPI without quality loss for print‑ready output.

Optimizes pixel dimensions and metadata for journal specifications.

Export figures to PNG, TIFF, PDF, or SVG in one step.

Ensures consistent dimensions, fonts, and color profiles across formats.

Compute standardized effect sizes for common tests.

Calculates Cohen’s d, Hedges’ g, r, and η² with interpretation thresholds.

Guided interpretation of effect sizes by context and domain.

Provides qualitative labels and domain‑appropriate cutoffs with caveats.

Estimate power or required sample size for planned studies.

Handles common designs and inputs effect size, alpha, power, and allocation ratios.

Interactive guidance on study design, sampling, and bias control.

Outlines design choices, measurement strategies, and threats to validity.

Select the right test based on variable type, groups, and assumptions.

Walks through data structure and recommends appropriate tests with alternatives.

Collect, edit, and run analysis syntax within your workflow.

Keeps a reproducible log of steps, parameters, and outputs for transparency.

Verify manuscript readiness against journal expectations.

Covers formatting, ethical statements, data availability, and figure specifications.