Celeus - 2026 Pricing, Features, Reviews & Alternatives

Celeus is a web-based statistical analysis tool designed for researchers, analysts, and practitioners who need to run, interpret, and report statistical analyses without writing code from scratch. Users upload datasets directly in the browser; Celeus then applies an AI-assisted engine to suggest an appropriate statistical method based on the uploaded data structure and outcome type, checks key assumptions, executes the analysis in R, and returns results alongside an exportable R script. No data rows are transmitted to an external AI model, preserving data privacy throughout the workflow.

Celeus supports a broad range of analyses including Welch t-test, Student t-test, Mann-Whitney U, one-way ANOVA, Welch ANOVA, two-way ANOVA, repeated-measures ANOVA, logistic regression, Firth penalised logistic regression, ordinal logistic regression, multinomial logistic regression, Spearman and Pearson correlation, and 2x2 table measures (risk ratio, risk difference, odds ratio). Assumption checking covers normality, equal variances, events-per-variable, proportional-odds, and separation in logistic regression, with a Firth re-run offer when separation is detected. Confidence intervals are computed using Wald, score, exact, or profile-likelihood methods depending on context. Outlier flagging uses the Tukey 1.5xIQR fence and is descriptive only; no automatic data alteration occurs.

Celeus targets academic research, clinical and epidemiological research, social sciences, and education contexts. Every analysis produces a deterministic, seeded R script that can be exported and independently verified, with byte-for-byte reproducibility checks across runs. A built-in guide library covers method selection, assumption interpretation, and reporting standards, lowering the barrier to correct statistical practice for users across varying levels of statistical programming expertise.