Provides a unified, user-friendly interface for fitting parametric probability distributions to continuous univariate data. 'FitVerse' supports 52 distribution families spanning symmetric, right-skewed, heavy-tailed, bounded, and extreme-value shapes, and three estimation methods: Maximum Likelihood Estimation (MLE), Method of Moments (MOM), and L-Moments (L-MOM). Automatic best-fit selection is performed using AIC, BIC, and goodness-of-fit tests (Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises (CvM)). Every fitted model produces a publication-quality diagnostic plot: a histogram overlaid with the fitted density curve and the estimated PDF formula annotated directly on the figure. An optional interactive version is produced via 'plotly'. Additional tools include bootstrap confidence intervals for parameter estimates and return levels, batch fitting across multiple columns for automated workflows and web-upload use cases, JSON serialisation for integration with 'Shiny' web applications, and automated HTML/PDF report generation. 'FitVerse' is designed to support data characterisation in survey sampling, hydrology, and actuarial workflows, where identifying the underlying distribution of a variable is a prerequisite for downstream modelling and inference. L-moment estimation follows Hosking (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x> and Hosking and Wallis (1997, ISBN:9780521430456). Model selection via AIC follows Akaike (1974) <doi:10.1109/TAC.1974.1100705> and via BIC follows Schwarz (1978) <doi:10.1214/aos/1176344136>. Bootstrap confidence intervals follow Efron and Hastie (2016, ISBN:9781107149892).