Contains several functions for equivalence testing and practical significance testing. First, the tsti() command provides an automatic computation of three-sided testing results for a given estimate, standard error, and region of practical equivalence. For details, see Goeman, Solari, & Stijnen (2010) <doi:10.1002/sim.4002> and Isager & Fitzgerald (2024) <doi:10.31234/osf.io/8y925>. Second, the lddtest() command performs logarithmic density discontinuity equivalence testing for regression discontinuity designs. For reference, see Fitzgerald (2025) <doi:10.31222/osf.io/2dgrp_v1>.

Support functions for R-based "EQUALCompareImages - Compare similarity between and within images" shiny application which allow researchers without coding skills or expertise in image comparison algorithms to compare images. Gurusamy,K (2025)<doi:10.5281/zenodo.16994047>.

Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.

The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.

Support functions for R-based "EQUALencrypt - Encrypt and decrypt whole files" and "EQUALencrypt - Encrypt and decrypt columns of data" shiny applications which allow researchers without coding skills or expertise in encryption algorithms to share data after encryption. Gurusamy,K (2025)<doi:10.5281/zenodo.16743676> and Gurusamy,K (2025)<doi:10.5281/zenodo.16744058>.

Functions that help with analysis of prognostic study data. This allows users with little experience of developing models to develop models and assess the performance of the prognostic models. This also summarises the information, so the performance of multiple models can be displayed simultaneously. This minor update fixes issues related to memory requirements with large number of simulations and deals with situations when there is overfitting of data. Gurusamy, K (2026)<https://github.com/kurinchi2k/EQUALPrognosis>.