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ler is a Python package for simulating compact-binary gravitational-wave populations and estimating detectable event rates for current and future LIGO-Virgo-KAGRA detector networks. The package provides a unified framework for unlensed and strongly lensed binary black hole, binary neutron star, and neutron star-black hole mergers. It samples source and lens populations, evaluates detector selection effects, solves lens equations for strongly lensed systems, and computes image properties such as magnifications and time delays. The framework supports multiple source-population and lens models, including SIS, SIE, and EPL plus external shear, and allows users to replace default distributions and detection criteria through modular interfaces. Computational efficiency is obtained through vectorized sampling, inverse-transform and importance-sampling strategies, multiprocessing, and just-in-time compiled routines. ler is designed for large-scale Monte Carlo studies in which rates and selected populations must be evaluated repeatedly, including forecasts for observing runs, studies of lensing candidate validation, and selection-function calculations for population inference. The package is distributed with documentation (https://ler.hemantaph.com/), validation examples, and reproducible workflows.