Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. Cohort state-transition models (cSTM) are decision models commonly used in medical decision-making to simulate hypothetical cohorts' transitions among various health states over time. This tutorial focuses on time-independent cSTM, where transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We construct time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, conduct a cost-effectiveness analysis of multiple strategies, and conduct a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second more advanced tutorial, we illustrate time-dependent cSTMs.