Stable Isotope Mixing Models (SIMMs) are important for ecologists. They allow for the study of animal diets via measurement of biologically relevant stable isotopes. These measurements can be used to estimate the contribution of different food sources to an animals diet. Knowledge of an animals diet is important when we wish to conserve species, as we need to know what food they rely on. Knowledge of an animals diet can be used to quantify an animals niche and to assess competition between species. SIMMs are also widely used in studies on pollution and air quality, where they may be referred to as ‘source apportionment’, ‘end member analysis’, or ‘mass balance analysis’ models. However, SIMMs are currently mainly run using Markov chain Monte Carlo (MCMC), which, while guaranteed to converge, can be prohibitively slow, requiring millions of iterations in order to reach convergence if the model is complex. In this thesis we have developed new tools for running SIMMs via Variational Bayes. This allows for a speed improvement ranging between two and one hundred times when compared to MCMC while still obtaining comparable results. The work in this thesis is divided into 3 chapters, each focusing on a different R package. Separate R packages were implemented for ease-of-use for non-expert users as well as to allow past work using these packages to be replicable in future. The packages are all designed for ecologists and individuals without a robust statistical background, with detailed vignettes and examples included in the packages. Chapter 3 in this thesis focuses on simmr, an R package for running SIMMs. simmr allows users to choose between running models through MCMC or through Fixed Form Variational Bayes. simmr is designed for ease of use for non-expert users and has built-in plotting and summary functions. Chapter 4 in this thesis focuses on cosimmr, an R package developed for running SIMMs with fixed covariates included. This package is developed using Variational Bayes and offers up to a one order of magnitude speed improvement over other packages. cosimmr has built-in predict and plotting functions and allows for users to easily visualise their results. Chapter 5 focuses on cosimmrSTAN, an R package developed which utilises STANs Variational Bayes functionality in order to run complex SIMMs with fixed and/or random effects, as well as allowing the hierarchical fitting of food sources or the use of raw source data. cosimmrSTAN offers between 70-100 times speed improvement over other packages. These speed improvements mean that ecologists can use SIMMs more easily, with accessible packages and quicker turnaround for results. This also means model comparison becomes more accessible, with users able to run multiple models quickly and compare results between them in order to make better informed decisions about covariate inclusion. Ultimately, use of these packages will allow for more comprehensive analyses of animal diets, and will allow users to gain insights into species’ role in the ecosystem.