A collection of python and bash scripts to implement, train and deploy a generative adversarial network for population genetic inferences. The networks have been tuned to be deployed to genomic data from Anopheles mosquitoes. However, the general framework can be applied to other species. It requires the input data to be in Variant Call Format (VCF) format and the simulations need to be in msprime format. Full details about this application can be found at https://doi.org/10.5285/3ae572f6-4862-47ae-b4a0-4b9c496b5b54

R implementation of the Load Apportionment Model (LAM) model for determining relative quantities of phosphorus entering rivers from sewage treatment works and agriculture, including an user-friendly R shiny interface. Users can load their own datasets of observed phosphorus and discharge and perform four types of model fitting: automated fitting on full dataset, fitting with one user-specified coefficient on full dataset, automated and user-specified fitting on data subset. Software requirements: R or R studio with shiny and ggplot2 packages; web browser. Full details about this application can be found at https://doi.org/10.5285/900bf809-5dfd-4232-a654-df58d43b1a3f