Hofmann et al., (2021). atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series. Journal of Open Source Software, 6(62), 3294, https://doi.org/10.21105/joss.03294

Schimeczek et al., (2023). AMIRIS: Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. Journal Open Source Software, 8(84), 5041, https://doi.org/10.21105/joss.05041

Fleischhacker et al., (2022). Generative DAGs as an Interface Into Probabilistic Programming with the R Package causact. Journal of Open Source Software, 7(76), 4415, https://doi.org/10.21105/joss.04415

Shahmoradi et al., (2021). ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran. Journal of Open Source Software, 6(61), 2741, https://doi.org/10.21105/joss.02741

Correndo et al., (2022). metrica: an R package to evaluate prediction performance of regression and classification point-forecast models. Journal Open Source Software, 7(79), 4655, https://doi.org/10.21105/joss.04655

background : accidental needle-stick injuries (nsis) are a hazard for health-care workers and general public health. nursing workers are at high risk for occupational exposure to blood-borne pathogens (such as hbv, hcv and hiv) via sharp injuries of needle stick. methods : this descriptive analytical cross-sectional study was done on 158 nursing workers of khanevadeh hospital in tehran to study...

Lange, J., (2021). CliquePercolation: An R Package for conducting and visualizing results of the clique percolation network community detection algorithm. Journal Open Source Software, 6(62), 3210, https://doi.org/10.21105/joss.03210

Wiebe et al., (2022). Torque-limited simple pendulum: A toolkit for getting familiar with control algorithms in underactuated robotics. Journal of Open Source Software, 7(74), 3884, https://doi.org/10.21105/joss.03884

Bartman et al., (2022). PySDM v1: particle-based cloud modeling package for warm-rain microphysics and aqueous chemistry. Journal of Open Source Software, 7(72), 3219, https://doi.org/10.21105/joss.03219

Oishi et al., (2021). eigentools: A Python package for studying differential eigenvalue problems with an emphasis on robustness. Journal of Open Source Software, 6(62), 3079, https://doi.org/10.21105/joss.03079