Strength training prescription using percent-based approach requires
    numerous computations and assumptions. 'STMr' package allow users to estimate 
    individual reps-max relationships, implement various progression tables, and
    create numerous set and rep schemes. The 'STMr' package is originally created as
    a tool to help writing Jovanović M. (2020) Strength Training Manual
    <ISBN:979-8604459898>.

Fit a spatial-temporal occupancy models using
    a probit formulation instead of a traditional logit
    model.

Stochastic blockmodeling of one-mode and linked networks as implemented in Škulj and Žiberna (2022) <doi:10.1016/j.socnet.2022.02.001>. The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency (<https://www.arrs.si/>) within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

Collision Risk Models for avian fauna (seabird and migratory birds) at
    offshore wind farms. The base deterministic model is derived from 
    Band (2012) <https://tethys.pnnl.gov/publications/using-collision-risk-model-assess-bird-collision-risks-offshore-wind-farms>. 
    This was further expanded on by Masden (2015) <doi:10.7489/1659-1> and code 
    used here is heavily derived from this work with input from Dr A. Cook at the 
    British Trust for Ornithology. These collision risk models are useful for 
    marine ornithologists who are working in the offshore wind industry, particularly 
    in UK waters. However, many of the species included in the stochastic collision 
    risk models can also be found in the North Atlantic in the 
    United States and Canada, and could be applied there.