This tutorial is designed to walk the user through some basic features of the `mop` package (Cobos, _et al_. 2024a), a suite of tools designed to extend the Mobility-Oriented Parity (MOP) metric (Owens, _et al_. 2013). MOP is a method for estimating dissimilarity between environmental datasets when projecting a niche model to novel environments outside those used to train the model (examples: assessing risk of spread of invasive species, estimating changes in available suitable habitat under climate change). When niche models are projected, generally some form of statistical extrapolation is done. This may lead to additional uncertainty in model projections, which should be strongly considered when interpreting model projection results. MOP was originally proposed as an alternative to the Multivariate Environmental Similarity Surface (MESS) metric (Elith, _et al_. 2010).

This project was completed for my STAT245 class, in collaboration with two of my classmates, Ha Dong Park and Alex Miller, under the guidance of Professor Katherine Kunnen from the Calvin Nursing Department. Our primary aim was to analyze the impact of virtual reality (VR) experiences on nursing students' confidence in conducting health assessments. We conducted a pre-survey before the nursing simulation experience and a post-survey afterward, where students answered questions to determine if their confidence levels were influenced by the VR experience.

This dashboard presents a global map and database of Global Thermal Detection Index (TDI) values to facilitate the planning of primate monitoring with Thermal Infrared (TIR) drones

ejemplo de procesos de Poisson aplicacion tesis

Limpieza de datos

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