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  • Automated assessment of accuracy and geographical status of georeferenced biological data. The methods rely on reference regions, namely checklists and range maps. The package includes functions to obtain data from the Global Biodiversity Information Facility (https://www.gbif.org) and from the Global Inventory of Floras and Traits (https://gift.uni-goettingen.de/home). Alternatively, the user can input their own data. Furthermore, it provides easy visualisation of the data and the results through the plotting functions. It is especially suited for large datasets. The reference for the methodology is: Arlé et al. https://doi.org/10.1111/2041-210X.13629

  • The package provides features to manage the complete workflow for biodiversity data cleaning: uploading data, gathering input from users (in order to adjust cleaning procedures), cleaning data and finally, generating various reports and several versions of the data. It facilitates user-level data cleaning, designed for the inexperienced R users.

  • The package contains a number of discrete functions, each of which can be used to assess a particular form of bias, uncertainty or coverage. Generally, users must pass their occurrence data to the functions along with a list of time periods into which the outputs will be split. The functions generally return a list with two elements: a ggplot2 object, and the data that underpins that plot.

  • The package allows creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments. Moreover, it permits to investigate and visualise the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019), https://doi.org/10.1111/2041-210X.13107.

  • The package estmates the importance and the relative contribution of factors to explain species distribution by using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.

  • A set of functions for reproducible and shareable analysis of models from an online repository, comparing and diagnostic models. The package has been developed specifically to improve reproducibility and comparability of SDMs in R by allowing users to encode entire SDM analyses as repeatable and extensible workflows consisting of independently executable, community-contributed modules. The module-workflow structure enables scientists to more easily create and share components of their analysis, and then, access, modify, reuse and combine the components of others.

  • The package evaluates the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. It enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. The package is also available as shiny app.

  • The package performs spatial analyses of species' niche overlap in e-espace (environment or climate space). This software is named after Alexander von Humboldt, who was a notable Prussian geographer, explorer, and naturalist. He is widely recognized for his works on botanical geography that laid the foundation for the field of biogeography. His greatest legacy is his sweeping idea about the interconnectedness of the world, however it would be wrong to see Humboldt as only a "big picture" man. Much of Humboldt's work was based on measurements - and lots of them. He used the best technology of the day to measure everything he could - temperature, humidity, the magnetic field. And alongside this were observations of rock and soil, fungi, insects, plants, animals and people. Humboldt fused all of this together to show the links of dependency in nature connecting species distributions to key environmental variables. Since Humboldt, Lyell, Darwin, Mendel, and Grinnell brought us geology, evolution, genetics and niche theory, respectively. Using these new concepts and modern tools, we continue to pursue Humboldt's basic ideas regarding drivers of the geographic distributions of species. This package builds upon the framework introduced by Dr. Olivier Broennimann that was published in 2012 in the paper entitled "Measuring ecological niche overlap from occurrence and spatial environmental data" in the journal Global Ecology and Biogeography (issue 21: pgs 481-497). For some functions, Humboldt builds upon their the framework, updating, supplementing to, and improving their supplied R code (as a derivative work). In most cases these scripts are entirely diffent and most analogous scripts represent completely different calcuations. For most other functions, they are entirely new, such as 'humboldt.g2e', 'humboldt.doitall', 'humboldt.plot.overlap', 'humboldt.espace.correction', 'humboldt.top.env', 'humboldt.background.test', 'humboldt.pnt.index' to name a few.

  • A set of algorithms for climate and ecological-niche factor analyses. The package includes functions for visualisation of spatial variability of species sensitivity, exposure, and vulnerability to climate change. It supports processing of large files and parallel methods.

  • Read and write Frictionless Data Packages. A 'Data Package' (https://specs.frictionlessdata.io/data-package/) is a simple container format and standard to describe and package a collection of (tabular) data. It is typically used to publish FAIR (https://www.go-fair.org/fair-principles/) and open datasets.