Imports: AzureAuth, AzureGraph (>= 1.3.0), utils, curl, httr, jsonlite,

Imports: AzureAuth, AzureGraph (>= 1.3.1), utils, curl, httr, jsonlite,

Title: Simulating migration under climate change
Author: Robin Engler <robin.engler@gmail.com> and Wim Hordijk
Description: Functions for simulating species migration under climate

Author: Robin Engler <robin.engler@gmail.com> and
Description: Functions for simulating species migration

Title: Implementing dispersal into species distribution models
Description: Functions for implementing species dispersal into
projections of species distribution models (e.g. under climate

Description: Functions for implementing species dispersal into projections
of species distribution models (e.g. under climate change scenarios).

Title: Implementing Dispersal into Species Distribution Models
X-CRAN-Comment: Orphaned and corrected on 2020-01-18 as maintainer was

Title: Determining the Minimal Background Area for Species Distribution
person("Anna", "Traveset", email = "atraveset@imedea.uib-csic.es", role = c("aut")))
Description: A versatile tool that aims at (1) defining what is the minimum background extent necessary to fit good partial species distribution models and/or (2) determining if the background area used to fit a partial species distribution model is reliable enough to extract ecologically relevant conclusions from it. See Rotllan-Puig, X. & Traveset, A. (2019) <doi:10.1101/571182>.
Imports: raster, rgdal, sp, maxnet, dismo (>= 1.1-4), ecospat (>=
2.2.0), geosphere (>= 1.5-5), lattice, latticeExtra

Description: A versatile tool that aims at (1) defining the minimum background extent necessary to fit Species Distribution Models reliable enough to extract ecologically relevant conclusions from them and (2) optimizing the modelling process in terms of computation demands. See Rotllan-Puig, X. & Traveset, A. (2021) <https://www.sciencedirect.com/science/article/pii/S0304380020304191>.

Title: A Bayesian Design for Minimum Effective Dosing-Finding Trial
Author: Chia-Wei Hsu, Fang Wang, Rongji Mu, Haitao Pan, Guoying Xu
Description: The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials.