person(given = "Ashish",
family = "Singal",
role = c("aut", "cre", "cph"),
email = "arslan@fidap.com",
comment = c(ORCID = "0000-0002-6409-4304"))
This Fidap <https://app.fidap.com> client connect to our big data warehouses and gives you seamless access to external data.
Ashish Singal [aut, cre, cph] (<https://orcid.org/0000-0002-6409-4304>)

person(given = "Matthew T.",
family = "Warkentin",
role = c("aut", "cre", "cph"),
email = "warkentin@lunenfeld.ca",
comment = c(ORCID = "0000-0001-8730-3511"))
Create secure, encrypted, and password-protected static HTML
documents that include the machinery for secure in-browser authentication
and decryption.
askpass, fs, htmltools, readr, rlang, rmarkdown, rstudioapi,
sodium, whisker
Matthew T. Warkentin [aut, cre, cph]
(<https://orcid.org/0000-0001-8730-3511>)

Bayesian Multinomial Logistic Normal Regression
c(person("Justin", "Silverman", role=c("aut"),
email = "Justin.Silverman@psu.edu"),
person("Kim", "Roche", role=("ctb"),
email = "kimberly.roche@duke.edu"),
person("Michelle", "Nixon", role=c("ctb","cre"),
email = "pistner@psu.edu"))
Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation
and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for
scalability. 'fido' replaces the previous package 'stray'.
Rcpp (>= 0.12.17), dplyr, ggplot2, purrr, tidybayes, rlang,
tidyr, phyloseq
Rcpp, RcppEigen, RcppNumerical, RcppZiggurat, BH
testthat (>= 2.1.0), knitr, rmarkdown, ape, numDeriv,
MCMCpack, MicrobeDS
Justin Silverman [aut],
Kim Roche [ctb],
Michelle Nixon [ctb, cre]

Rcpp (>= 0.12.17), dplyr, ggplot2, purrr, tidybayes, rlang,
tidyr
testthat (>= 2.1.0), knitr, rmarkdown, ape, numDeriv,
MCMCpack, MicrobeDS, phyloseq

person("Ruben", "Remelgado", role = c("aut", "cre"), email="ruben.remelgado@uni-wuerzburg.de")
In remote sensing, designing a field campaign to collect ground-truth data can be a challenging task. We need to collect representative samples while accounting for issues such as budget constraints and limited accessibility created by e.g. poor infrastructure. As suggested by Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015>, this demands the establishment of best-practices to collect ground-truth data that avoid the waste of time and funds. 'fieldRS' addresses this issue by helping scientists and practitioners design field campaigns through the identification of priority sampling sites, the extraction of potential sampling plots and the conversion of plots into consistent training and validation samples that can be used in e.g. land cover classification.
raster, sp, caret, rgdal, ggplot2, grDevices, rsMove,
spatialEco, rgeos, RStoolbox