Provides functions to access, search and download spacetime earth
observation data via SpatioTemporal Asset Catalog (STAC). This package
supports the version 1.0.0 of the STAC specification
(<http://stacspec.org>).

Provides functions to access, search and download spacetime earth
observation data via SpatioTemporal Asset Catalog (STAC). This package
supports the version 1.0.0 of the STAC specification
(<https://github.com/radiantearth/stac-spec>).
For further details see Simoes et al. (2021) <doi:10.1109/IGARSS47720.2021.9553518>.
httr, crayon, utils, magrittr, jsonlite, lifecycle

An extremely simple stack data type, implemented with 'R6'
classes. The size of the stack increases as needed, and the amortized
time complexity is O(1). The stack may contain arbitrary objects.

Persistent Fast Amortized Stack and Queue Data Structures
Provides fast, persistent (side-effect-free) stack, queue and
deque (double-ended-queue) data structures. While deques include a superset
of functionality provided by queues, in these implementations queues are
more efficient in some specialized situations. See the documentation for
rstack, rdeque, and rpqueue for details.

c(person("Jiqiang", "Guo", email = "guojq28@gmail.com", role = "aut"),
person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = "aut"),
person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu", role = c("cre", "aut")),
person("Daniel", "Lee", role = "ctb"),
person("Krzysztof", "Sakrejda", role = "ctb"),
person("Trustees of", "Columbia University", role = "cph"),
person("Oleg", "Sklyar", role = "cph", comment = "R/cxxfunplus.R"),
person("The R", "Core Team", role = "cph", comment = "R/pairs.R"),
person("Jens", "Oehlschlaegel-Akiyoshi", role = "cph", comment = "R/pairs.R"),
person("Hadley", "Wickham", role = "cph", comment = "R/rtools.R"),
person("Joel", "de Guzman", role = "cph", comment = "Boost"),
person("John", "Fletcher", role = "cph",  comment = "Boost"),
person("Thomas", "Heller", role = "cph",  comment = "Boost"),
person("Eric", "Niebler", role = "cph",   comment = "Boost"))
User-facing R functions are provided to parse, compile, test,
estimate, and analyze Stan models by accessing the header-only Stan library
provided by the 'StanHeaders' package. The Stan project develops a
probabilistic programming language that implements full Bayesian statistical
inference via Markov Chain Monte Carlo, rough Bayesian inference via variational
approximation, and (optionally penalized) maximum likelihood estimation via
optimization. In all three cases, automatic differentiation is used to quickly
and accurately evaluate gradients without burdening the user with the need
to derive the partial derivatives.
methods, stats4, inline, gridExtra (>= 2.0.0), Rcpp (>=
0.12.0)
RUnit, RcppEigen, BH (>= 1.58), parallel, KernSmooth, RCurl,
loo (>= 0.1.6), shinystan (>= 2.1.0), rstudioapi, Matrix, knitr
Jiqiang Guo [aut],
Jonah Gabry [aut],
Ben Goodrich [cre, aut],
Daniel Lee [ctb],
Krzysztof Sakrejda [ctb],
Trustees of Columbia University [cph],
Oleg Sklyar [cph] (R/cxxfunplus.R),
The R Core Team [cph] (R/pairs.R),
Jens Oehlschlaegel-Akiyoshi [cph] (R/pairs.R),
Hadley Wickham [cph] (R/rtools.R),
Joel de Guzman [cph] (Boost),
John Fletcher [cph] (Boost),
Thomas Heller [cph] (Boost),
Eric Niebler [cph] (Boost)