Systematic conservation prioritization using mixed integer linear
programming (MILP). It provides a flexible interface for building and
solving conservation planning problems. Once built, conservation planning
problems can be solved using a variety of commercial and open-source exact
algorithm solvers. By using exact algorithm solvers, solutions can be
generated that are guaranteed to be optimal (or within a pre-specified
optimality gap). Furthermore, conservation problems can be constructed to
optimize the spatial allocation of different management actions or zones,
meaning that conservation practitioners can identify solutions that benefit
multiple stakeholders. To solve large-scale or complex conservation
planning problems, users should install the Gurobi optimization software
(available from <https://www.gurobi.com/>) and the 'gurobi' R package (see
Gurobi Installation Guide vignette for details). Users can also install the
IBM CPLEX software (<https://www.ibm.com/analytics/cplex-optimizer>) and
the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>).
Additionally, the 'rcbc' R package (available at
<https://github.com/dirkschumacher/rcbc>) can be used to generate solutions
using the CBC optimization software (<https://github.com/coin-or/Cbc>).

Conservation planning data sets and tutorials for learning how to use the 'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.
testthat, knitr, rgeos, rasterVis, viridis, prioritizr

Conservation planning data sets for learning how to use the 'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.

Conservation planning datasets for learning how to use the
'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.

Analyzing Multiple Omics Data with an Offset Approach
Fits successive Lasso models for several blocks of (omics) data with different priorities and takes the predicted values as an offset for the next block.

Access Data from the Oregon State Prism Climate Project
Allows users to access the Oregon State Prism climate data. Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.
c(person("Hart", "Edmund", role = c("aut","cre"),email =
"Edmund.m.hart@gmail.com"),
person("Kendon", "Bell", role = c("aut"),
email = "kmb56@berkeley.edu") )
Hart Edmund [aut, cre],
Kendon Bell [aut]