problem()
so that an error will be thrown if argument to features
contains only missing (NA
) values (e.g., an sf object is supplied that
has NA
values in all rows for a feature's column).raster::stack()
and sp::SpatialPolyonsDataFrame()
)
are still supported, the prioritizr package will now throw deprecation
warnings. Since support for the sp and raster package classes
will be fully deprecated and removed in a later version this year, we
recommend updating code to use the sf and terra packages.problem()
objects can now contain many more
constraints and penalties. Note that any problem()
objects
that were produced using earlier versions of the package are no longer
compatible.library(sf)
).get_sim_pu_raster()
,
get_sim_pu_polygons()
, get_sim_pu_lines()
, get_sim_pu_points()
,,
get_sim_locked_in_raster()
, get_sim_locked_out_raster()
,
get_sim_zones_pu_raster()
, get_sim_zones_pu_polygons()
,
get_sim_phylogeny()
, get_sim_features()
, get_sim_zones_features()
).
These functions now return sf::st_sf()
,
terra::rast()
, ape::read.tree()
and zones()
objects.
Note that these functions are provided because data(...)
cannot be
used with terra::rast()
objects. See ?data
for more information.boundary_matrix()
output format has been updated. This means that
users will not be able to use boundary data generated using previous
versions of the package.add_lpsymphony_solver()
now throws an error, instead of a warning,
if an old version of the lpsymphony R package is installed that is known
to produce incorrect results.marxan_boundary_data_to_matrix()
function is no longer compatible
with boundary data for multiple zones.distribute_load()
function has been deprecated, because it is no
longer used. For equivalent functionality, See parallel::splitIndices()
.new_optimization_problem()
and predefined_optimization_problem()
functions have been superseded by the new optimization_problem()
function.is.Waiver()
, add_default_decisions()
new_id()
, is.Id()
, print.Id()
, pproto()
.print()
function for problem()
, optimization_problem()
, and
zones()
objects has been updated to provide more information.summary()
function to provide extensive detail on problem()
objects."bad error message"
!add_feature_weights()
when applied to problems with
an add_max_phylo_div_objective()
or add_max_phylo_end_objectve()
.
Specifically, the bug meant that weights weren't being applied to
problems with these particular objectives.add_gurobi_solver()
documentation for opening vignette.add_extra_portfolio()
) default to generating 10 solutions.solve()
function will now output tibble::tibble()
objects
(instead of data.frame()
objects), when the planning unit data are
tibble::tibble()
objects.boundary_matrix()
function now uses terra::sharedPaths()
for
calculations, providing greater performance (#257).eval_ferrier_importance()
function can now be used with
any objective function that uses targets and a single zone.add_shuffle_portfolio()
and eval_replacement_importance()
functions.add_highs_solver()
function for the HiGHS optimization software (#250).add_default_solver()
to use the HiGHS solver if the Gurobi, IBM
CPLEX, and CBC solvers aren't available.add_default_solver()
so that the add_lpsymphony_solver()
is used
instead of add_rsymphony_solver()
.problem()
and eval_feature_representation_summary()
to avoid
needlessly converting sparse matrices to regular matrices (#252).add_cbc_solver()
to throw a segfault when solving
a problem wherein the rij_matrix(x)
has a zero amount for the last feature
in the last planning unit (#247).simulate_data()
, simulate_cost()
and simulate_species()
functions to improve performance using the fields package.boundary_matrix()
to use STR query trees by default.boundary_matrix()
to use the geos package (#218).simulate_cost()
and simulate_species()
so that they no longer
depend on the RandomFields package.presolve_check()
function to (i) reduce chances of
it incorrectly throwing an error when the input data won't actually
cause any issues, and (ii) provide recommendations for addressing issues.add_min_largest_shortfall_objective()
so that
examples complete in a shorter period of time.x
that are numeric
or matrix
format, (ii)
x
that contain missing (NA
) values, and (iii) rij_matrix
that
are in dgCMatrix
format. This bug only occurred when all three of these
specific conditions were met. When it occurred, the bug caused planning units
with NA
cost values to receive very high cost values (e.g., 1e+300).
This bug meant that when attempting to solve the problem, the
presolve checks (per presolve_check()
) would throw an error complaining
about very high cost values (#236).add_locked_in_constraints()
and add_locked_out_constraints()
to ensure that a meaningful error message is provided when no planing
units are locked (#234).presolve_check()
so that it does not throw a meaningless warning
when the mathematical objective function only contains zeros.presolve_check()
to help reduce chances of mis-attributing
high connectivity/boundary values due to planning unit costs.add_connectivity_penalties()
function and documentation so that
it is designed specifically for symmetric connectivity data.add_asym_connectivity_penalties()
function that is designed
specifically for asymmetric connectivity data. This function has been
created to help ensure that asymmetric connectivity data are handled
correctly. For instance, using asymmetric connectivity data with
add_connectivity_penalties()
function in previous versions of the package
sometimes resulted in the data being incorrectly treated as symmetric data.
Additionally, this function uses an updated mathematical formulation
for handling asymmetric connectivity so that it provides similar
results to the Marxan software (#323).marxan_problem()
function so that it can be used with asymmetric
connectivity data. This is now possible because there are dedicated functions
for symmetric and asymmetric connectivity.zones
parameter of the
add_connectivity_penalties()
function.eval_ferrier_importance()
(#220). Although this
function is now recommended for general use, the documentation
contained an outdated warning and so the warning has now been removed.eval_n_summary()
function now returns a table with
the column name "n"
(instead of "cost"
) for the number
of selected planning units (#219).marxan_problem()
for importing
Marxan data files.sim_pu_sf
and sim_pu_zones_sf
data given class
updates to the sf package (compatible with version 1.0.3+).write_problem()
function.eval_ferrier_importance()
function with verified code.presolve_check()
function to throw warning when
really high values specified in add_neighbor_constraints()
.Update add_cbc_solver()
function so that it can use a starting solution to reduce run time (via the start_solution
parameter).
add_linear_constraint()
function to add arbitrary constraints.add_min_shortfall_objective()
and
add_min_largest_shortfall_objective()
functions to handle targets with
a target threshold value of zero.eval_connectivity_summary()
function,
and tweaking the header in the README.problem()
function.add_gurobi_solver()
function so that it doesn't print excess
debugging information (accidentally introduced in previous version 7.0.1.1).add_gurobi_solver()
function to support the node_file_start
parameter for the Gurobi software. This functionality is useful solving large
problems on systems with limited memory (#139).write_problem()
function to save the mixed integer programming
representation of a conservation planning problem to a file. This
function is useful for manually executing optimization solvers.rij_matrix()
function documentation (#189).add_gurobi_solver()
function to allow specification of a starting
solution (#187). This functionality is useful for conducting a boundary
penalty parameter calibration exercise. Specifically, users can specify the
starting solution for a given penalty value based on the solution
obtained using a smaller penalty value.solve
now assigns layer names based on zone names for solutions in
format.time_limit
and verbose
parameters for add_cbc_solver()
now work
as expected.add_gurobi_solver()
function to report timings following the same
methods as the other solvers.add_lpsymphony_solver()
function to be more memory efficient (#183).add_cbc_solver()
is now preferred over all other open source
solvers.add_cbc_solver()
would sometimes return incorrect solutions to
problems with equality constraints.add_cbc_solver()
function to generate solutions using the open source
CBC solver via the rcbc R package (https://github.com/dirkschumacher/rcbc).add_rsymphony_solver()
and add_lpsymphony_solver()
functions to
have a default time_limit
argument set as the maximum machine integer for
consistency.add_rsymphony_solver()
, add_lpsymphony_solver()
, and
add_gurobi_solver()
functions to require logical
(TRUE
/FALSE
)
arguments for the first_feasible
parameter.add_default_solver()
function so that it prefers
add_lpsymphony_solver()
over add_rsymphony_solver()
, and
add_cbc_solver()
over all open source solvers.gap
parameter
for the add_rsymphony_solver()
and add_lpsymphony_solver()
corresponded
to the maximum absolute difference from the optimal objective value.
This was an error due to misunderstanding the SYMPHONY documentation.
Under previous versions of the package, the gap
parameter actually
corresponded to a relative optimality gap expressed
as a percentage (such thatgap = 10
indicates that solutions must be at
least 10% from optimality). We have now fixed this error and the documentation
described for the gap
parameter is correct. We apologize for any
inconvenience this may have caused.eval_
) to mention that
the argument to solution
should only contain columns that correspond to
the solution (#176).sf
data to documentation for importance
evaluation functions (#176).solution
arguments are
supplied to the evaluation functions (#176).sf
planning unit data.add_manual_targets()
documentation.add_min_largest_shortfall()
objective function.eval_cost()
function to calculate the cost of a solution.eval_boundary()
function to calculate the exposed boundary length
associated with a solution.eval_connectivity()
function to calculate the connectivity associated
with a solution.feature_representation()
function. It is now superseded by the
eval_feature_representation()
function.eval_feature_representation()
function to assess how well each
feature is represented by a solution. This function is similar to the
deprecated eval_feature_representation()
function, except that it
follows conventions for other evaluation functions (e.g. eval_cost
).eval_target_representation()
function to assess how well each
target is met by a solution. This function is similar to the
eval_feature_representation()
, except that it corresponds to the targets
in a conservation planning problem.ferrier_score
function as eval_ferrier_importance()
function for
consistency.replacement_cost
function as eval_replacement_importance()
function
for consistency.rarity_weighted_richness
function as
eval_rare_richness_importance()
function for consistency.add_locked_out_constraints()
function to enable a single planning unit
from being locked out of multiple zones (when data are specified in raster
format).problem()
function to reduce memory consumption for sparse
matrix arguments (#164).add_cplex_solver()
function to generate solutions using
IBM CPLEX
(via the cplexAPI package).add_gap_portfolio()
documentation to note that it only works for
problems with binary decisions (#159).add_loglinear_targets()
and
loglinear_interpolation()
functions. Previously they used a natural
logarithm for log-linear interpolation. To follow target setting approaches
outlined by Rodrigues et al. (2004), they now use the decadic logarithm (i.e.
log10()
).ferrier_score()
function. It no longer incorrectly
states that these scores can be calculated using CLUZ and now states
that this functionality is experimental until the formulation can be double
checked.--run-donttest
).feature_representation()
bug incorrectly throwing error with vector
planning unit data (e.g. sf-class data).rij_matrix()
to throw an error for large raster data
(#151).add_linear_penalties()
to add penalties that penalize planning units
according to a linear metric.connectivity_matrix()
documentation to provide an example of how
to generate connectivity matrices that account for functional connectivity.solve()
function.solve()
function to the Salt Spring
Island and Tasmania vignettes.compile()
to throw warning when compiling problems that include
feature weights and an objective function that does not use feature weights.add_gurobi_solver()
function to provide more options for controlling
the pre-solve step when solving a problem.ferrier_score()
function to compute irreplaceability scores following
Ferrier et al (2000).proximity_matrix()
function to generate matrices indicating which
planning units are within a certain distance of each other (#6).connected_matrix()
function to adjacency_matrix()
function to
follow the naming conventions of other spatial association functions (#6).add_extra_portfolio()
, add_top_portfolio()
, add_gap_portfolio()
functions to provide specific options for generating portfolios (#134).intersecting_units
and fast_extract
functions to use the
exactextractr and fasterize packages to speed up raster data extraction
(#130).boundary_matrix()
function when handling SpatialPolygon
planning unit data that contain multiple polygons (e.g. a single planning unit
contains to two separate islands) (#132).set_number_of_threads()
, get_number_of_threads()
, and
is.parallel()
functions since they are no longer used with new data
extraction methods.add_pool_portfolio()
function because the new
add_extra_portfolio()
and add_top_portfolio()
functions provide this
functionality (#134).add_rsymphony_solve()r
and add_lpsymphony_solver()
throwing an
an infeasible error message for feasible problems containing continuous or
semi-continuous variables.presolve_check()
function more informative (#124).rij_matrix()
so that amounts are calculated correctly for
vector-based planning unit data.fast_extract()
.add_locked_in_constraints()
and add_locked_out_constraints()
functions so that they no longer throw an unnecessary warning when
when they are added to multi-zone problems using raster data with NA
values.add_locked_in_constraints()
and
add_locked_out_constraints()
functions to provide recommended practices
for raster data.rarity_weighted_richness()
returning incorrect scores when
the feature data contains one feature that has zeros amounts in all planning
units (e.g. the tas_features
object in the prioritizrdata R package;
#120).add_gurobi_solver()
returning solution statuses that are
slightly larger than one (e.g. 1+1.0e-10) when solving problems with
proportion-type decisions (#118).replacement_cost()
function to use parallel processing to speed up
calculations (#119).add_manual_bounded_constraints()
function to apply lower and upper
bounds on planning units statuses in a solution (#118).add_gurobi_solver()
, add_lpsymphony_solver()
, and
add_rsymphony_solver()
functions so that they will not return solutions with
values less than zero or greater than one when solving problems with
proportion-type decisions. This issue is the result of inconsistent precision
when performing floating point arithmetic (#117).add_locked_in_constraints()
and add_locked_out_constraints()
functions to provide a more helpful error message the locked_in
/locked_out
argument refers to a column with data that are not logical (i.e.
TRUE
/FALSE
; #118).solve()
function to throw a more accurate and helpful error
message when no solutions are found (e.g. due to problem infeasibility or
solver time limits).add_max_phylo_objective()
function to
add_max_phylo_div_objective()
.add_max_phylo_end_objective()
function to maximize the phylogenetic
endemism of species adequately represented in a prioritization (#113).add_max_phylo_end_objective()
, replacement_cost()
, and
rarity_weighted_richness()
functions to the Prioritizr vignette.sim_phylogeny
).add_max_phylo_div_objective()
function.irreplaceability
manual entry to document functions for calculating
irreproducibility scores.replacement_cost()
function to calculate irreproducibility scores
for each planning unit in a solution using the replacement cost method (#26).rarity_weighted_richness()
function to calculate irreproducibility
scores for each planning unit in a solution using rarity weighted richness
scores (#26).add_min_shortfall_objective()
function to find solutions that minimize
target shortfalls.add_min_shortfall_objective()
function to main vignette.problem()
tests so that they work when no solvers are installed.feature_representation()
function now requires missing (NA
) values for
planning unit statuses in a solution for planning units that have missing
(NA
) cost data.presolve_check()
function to investigate potential sources of numerical
instability before trying to solve a problem. The manual entry for this
function discusses common sources of numerical instability and approaches
for fixing them.solve()
function will now use the presolve_check()
function to
verify that problems do not have obvious sources of numerical instability
before trying to solve them. If a problem is likely to have numerical
instability issues then this function will now throw an error (unless
the solve(x, force = TRUE)
).add_rsymphony_solver()
function now uses sparse matrix formats so that
attempts can be made to solve large problems with SYMPHONY---though it is
unlikely that SYMPHONY will be able to solve such problems in a feasible
period of time.tibble::as.tibble()
instead of tibble::as_tibble()
.solve()
(#110).add_boundary_penalties()
and
add_connectivity_penalties()
function (#106).add_rsymphony_solver()
and add_lpsymphony_solver()
sometimes returned infeasible solutions when subjected to a
time limit (#105).ConservationProblem-class
objects. These methods were implemented to be
used in future interactive applications and are not currently used in the
package. As a consequence, these bugs do not affect the correctness of
any results.bad error message
error being thrown when input rasters are not
comparable (i.e. same coordinate reference system, extent, resolutions, and
dimensionality) (#104).solve()
printing annoying text about tbl_df
(#75).add_max_features_objective()
example code.add_neighbor_constraints()
and
add_contiguity_constraints()
functions used more memory than they actually
needed (#102). This is because the argument validation code converted sparse
matrix objects (i.e. dgCMatrix
) to base objects (i.e. matrix
) class
temporarily. This bug only meant inefficient utilization of computer
resources---it did not affect the correctness of any results.add_mandatory_allocation_constraints()
function. This function can be
used to ensure that every planning unit is allocated to a management zone in
the solution. It is useful when developing land-use plans where every single
parcel of land must be assigned to a specific land-use zone.add_mandatory_allocation_constraints()
to the Management Zones and
Prioritizr vignettes.$find(x)
method for Collection
prototypes that caused
it to throw an error incorrectly. This method was not used in earlier versions
of this package.feature_representation()
function that caused the "amount_held"
column to have NA values instead of the correct values. This bug only
affected problems with multiple zones.category_layer()
function that
it this function to incorrectly throw an error claiming that the input
argument to x
was invalid when it was in fact valid. This bug is
encountered when different layers the argument to x
have non-NA values in
different cells.add_contiguity_constraints()
function now uses sparse matrix formats
internally for single-zone problems. This means that the constraints
can be applied to single-zoned problem with many more planning units.add_connectivity_penalties()
function now uses sparse matrix formats
internally for single-zone problems. This means that connectivity penalties
can be applied to single-zoned problem with many more planning units.add_max_utility_objective()
and
add_max_cover_objective()
functions to make it clearer that they
do not use targets (#94).add_locked_in_constraints()
and add_locked_out_constraints()
that incorrectly threw an error when using logical
locked data
(i.e. TRUE
/FALSE
) because it incorrectly thought that valid inputs were
invalid.add_locked_in_constraints()
, add_locked_out_constraints()
,
and add_manual_locked_constraints()
where solving the same problem object
twice resulted in incorrect planning units being locked in or out of the
solution (#92).feature_abundances()
that caused the solve function to throw an
error when attempting to solve problems with a single feature.add_cuts_portfolio()
that caused the portfolio to return
solutions that were not within the specified optimality gap when using the
Gurobi solver.add_pool_portfolio()
function.feature_representation()
function now allows numeric
solutions with
attributes (e.g. when output by the solve()
function) when calculating
representation statistics for problems with numeric
planning unit data
(#91).add_manual_targets()
function threw a warning when some features had
targets equal to zero. This resulted in an excessive amount of warnings. Now,
warnings are thrown for targets that are less then zero.problem()
function sometimes incorrectly threw a warning that feature
data had negative values when the data actually did not contain negative
values. This has now been addressed.problem
function now allows negative values in the cost and feature
data (and throws a warning if such data are detected).add_absolute_targets()
and add_manual_targets()
functions now allow
negative targets (but throw a warning if such targets are specified).compile
function throws an error if a problem is compiled using
the expanded formulation with negative feature data.add_absolute_targets()
function now throws an warning---instead of an
error---if the specified targets are greater than the feature abundances
in planning units to accommodate negative values in feature data.add_max_cover_objective()
in prioritizr vignette (#90).add_relative_targets()
documentation now makes it clear that locked out
planning units are included in the calculations for setting targets (#89).add_loglinear_targets()
function now includes a feature_abundances()
parameter for specifying the total amount of each feature to use when
calculating the targets (#89).feature_abundances()
function to calculate the total amount of each
feature in the planning units (#86).add_cuts_portfolio()
function uses the Gurobi solution pool to
generate unique solutions within a specified gap of optimality when tasked
with solving problems with Gurobi (version 8.0.0+; #80).add_pool_portfolio()
function to generate a portfolio of solutions using
the Gurobi solution pool (#77).boundary_matrix()
function now has the experimental functionality to
use GEOS STR trees to speed up processing (#74).feature_representation()
function to how well features are represented
in solutions (#73).solve()
function printing superfluous text (#75).problem()
function.sim_pu_zones_stack
, sim_pu_zones_polygons
,
and sim_features_zones
for exploring conservation problems with
multiple management zones.zones
function and Zones
class to organize data with multiple
zones.problem()
function now accepts Zone
objects as arguments for
feature
to create problems with multiple zones.add_relative_targets()
and add_absolute_targets()
functions for adding
targets to problems can be used to specify targets for each feature in
each zone.add_manual_targets()
function for creating targets that pertain to
multiple management zones.solve()
function now returns a list
of solutions when generating
a portfolio of solutions.add_locked_in_constraints()
and add_locked_out_constraints()
functions for specifying which planning units are locked in or out
now accept matrix
arguments for specifying which zones are locked
in or out.add_manual_locked_constraints()
function to manually specify which
planning units should or shouldn't be allocated to specific zones in
solutions.zones
parameter) and specify how they
they should be applied (using the data
parameter. All of these functions
have default arguments that mean that problems with a single zone
should have the same optimal solution as problems created in the earlier
version of the package.add_feature_weights()
function can be used to weight different
the representation of each feature in each zone.binary_stack()
, category_layer()
, and category_vector()
functions
have been provided to help work with data for multiple management zones.?prioritizr
), and README.marxan_problem()
has been updated with more comprehensive documentation
and to provide more helpful error messages. For clarity, it will now only
work with tabular data in the standard Marxan format.add_boundary_penalties()
(#62).add_locked_in_constraints()
and add_locked_out_constraints()
throw an exception when used with semi-continuous-type decisions (#59).compile()
thrown when the same planning unit is locked in and
locked out now prints the planning unit indices in a readable format.add_locked_in_constraints()
and add_locked_out_constraints()
are ignored when using proportion-type decisions (#58).predefined_optimization_problem()
which incorrectly recognized
some inputs as invalid when they were in fact valid.R CMD check
related to proto in Depends.add_lpsymphony_solver()
now throws warnings to alert users to potentially
incorrect solutions (partially addressing #40).add_*_objectives
now pass when executed with slow solvers
(partially addressing #40).compile()
now works when no solvers are installed (#41).add_*_solvers
are now unbounded and can accept values
larger than 1 (#44).add_max_cover_objective()
function has been renamed to the
add_max_utility_objective()
, because the formulation does not follow the
historical formulation of the maximum coverage reserve selection problem
(#38).add_max_cover_objective()
function now follows the historical maximum
coverage objective. This fundamentally changes add_max_cover_objective()
function and breaks compatibility with previous versions (#38).add_lpsymphony_solver()
examples and tests to skip on Linux
operating systems.add_lpsymphony_solver()
causing error when attempting to solve
problems.numeric
vector data that caused an error.numeric
vector input with rij data
containing NA values.apply_boundary_penalties()
and add_connectivity_penalties()
causing the function to throw an error when the number of boundaries/edges is
less than the number of planning units.boundary_matrix()
calculations (#30).add_max_phylo_objective()
(#24).ScalarParameter
and ArrayParameter
prototypes to check t
that functions for generating widgets have their dependencies installed.numeric
planning unit data and portfolios that caused the
solve()
to throw an error.Spatial*DataFrame
input to marxan_problem()
would always
use the first column in the attribute table for the cost data. This bug is
serious so analysis that used Spatial*DataFrame
inputs in
marxan_problem()
should be rerun.problem()
objects.add_cuts_portfolio()
on Travis.add_cuts_portfolio()
and add_shuffle_portfolio()
tests on
CRAN.data.frame
and Spatial*DataFrame
objects
are now stored in columns named "solution_*" (e.g. "solution_1")
to store multiple solutions.verbose
argument to all solvers. This replaces the verbosity
argument in add_lpsymphony_solver()
and add_rsymphony_solver()
.add_lpsymphony_solver()
and add_rsymphony_solver()
is reduced.ConservationProblem$print()
now only prints the first three species names
and a count of the total number of features. This update means that
ConservationProblem
objects with lots of features can now safely be printed
without polluting the R console.time_limit
.devtools::build_vignettes()
. Earlier versions needed the vignettes to be
compiled using the Makefile to copy files around to avoid tangled R code
causing failures during R CMD CHECK. Although no longer needed, the vignettes
can still be compiled using the shell command make vigns
if
desired.rmarkdown::render("README.Rmd")
or using the shell command make readme
. Note that the figures for
README.md
can be found in the directory man/figures
.prshiny
will now only be run if executed during an
interactive R session. Prior to this R CMD CHECK would hang.marxan_problem()
using input data.frame()
objects.compile()
function.problem.data.frame
that meant that it did not check for missing
values in rij$pu
.add_absolute_targets()
and add_relative_targets` related to their
standardGeneric being incorrectly definedadd_corridor_targets()
when argument connectivities
is a
list
. The elements in the list are assumed to be dsCMatrix
objects
(aka symmetric sparse matrices in a compressed format) and are coerced
to dgCMatrix
objects to reduce computational burden. There was a typo,
however, and so the objects were coerced to dgCmatrix
and not dgCMatrix
.
This evidently was ok in earlier versions of the RcppArmadillo and/or
Matrix packages but not in the most recent versions.parallel::detectCores()
returns NA
on some systems
preventing users from using the Gurobi solver--even when one thread is
specified.structure(NULL, ...)
with structure(list(), ...)
.new_waiver()
.add_default_decisions()
and add_default_solver()
to own help fileadd_default_objectives()
and add_default_targets()
private functionsadd_corridor_constraints()
that fails to actually add the
constraints with argument to connectivity
is a list.make install
command so that it now actually installs the
package.Add the following code to your website.
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