crayon
and extrafont
.ggalt
.showtext
.get_base_family()
is moved to "Arial Narrow", if available.readr
options.markdown
now a suggested package for vignette building.tibble
and tidyr
changes.vctrs
0.2.0 CRAN release.summarize_to_disk()
and create_templates()
no longer default to the
user's home directory for writing to disk. A path to place the generated
files must be provided.Removed inadvertent dependency on R 3.5 tempdir()
syntax.
openfair_example()
did not check for the presence of the DT package. Fixed.
check_availability()
error message reported for all checked packages, rather
than reporting on just the packages that are missing.This release includes a greatly improved flow when starting directly from
quantitative inputs. In addition to the spreadsheet importing flow,
users have the option to start with quantitative inputs and leverage the new
tidyrisk_scenario
objects to move directly to simulation. Users with
quantitative flows may also use collector to
work with their quantitative data.
read_qualitative_inputs()
pulls in all qualitative inputs.tidyrisk_scenario
object for containing the elements of a risk scenario
for simulation.tidyrisk_scenario
object is
available.summarize_scenario()
function creates summary for a single scenario's results. summarize_scenarios()
is now a wrapper around summarize_scenario()
openfair_tef_tc_diff_plm_sr()
is now available for
simulating scenarios with secondary loss risk.loss_scatterplot()
generates a scatter plot of total loss exposure vs.
number of loss events for a single scenario.exposure_histogram()
generates a histogram of losses for a single scenario,
optionally displaying the 95% value at risk level.loss_exceedance_curve()
generates a loss exceedance curve for one or
more simulations summarized at the iteration level.scales::percent()
.select_loss_events()
now returns zeros when there are zero threat events in
a given period. Previously this function returned NA which could potentially
cause issues in reporting.aes_()
functions.run_simulation()
and modeling functions
such as openfair_tef_tc_diff_lm()
is established. run_simulation()
will always confirm a scenario object has all of the OpenFAIR components
needed by the specified modeling function. This enables easier extension of
Evaluator to new models.openfair_example()
uses (at last!) the same simulation engine as
run_simulation()
. This provides more consistent results, less code to
maintain, and opens this demo app to displaying more metrics that are native
to evaluator. A very initial pass to this last point has implemented.generate_scatterplot()
is deprecated in favor of the new
loss_scatterplot()
function.mc_
prefix to distinguish
them from parameter names.load_data()
. Use read_qualitative_inputs()
or
read_quantitative_inputs()
as appropriate.%>%
simulation
column to iteration
to be more consistent with general
MC uses.purrr::invoke()
to rlang::eval()
.purrrlyr
read_quantitative_inputs()
added to allow easier skipping over qualitative
inputs and going straight to quantitative inputs, such as generated by
collector
.summarize_domains()
was still referencing an ALE column which did not
exist on the summary roll up (aggregating ALE is possible as a strict sum
across scenarios). capabilities
table has renamed the id
column to capability_id
to
be consistent with other ID columns throughout the schema. Survey (Excel)
users are not impacted by this change.diff_estimates
option. The run_simulations()
function accounts for this change. Users using
the standard flow will not be impacted.summarize_domains()
- Incorporates the now removed
calculate_domain_impact()
and calculate_weak_domains()
functions. As part
of this consolidation, the mean_tc_exceedance
and mean_diff_exceedance
calculations are improved by handling NAs in some simulations without zeroing
out the entire calculation.summarize_domains()
- Properly calculates mean_diff_exceedance
when there
all threat events are successfully avoided.generate_heatmap()
- Takes a domain_summary
input rather than the
deprecated domain_impact
structure.base
or stats
namespaces was practically
impossible. All atomic OpenFAIR functions have been refactored to take a
fully qualified function (i.e. EnvStats::rnormTrunc
).explore_scenarios()
was trying to assign a mappings variable to the global
context, which rightly failed. Scaled back the assignment to the current
scope.select_loss_opportunities()
properly returns an NA for the threat & difficulty
exceedance calculations when there are no threat events in a given simulated
period.summarize_scenarios()
- correctly handles scenarios in which no threat
events occur in a given simulation. This bug was limited to mean_tc_exceedance
.
For previously run simulations, re-summarizing the scenario_results
will
generate corrected values.calculate_max_losses()
- no longer returns a duplicate set of results if
not passed any outliers.run_simulations()
- New ale_maximum
parameter allows an absolute cap on
per simulation annual losses to be set. This is an interim step in lieu of
full hierarchical interaction modeling.run_simulations()
- Errors encountered during runs are now reported better.run_simulations()
- Implements parallel execution via the furrr
package.
To run simulations across all cores of a local machine, load furrr
and
run plan(multicore)
before launching an analysis. For more information,
see the furrr::future_map()
documentation.sample_lm()
and sample_tc()
check if they are asked to generate zero
requested samples, bypassing calling the underlying generation function. This
avoids problems with generating functions which do not gracefully handle being
asked to sample a non positive number (zero) of events.load_data()
now fully specifies the expected CSV file formats, avoiding
possible surprises and making invocations less noisy on the console.rlang::.data
constructs, making CRAN checks much simpler.render
from
from trying to write to the package install directory.readr::cols
.intermediates_dir
value of tempdir()
.
This can be overwritten on the function call if needed.mappings
contains sample qualitative to quantitative
parameters.pkgdown
generated web documentation at https://evaluator.severski.net.generate_report
function.format
parameter to specify HTML, PDF, or Wordstyles
parameter allows user to supply custom CSS or Word
reference document to customize styles and fontsdevtools::install_github("rstudio/rmarkdown", "b84f706")
or greater.run_simulation()
callcreate_templates()
function for populating starter/sample files, making
starting a fresh analysis easier than ever!create_templates()
.extrafont
database for better font detectionsans
family when none of the preferred options
are availabletcltk
progress bar in favor of console-compatible
dplyr::progress_estimated()
. Also enables reduced package dependencies.capabilities
validationpurrrlyr
generate_report
defaults to creating a MS Word document as the output typemodeest
with a slimmer statip
dependencymagrittr
generate_report()
now takes an optional focus_scenario_ids
parameter to override the scenarios on which special emphasis (usually executive interest) is desired.summarize_all()
renamed to the more descriptive summarize_to_disk()
to avoid dplyr conflicttibble
compatibilityViewer
to rstudioapi
purrrlyr
annotate_logticks()
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