print.Design() gains a show.IDs flag to
show the internally stored condition identifiers. Set to
FALSE by default
New notification system in runSimulation()
contributed by Moritz Ketzer
max_time now behaves more accurately when using
multi-core applications in runSimulation() and
runArraySimulation()
S3 rbind.Design() changed to
rbindDesign as there was some confusion in the S3 dispatch
purpose when internal ID elements needed to be tracked (see
#50)
Multiple parameter bias() returned incorrect
behavior/errors for relative and abs_relative
types (see #60)
Objects built by createDesign() gain []
and rbind() S3 functions for subsetting and combining by
rows. Largely included so that internal attributes such as
Design.ID are better tracked (reported by Michael S.
Truong)
runArraySimulation(..., max_time) now correctly
applies the maximum time across all subsetted conditions rather than
over each condition, thereby matching, for example, SBATCH commands in
SLURM (reported by Michael S. Truong)
SimResults() now gives the same output behavior when
store_results or save_results are used (see
issue #45)
Use of SimSolve(..., wait.time) now automatically
sets the maxiter to 3000 to avoid early
terminations
runArraySimulation() now correctly searches in
.GlobalEnv for user defined functions
manageWarnings(... suppress) argument now allows for
partial matching and other regex inputs
SimCollect() now automatically checks whether all
files are expected to be present via SimCheck()
runArraySimulation() gains a array2row
function to allow array jobs to index multiple conditions in the
design object (default uses one arrayID per
row, the original behaviour)
runArraySimulation() gains parallel
flag and friends to use multi-core processing within array
distributions. RNG numbers within the L’Ecuyer-CMRG algorithm are
incremented using parallel::nextRNGSubStream() within each
defined core
Better name checking when using the supported list
inputs in runSimulation() and
runArraySimulation()
SimCollect() more efficient when combining a large
number of files (e.g., greater than 5000 .rds files stored
via runArraySimulation()). Gains a dir
argument for this purpose as well so that a full directory can be
specified
SimCheck() repurposed to check for missing files for
runArraySimulation()
Fix for SimCollect() when
runArraySimulation() result contains mixed warning outputs
(reported by Michael S. Truong)
manageMessages() added in a similar spirit to
manageWarnigns(), though to change messages into either
errors or warnings (default behavior is the same as
quiet())
manageWarnings() gains an suppress
argument to specify explicit warnings strings that can be suppressed
(i.e., are known to be innocuous). This provides better coding practice
than the nuclear alternative
base::suppressWarnings()
convertWarnings() name changed to
manageWarning() given its increased functionality.
timeFormater() function added to isolate logic of
SBATCH time specification utility. Now used in several places of the
package (e.g., runArraySimulation(), PBA(),
SimSolve())
Switch to camel casing format in all functions (e.g.,
add_missing() -> addMissing(),
gen_seeds() -> genSeeds(), etc). Exception is that
aggregate_simulations() has changed to
SimCollect()
SimSolve() gains a predCI.tol argument
to allow algorithm termination based advertised precision of the
estimates
runSimulation(..., control = list(store_Random.seeds))
logical added to store all .Random.seed replication states.
Generally not recommended due to the size of these stored elements in
larger simulations, however can be useful for debugging purposes where
errors or warnings are not thrown
runArraySimulation() added to better support
distributing array’s of jobs on HPC clusters. Works best when combined
with new expandDesign() function (see next point) and the
improved aggregate_simulations() behaviour for more evenly
distribution replication budgets across independent jobs. An associated
vignette file has been added to the package to provide context and
tutorial information for Slurm clusters
expandDesign() added to repeat the row conditions a
number of times instead of just once. This is useful when exporting each
condition independently to computing clusters, where each cluster
contains only a fraction of the target replications (see
issue #33)
getArrayID() added to detect the array job ID (used
with runArraySimulation(..., arrayID))
aggregate_simulations() now requires explicit
filename argument to save the collapsed simulation
information
aggregate_simulations() generalized to detect
whether the Design conditions have repeated row definitions
and therefore should be conditionally averaged over (see new
expandDesign() function)
runArraySimulation() and
runSimulation()’s control list gain new
max_time and max_RAM arguments to evaluate
simulation replications up until this time or RAM storage constraint is
reached. In the event that the target replications are not reached the
simulations up to this point, or the max RAM storage has been reached,
then on the partial results will be returned (with a warning). This is
mainly useful for HPC cluster jobs that require time and RAM constraints
(e.g., 4 days per job; 4GB of RAM), where some jobs or simulation
conditions may be more time/RAM consuming than others (requested by
Mikko Rönkkö)
Expose seed generation control per simulation condition via the
function gen_seeds(), which also automatically constructs
proper L’Ecuyer-CMRG seeds to be distributed across the
runArraySimulation() jobs
SimSolve() function added to perform (stochastic)
root-solving to estimate specific criteria from simulation studies.
Currently supports uni-root type problems for continuous or discrete
variables via the probabilistic bisection algorithm with bolstering and
interpolations (ProBABLI), Brent’s method, and the classical bisection
approach, the latter two of which can be problematic if the number of
replications per iteration are too low
SFA() function added for fitting surrogate
functional forms to simulation results and subsequently solving specific
roots. Supports single root or multi-root applications, where by default
the modelling is performed via generalized linear models
runSimulation(..., store_results = TRUE) is now the
default to automatically store the results from Analyse()
in the returned simulation object. If RAM issues are suspected then
save_results = TRUE is still the recommended
approach
convertWarnings() wrapper/post-hoc function added to
convert specific warning messages to errors during simulations. Useful
when only a subset of warnings are known to be problematic, while other
warning messages (whether known or not) are treated as provisionally
innocuous
control gains a print_RAM logical
argument to suppress printing the RAM when verbose = TRUE.
Disabling this can reduce execution time as garbage collector
(gc()) calls are avoided, which is required extract the
current RAM state. Setting verbose = FALSE will also
automatically disable the RAM and gc() calls and their
overhead
Attach() now accepts matrix input
objects, and gains a RStudio_flags argument to generate
syntax that suppresses false positives about variables outside of the
function’s scope
Fix Github issue #26 related to extremely long warning/error messages
save_results_filename added to
runSimulation() saving details to allow asynchronous
(though unchecked) file storage to the same results directory (suggested
by Jan Göttmann)
ECR() gain a complement logical to
indicate whether parameter was outside advertised interval (complement
of coverage). Useful when CIs are used as formal hypothesis tests (e.g.,
bootstrap CI tests for power)
runSimulation(..., extra_options) changed to
control instead to control less commonly used
flags
createDesign() gains a fractional
argument to support design input structures from the FrF2
package for fractional factorial designs. Useful when detecting
main/low-dimensional interaction effects across a large number of factor
variables (suggested by Achim Zeileis). Example added to the wiki to
demonstrate its use
When summarise() function not supplied the
Design input is now appended to the results
object when using SimExtract(res, what = 'results'). Only
supported when the results object is a
matrix-like structure
RAM element added to resulting objects to indicate
the amount of RAM used during each evaluation. This is particularly
useful when using runSimulation(..., store_results = TRUE)
to inspect how much RAM is being being consumed (otherwise,
runSimulation(..., save_results = TRUE) should be used if
RAM storage is suspected to be an issue)
resummarise() and
aggregate_simulation() now better support the internally
stored results terms when using
store_results = TRUE
runSimulation(..., save = TRUE) changed to
save = replications > 10 to only write temporary files
when the replications are larger (less hard-drive strain when initially
testing simulation experiment with very small replications)
hexsticker added to make SimDesign part of the
cool-kids club
filename and save_results_dirname
extractors added to SimExtract()
PBA() function added for probabilistic bisection
algorithm, with associated print() and plot()
S3 methods
debug gains '-' structure to allow
debugging on specific rows of the design input. For
instance, if the simulation ran successfully until row 10, and unknown
errors terminated the simulation, then using
runSimulation(..., debug = 'error-10') will initiate the
debugger on the first instance for the 10th row conditions in the
supplied design object
Progress reporting now includes abbreviated condition names and values in the console per condition
New function nc() to be used in situation where
uniquely naming a vector or list according to the object names is useful
(cf. x <- c(A,B,C), which typically returns an unnamed
vector, to x <- nc(A,B,C), in which
names(x) is "A" "B" "C"). This is mainly
useful in the Analyse() step where objects must be named
uniquely in order to track the results in
Summarise()
Added Bradley1978() for test of Bradley’s (1978)
robustness interval for empirical detection/coverage rate
statistics
runSimulation(..., Generate) can now be specified as
a named list of functions similar to Analyse(), however
only the first valid data generation function will be used as the
constructor of the simulated data (see the new GenerateIf()
function to control the flow of these generation steps). This list input
should really only be used when the population generation functions are
differ widely depending on the condition under
investigation
SimFunctions() adds a few new inputs for saving one
or more files (save_structure), defining one or more
generate function (nGenerate), whether to include an extra
file for user-defined objects and functions (extra_file),
and whether a basic knitr::spin() header should be included
when saving the files (spin_header)
Support the future package by using
runSimulation(..., parallel = 'future') to replace the
built-in parallel processing inputs. Using the future
package approach makes several arguments to runSimulation()
unnecessary as these can be specified when defining
future::plan() (e.g., cl, MPI,
etc)
When using the future approach the
progressr package is used. Allows the progress bar to be
started via progressr::with_progress() and modified by the
front-end user (see ?runSimulatino for an example using
progressr::handler())
extra_options gains support for
.options.mpi to control the MPI properties documented in
doMPIquite() now removes the sunk connection temp file to
save storage issues (e.g., when distributing on Slurm)Attach() gains an omit argument to omit
specific elements from being attached to the working environment
(default still attaches all objects supplied)Using a list definition for Analyse input now
executes all functions by default regardless of errors thrown. Error
messages and seeds remain captured in the output, however are labelled
according to the number of errors that were observed (e.g.,
SimExtra(result, what = 'errors') may return column with
"ERROR: 2 INDEPENDENT ERRORS THROWN: ..."). Previous early
termination default can be reset by passing
extra_options = list(try_all_analyse = FALSE) to
runSimulation(). Special thanks to Mark Lai for bringing
this to my attention on Issue #20
Added beep argument to runSimulation()
to play a beep message via the beepr
Added RSE() function to compute the relative
behaviour of the average standard error to the standard deviation of a
set of parameter estimates across the replications
(RSE = E(par_SEs) / SD(par_ests))
Bugfix for new list input for analysis functions when error raised (reported by Mark Lai)
SimExtract() gains a fuzzy argument to
allow fuzzy matching of error and warning messages. This helps collapse
very similar errors messages in the recorded tables, thereby improving
how to discern any pattern in the errors/warnings (e.g., Messages such
as “ERROR: system is computationally singular: reciprocal condition
number = 9.63735e-18” and “ERROR: system is computationally
singular: reciprocal condition number = 6.74615e-17” are
effectively the same, and so their number of recorded occurrences should
be collapsed)
Added AnalyseIf() function to allow specific
analysis function to be included explicitly. Useful when the defined
analysis function is not compatible with a row-condition in the
Design object. Only relevant when the analyse
argument was defined as a named list of functions
The analyse argument to runSimulation()
now accepts a named list of functions rather than a single
analysis function. This allows the user to separate the independent
analyses into distinct functional blocks rather than having all analyses
within the same function, and potentially allows for better modularity.
The debug argument now also accepts the names of these
respective list elements to debug these function definitions
quickly
SimFunctions() gains an nAnalyses
argument to specify how many analysis functions should be templated
(default is 1, retaining the previous package defaults)
Various performance improvements to reduce execution overhead
(e.g., REPLICATION ID now moved to an
extra_option as this was identified as a
bottleneck)
Meta-statistical functions now support a
fun(list, matrix) input form to compute element-wise
summaries that return a matrix structure
Summarise() can now return list
arguments that can later be extracted via
SimExtract(sim, what = 'summarise'). Consequently, because
list outputs are now viable the purrr package has been
added to the suggests list
Prevent aggregate_simulations() from overwriting
files and directories accidentally. As well, the auto-detection of
suitable .rds files has been removed as explicitly stating the
files/directories to be aggregated is less error prone
Removed plyr::rbind.fill in favour of
dplyr::bind_row(), which removed plyr as a
dependency
Attach() now accepts multiple list-like objects as
inputs
Added SimCheck() for checking the state of a
long-running simulation via inspecting the main temp file
sessioninfo package used in placed of the
traditional sessionInfo()
Print number of cores when parallel processing is in use
A number of arguments from runSimulation() moved
into extra_options list argument to simplify
documentation
Parallel processing now uses FORK instead of PSOCK when on Unix machines by default
More natural use of RPushbullet by changing the
notification input into one that accepts a character vector
(“none”, “condition”, “complete”) to send pbPost() call.
Also more informative in the default messages sent
Added “Empirical Supremum Rejection Sampling” method to
rejectionSampling() to find better constant
M (useful when there are local minimums in the
f(x)/g(x) ratio)
rejectionSampling() made more general, with
additional examples provided in the help files
Bootstrap CI estimates moved into runSimulation(),
deprecating the less optimal SimBoot()
runSimulation(..., save=TRUE) now default to always
store meta-information about the simulation state
Added renv to the suggests lists since it’s useful
to hard-store package versions used in simulations
data.frame objects largely replaced with
tibble data frames instead as they render better for larger
simulations
Support for rbind() and cbind() on
final simulation results to add additional condition/meta-summary
information
Use createDesign() instead of
expand.grid() in code, which provides more structured
information and flexibility
Added SimExtract() to extract important but silent
information
Added stop_on_fatal logical argument to more
aggressively terminate the simulation rather than do things more
gracefully