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params_bundle_source and
single-zip shg_load_params(url = ...).smok_params_source, mort_params_source, and
mort_params_type ("acm" or "ocm";
was params_mortality).shg_load_params() downloads/caches each zip, merges
params/ tables into engine layout smoking/ +
mortality/.getConfig() /
getReproConfig() use the new provenance field names.ftell/fseek dimension scans work; strip CR/LF
on all CSV lines in CPD, initiation/cessation, and mortality loaders so
missing "." fields parse correctly with CRLF.input_data_folder is
system.file("extdata", "2018", package = "SmokingHistoryGenerator")
(NHIS-1965–2018 csv-partial with cohort columns 1940, 1950,
2010). Removed transitional inst/extdata/2016/,
NHIS-1965–2016 test fixtures, and tests/fixtures/2016/ XML
goldens; tests and docs use the 2018 tree only. Regenerate the 2018
partial from tests/testdata/NHIS-1965-2018/csv-complete/
using
Rscript tools/refresh-nhis-2018-csv-partial.R.smoking/, mortality in mortality/; factory
defaults use relative paths (smoking/initiation.csv,
…).params_bundle_source,
params_mortality, and optional folder paths under a
params: map; shg_load_config /
shg_apply_config accept nested or flat keys.getReproConfig() / portable YAML omit
num_threads (effective segment count and seeds define the
run; thread count defaults to auto on reload).getReproConfig() exports package_repro as
r_package_version only (full install metadata remains
internal for fingerprint checks).attach_run_info = TRUE enrich
repro_config with a nested results block
(content_md5, compact summary) and a single
repro_digest (engine settings plus R session). Legacy flat
results_* / repro_engine_md5 /
r_session_md5 keys in YAML are merged or dropped on
load.-999; age_at_death
is split for never vs ever death-age stats (mean,
sd, n_obs). Top-level
ever_smokers holds count,
fraction, and integer cpd_mode (most common
rounded CPD among ever smokers). Keys count (never/ever
totals) and n_obs (rows contributing to each mean/sd)
replace bare n (YAML 1.1 reserves n).
Initiation and cessation means use ever smokers only;
age_at_death$ever_smokers is only death-age statistics (not
the same list as top-level ever_smokers).shg_save_config(..., results = ) optionally writes
those verification fields (including content_md5 and
compact results summary metadata when results is
supplied).CLI sync metadata: src/shg-cli-info.txt (YAML map
shg-cli: with MostRecentTag,
CommitHash, SrcHash; listed in
.Rbuildignore so it is not shipped in CRAN source
tarballs); python tools/shg-sync.py update-description
refreshes it from the sibling shg-cli checkout. R merges these into the
packageDescription() list as SHGMostRecentTag,
SHGCommitHash, and SHGsrcHash when the file is
present (for example devtools::load_all() from a checkout).
The old RWrapperVersion field is dropped.
shg_load_params() (URLs):
shg.params.download.timeout_sec (default 600) and
shg.params.download.connect_sec (default 60) when httr2 is
installed; clearer HTTP/network errors; HTML and non-zip responses
detected before unzip.
shg_reset_defaults() /
shg$reset_to_factory_defaults() restore engine fields to
the same defaults as a fresh SHGInterface.
shg_apply_config(shg, config) resets defaults, then
applies a sparse or full named list via useConfig(), so
partial YAML/intent configs do not inherit stale instance
state.
shg_apply_config() with
params_bundle_source now calls
shg_load_params() the same way as
shg_load_config() (clears derived paths, restores the
bundle). Without a bundle, explicit input_data_folder /
table filenames in the list are still applied.
shg_load_config() now starts from factory defaults
before applying the YAML bundle (via
reset_to_factory_defaults() in the bundle
applier).
shg_write_config_yaml(config, path) serializes any
config list: drops audit keys, and strips redundant table paths when
params_bundle_source is present (shape-driven “portable”
output).
Config lists and YAML may use mortality as an alias
for params_mortality. Normalization uses [[
only so it does not partially match
mortality_filename.
Factory defaults: mortality file is
mortality/acm.csv, matching
shg_load_params(..., mortality = "acm") bundle
layout.
shg$runSimFromFixedValues(..., attach_run_info, original_config)
and
shg$runSimFromDataFrame(..., attach_run_info, original_config)
(6-argument forms): when attach_run_info is
TRUE, the return value is a list with results,
original_config (sparse intent; default for fixed cohort =
repeat/race/sex/cohort_year), repro_config (post-run
getReproConfig(FALSE)), and run_info
(host/software/audit).data.frame of simulation output
(attach_run_info = FALSE).shg_run() / shg$runSim() accept
attach_run_info (default TRUE; set
FALSE for data-frame-only return).shg_run() / shg$runSim(): if
repeat, individuals, and N are
all omitted, repeat defaults to 1000.Direct shg$useConfig() without
shg_apply_config() still overlays on the current instance
(legacy). Prefer shg_apply_config() for defaults-first
semantics.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.