Corrects two input checks and one smoothing detail reported in the second round of the methodological review. Neither input-check defect affects results built from complete INE data with the default even Lexis split.
build_abridged_life_table(),
build_abridged_life_tables() and
validate_abridged_life_table() now reject age groups that
are missing, duplicated, overlapping or unparseable, a schedule that
does not start at "00-04" or does not end in exactly one
open interval, and single-year infant input that does not hold ages 0-4
exactly once. Previously such schedules were tabulated and passed
validation.compute_exposure() with a cohort column
keeps an explicit NA triangle death count as unavailable:
that cell’s exposure is NA, a new column
is_missing_deaths flags it, and validation fails
(missing_triangle_deaths). An absent triangle row is still
a structural zero. Previously the NA became zero deaths and
validation passed. The cohort path also no longer fails when one
triangle is absent from the whole input.build_life_tables(): the smoothed female share of
exposure used for the combined-sex table is fitted on the same ages as
the Kannisto model (fit_min-99), so the pooled 100+ group
is no longer treated as a single age and the window follows
fit_min. Combined-sex life expectancy changes by less than
0.01 years.This release corrects numerical errors in 0.1.0 identified in a
methodological audit and validation of the package against the Human
Mortality Database protocol and the official tables of the Spanish
statistics institute (INE). Several corrections change numerical
results; their effect on every number reported in the accompanying
article is tabulated by
reproducibility/02_manuscript_numbers.R in the GitHub
repository.
andreev_kingkade_a0() now implements the sex-specific,
three-segment Andreev-Kingkade (2015) formulas of HMD Methods Protocol
V6, Table 3. 0.1.0 used Coale-Demeny-style coefficients (e.g. female a0
at m0 = 0.004 was 0.064 instead of 0.141).ax = 0.5 at every age except 0 (0.1.0
used a1 = 0.4); combined-sex a0 is the death-weighted
average of the sex-specific values (HMD V6 Eq. 77).build_life_tables() replaces the unweighted logit
regression with the HMD V6 Kannisto model fitted by Poisson maximum
likelihood on deaths and exposures at ages 80-99 (parameters constrained
to be non-negative), with the data-dependent replacement age Y
(100-deaths rule, 80 <= Y <= 95) and smoothed female-exposure
weights for the combined-sex table. It now needs the d_*
and e_* columns that compute_death_rates()
returns. New arguments kannisto_age and
fit_min support sensitivity analysis.compute_exposure(): the Lexis correction is
(D_L - D_U) / 6 (HMD V6 Eq. 57) with conventional triangle
labels; 0.1.0 used (D_U - D_L) / 2 with reversed labels.
Only affects deaths supplied with a cohort column; INE
deaths use the even split, where the correction is zero.nmx = ndx / nLx in every
closed group (Greville nax with the Chiang conversion,
constant-hazard fallback); 0.1.0 combined an exponential
nqx with a linear nLx.decompose_life_expectancy(method = "pollard")
integrates the open age interval exactly and closed intervals by the
midpoint rule; 0.1.0 treated the open interval as a point and recovered
only 22.5 per cent of a change confined to it. Both methods return the
attributes e0_difference and residual.x + 0.5. asfr and
asfr_female are now births per woman (TFR,
GRR and NRR are plain sums); asfr_per_1000 is provided for
display.NA death count stays NA (0.1.0
turned it into a zero rate); a province-year with no death records is
excluded rather than treated as zero mortality. Ages absent from INE’s
deaths table within a reported province-year remain structural
zeros.build_life_table() rejects non-contiguous ages and
non-finite rates. build_life_tables() withholds
province-years with missing exposure (e.g. INE populations top-coded at
85+ before 2002) or too few ages to fit the Kannisto model, and reports
them in $failed; $qc is now a tibble.validate_life_table() and
validate_abridged_life_table() also fail on missing ages,
non-finite values, a broken mx = dx / Lx identity, and
(full tables) implausible e0; validate_death_rates() checks
age coverage. ASDR is NA unless all ages have finite
rates.use_cache = TRUE and
cache_dir = NULL, retrieval now caches in a per-session
directory under tempdir() (0.1.0 silently did not
cache).provenance attribute
(request, retrieval time, API, package version).INEDEMOGR_LIVE_TESTS=true) and weekly workflow;
reproducibility/ holds frozen inputs, the validation study
against INE’s provincial life tables and fertility indicators, and the
manuscript-number scripts.get_ine_demog() retrieves live population, births, and
deaths totals from INE via ineapir::get_data_table(), at
whichever geographic level (municipality or province) each indicator is
actually published at.get_ine_geo() retrieves municipality/province
geometries via mapSpain, with an option to shift the Canary
Islands next to the mainland for compact national maps.list_ine_indicators() and
update_ine_data() round out data discovery and local cache
refresh.plot_ine_map() provides a highlight-region diagnostic
map; general choropleths are covered by System B’s
map_indicator() below.get_ine_births(), get_ine_births_by_age(),
get_ine_deaths(), and get_ine_population()
retrieve province-level, age/sex-disaggregated data directly from INE’s
Tempus3 API, following the Spanish subnational Human Mortality Database
(SHMD) protocol. get_ine_births_by_age() retrieves
single-year age-of-mother birth counts, the input the fertility schedule
functions below need.compute_exposure(), compute_death_rates(),
and build_life_table()/ build_life_tables()
implement exposure-to-risk, central death rate (1x1, 5x1,
age-standardised), and single-year period life table construction per
HMD Methods Protocol V6 (Andreev-Kingkade a0 at age 0, Kannisto old-age
smoothing).build_abridged_life_table()/build_abridged_life_tables()
build standard abridged (5-year age group) period life tables directly
from the 5x1 central death rates, complementing the single-year tables
above - useful for comparing against other agencies’ published abridged
tables.download_ine_data() runs any subset of the pipeline and
writes results to CSV and/or HMD-format .txt files for use
outside R.validate_*() family
(validate_population(), validate_births(),
validate_births_by_age(), validate_deaths(),
validate_deaths_age(), validate_exposure(),
validate_death_rates(), validate_life_table(),
validate_abridged_life_table()) flags data-quality issues
(suppressed cells, non-monotonic life tables, implausible exposure)
without failing hard.age_dependency_ratio(), aging_index(), and
sex_ratio() compute population-structure indicators from
age-disaggregated province data.crude_birth_rate(), crude_death_rate(),
general_fertility_rate(),
infant_mortality_rate(), and
rate_of_natural_increase() compute CBR, CDR, GFR, IMR, and
RNI.age_specific_fertility_rate(),
total_fertility_rate(),
mean_age_at_childbearing(),
gross_reproduction_rate(), and
net_reproduction_rate() build the full age-specific
fertility schedule (ASFR/TFR/MAC/GRR/NRR) from age-of-mother birth
counts - a true total fertility rate, which
crude_birth_rate()/ general_fertility_rate()
cannot compute on their own.birth_death_ratio() computes births per death directly
from System A’s totals, needing no age breakdown.life_expectancy_summary() and
life_expectancy() extract e0/e65 from a period life
table.decompose_life_expectancy() attributes a difference in
life expectancy at birth between two life tables (two provinces, or one
province across two years) to age-specific contributions, via Arriaga’s
(1984, exact) and Pollard’s (1988, approximate) methods.plot_population_pyramid() and
plot_demog_trend() provide population pyramid and generic
indicator time-series charts.map_indicator() provides a general-purpose choropleth
(binned or continuous) for any geography-keyed tibble;
map_life_expectancy() is a thin wrapper bridging the
mortality pipeline’s life tables onto province geometry.plot_lexis_diagram() draws an age x year mortality
surface with birth-cohort diagonals.vignette("inedemogR-tutorial") is a single
comprehensive tutorial covering data retrieval/cleaning/storage (Part I)
and demographic analysis/visualization (Part II), with the exact
formulas implemented in code and worked examples reproducing the scripts
in examples/.