First release. The software behind the companion paper on Lepanthes eltoroensis (Tremblay, submitted to Oikos, 2026).
expected_recruits() pairs each unit's recruits with its own reproduction by row
name, whatever the row order of X.missing = "drop", lag_ceiling() leaves the scored periods whose lag window
reaches before the record out of the observed series too, so that the observed and
the expected concentration are measured on the same periods (n_dropped says how
many).NA (with flat = TRUE), not
Inf; rain_gini() cannot return a negative value from rounding.lag_ceiling_by() applies min_periods to scored periods, and reports the per-unit
warnings once with the units they concern instead of silencing them.convolve_lag() refuses a record too short for the window and validates t_R and
lag0; lag weights are validated everywhere a profile is applied.recruit_triage() refuses a long data frame with a misnamed column instead of reading
it as a wide matrix, and refuses species = together with s/r.host_matrices() and series_from() warn when missing recruits are read as zero.ER, the expected series that is reported, and the period
effects. CmdStan is looked for after the data are validated.checks.R; the all-zero-reproduction diagnosis is a
classed condition (recruitlag_zero_lagged).recruitlag (formerly baby-steps). Lag bins are
labelled from the first lagged census (0-6, 6-12, ..., 24-30 months) in all prose.lag_ceiling(): the concentration ceiling that any delay of a reproductive record
implies, the searched ceiling over four families of lag weights (pure delays, windows,
geometric decay, Dirichlet draws; lag_kernels()), the observed concentration of the
recruit series, and their ratio, the exceedance.lag_ceiling_by(): the same for every replicate unit; rain_indices(): the ceiling
under every candidate reproductive column, reported as one table and never as a menu.ceiling_calibration(): what negative-binomial counts about a mean that obeys the
ceiling can produce, which shows what the pooled comparison cannot refute.host_lag_test() and host_matrices(): the test at the level at which the lag
hypothesis is asserted, expected recruitment on each unit being that unit's own lagged
reproduction scaled to its own total, with the negative-binomial size moment-matched about
each kernel's own expectations. Both refuse non-count recruit series.lag_ceiling_draws: lag_ceiling_boot()
(cluster bootstrap over units, BCa or percentile interval, one-sided bootstrap p),
lag_ceiling_bayesboot() (Bayesian bootstrap, equal-tailed or HDI, P(exceedance > 1)),
and lag_ceiling_stan() (negative-binomial model of the recruits with unit and period
effects, the exceedance of the expected series; needs cmdstanr and CmdStan). Each
print method says what it estimates and what its interval means.ceiling_draws_table() and rain_forest(): one table and one forest plot for several
records and routes.recruit_triage() and rain_sightings(): the state at first sighting, the runs of
unseen periods between sightings, and the expected number of first sightings that are
returns of plants present but unseen, calibrated on the published Caladenia survival
and resighting rates (caladenia_dormancy).rain_intervals(): the census schedule against its median interval, with the fraction
of a structure that a visit can see for a given persistence.period is an integer from 1 with no gaps; a missed census is a row of NA, not an
absent row; NA reproduction is read as zero with a warning.rain_gini(), rain_cv(), concentration(), phi_moment(), convolve_lag(),
expected_recruits(), series_from().rain_series(), rain_strip(), rain_strips(), rain_profiles(), rain_expected(),
rain_units(), and plot() methods for ceiling_calibration and host_lag_test.lepanthes_census, lepanthes_hosts, lepanthes_monthly, lepanthes_profile,
regimes, regimes_truth, caladenia_dormancy.recruitlag (introduction), invented-regimes, lepanthes, mathematics,
intervals.