First CRAN release. The statistical code is unchanged from version 0.2.1, so every estimate produced with 0.2.0 or 0.2.1 is reproduced exactly; the release prepares the package for CRAN, completes its documentation and fixes one display issue.
n_prompts in simulate_ema() and simulate_from_fit() (it was called
T in 0.2.x, which masks TRUE); the corresponding elements of the
returned objects are settings$n_prompts (simulations) and n_prompts
(FIML fits). Code written for 0.2.x must replace T = by
n_prompts =; the generated data are unchanged.dm-graphs (the motif taxonomy, d-separation and
recoverability), testing-informativeness (the silence test, the
sensor-gap test and the fatigue check), sensitivity-analysis (tilting,
break-even values, the three calibration designs and the missingness
declaration) and simulation-and-design (the simulator, every motif's
parameters, design planning). The workflow vignette is revised.?silentema) describing the workflow, the data format
and the simulator.DESCRIPTION: method references with DOIs, URL and BugReports fields
pointing to the GitHub repository and the documentation site; the title is
shortened.citation("silentema") now points to the CRAN page of the package.recoverability, silence_test or sensor_gap_test object
with [ now returns a plain data frame; previously the class was kept and
the print method showed an incomplete report. No estimate is affected.NA; state columns must be complete at answered prompts
(item-level missingness is reported instead of producing NA results); a
response column named other than the default "R" must exist; the prompt
index must be an integer index without NA, also in fatigue_check(); the
"too few pairs/triples" messages report the counts and hint at the prompt
index. bounds_support() checks its columns and returns the whole scale for
a person without any answered prompt (previously NaN).fit_pairs() (and the tilted fits) warn when no person reaches min_pairs
complete pairs, instead of silently returning NaN between-person
summaries; fit_fiml() starts from a diagonal between-person covariance
when fewer than three persons have enough pairs (previously it failed for
one to three persons), stops when the prompt index spans a single prompt,
and warns when prompt indices are missing for some persons;
fatigue_check() no longer fails when the prompt index has fewer than four
distinct values.tilt_profile(which = ) accepts a variable name; plot.tilt_profile()
reports unknown coefficient names; plot.dm_graph() honors a user main;
print.delta_calibration() reports failed bootstrap resamples; the EM loop
of fit_fiml() checks for user interrupts.Metadata release; no change to any R or C++ code, so every result produced with 0.2.0 is reproduced exactly (checked bit-for-bit on six simulation cells).
URL points to the OSF project; the placeholder BugReports field is
removed (contact the maintainer by email).citation("silentema") uses the revised title of the accompanying
manuscript, "What skipped prompts hide: Detecting, diagnosing, and
correcting informative nonresponse in ecological momentary assessment".Revision after peer review of the accompanying manuscript.
calibrate_delta(method = "postskip") gains burden = "fit": the simulated
model includes a burden term (motif M3) calibrated to the observed response
persistence, so that the post-skip calibration is valid under a declared
M2 + M3 graph. The number of simulated data sets is now n_sim (default 20;
formerly B = 10). The result reports the number of crossings, and no
crossing is reported as such.simulate_from_fit() gains days and kappa_R, and stops with a clear
message when the fitted dynamics are not stable.recoverability() distinguishes, under reactivity (M6), the recoverable
assessment-conditioned kernel (Phi and Psi) from the dynamics-recovered
person mean, which is biased; the observed person mean is judged by
d-separation as for the other motifs.fit_tilt() returns every quantity from one undamped weighted fit at the
converged iterate, reports max_weight, passes min_pairs through, and
returns class tilt_fit also at delta = 0.silence_test() gains poly (polynomial degree in the lagged states) and
seed; fatigue_check() gains day and a print method; summary.pairs_fit()
uses a t(G - 1) reference; coef(), vcov(), confint(), nobs() methods
for fits; plot() method for calibrations; plot.dm_graph() redrawn.missingness_declaration() fills its sections from the test, profile and
calibration objects when they are supplied.delta length, weights); functions that simulate or
bootstrap restore the caller's random-number state.