Dynamic Missingness Graphs and Sensitivity Analysis for EMA Data

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Documentation for package ‘silentema’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

silentema-package	silentema: dynamic missingness graphs and sensitivity analysis for EMA data
bounds_support	Worst-case (support) bounds for person means under arbitrary nonresponse
break_even	Break-even sensitivity values and identified sets from a tilt profile
calibrate_delta	Calibrate the self-censoring sensitivity parameter from a design feature
coef.pairs_fit	Standard errors and confidence intervals for the lagged coefficients
confint.pairs_fit	Standard errors and confidence intervals for the lagged coefficients
default_params	Default population parameters used in the simulation studies of Yu (2026)
dm_graph	Declare a dynamic missingness graph (dm-graph)
dsep	d-separation in a dm-graph
fatigue_check	Descriptive check for burden or fatigue in the response sequence
fit_fiml	Full-information maximum likelihood under missing at random (state-space EM)
fit_ipw	Inverse-probability-weighted within-person VAR(1) for observed context confounding
fit_pairs	Within-person VAR(1) estimated from complete adjacent pairs
fit_tilt	Tilted (self-censoring-adjusted) within-person VAR(1) at a fixed value of the sensitivity parameter
loglik_fiml	Log-likelihood of the two-level VAR(1) at given parameters (MAR)
make_pairs	Build adjacent prompt pairs from long experience-sampling data
missingness_declaration	Missingness declaration for preregistrations and reports
nobs.pairs_fit	Standard errors and confidence intervals for the lagged coefficients
partial_cors	Partial correlations from a covariance matrix
plot.delta_calibration	Plot the model-implied curve of a calibration
plot.dm_graph	Plot a dm-graph
plot.tilt_profile	Plot a tilt profile
recoverability	Recoverability report for a dm-graph
sensor_gap_test	The sensor-gap test: does an always-observed sensor differ at skipped prompts?
silence_test	The silence test: does the state after a skipped prompt differ?
silentema	silentema: dynamic missingness graphs and sensitivity analysis for EMA data
simulate_ema	Simulate experience-sampling data with a declared missingness mechanism
simulate_from_fit	Simulate data from a fitted tilt model
stationary_cov	Stationary covariance of a VAR(1) process
summary.pairs_fit	Standard errors and confidence intervals for the lagged coefficients
tilt_profile	Sensitivity profile over a grid of self-censoring values
vcov.pairs_fit	Standard errors and confidence intervals for the lagged coefficients