MRTAnalysis 0.3.0
- Added new functionality for mediated causal excursion effects in
MRTs:
- Added
mcee() function: streamlined workflow for
estimating natural direct excursion effect (NDEE) and natural indirect
excursion effect (NIEE) in micro-randomized trials (MRTs) with distal
outcomes.
- Added two advanced wrappers:
mcee_general(): flexible configuration of nuisance
models (p, q, eta, mu, nu) with support for multiple learners (glm, gam,
lm, rf, ranger, sl).
mcee_userfit_nuisance(): allows users to inject
externally fitted nuisance predictions.
- Included config helper functions (
mcee_config_glm(),
mcee_config_gam(), mcee_config_ranger(), etc.)
and mcee_config_maker() for building nuisance
specifications to pass into mcee_general().
- New dataset
data_time_varying_mediator_distal_outcome
included to illustrate usage.
- Added vignette “Time-Varying Causal Excursion Effect Mediation in
MRT: Continuous Distal Outcomes” with detailed examples and best
practices.
MRTAnalysis 0.2.0
- Added new functionality for distal outcomes in MRTs:
- Implemented
dcee() for estimating distal causal
excursion effects.
- Supports flexible nuisance regression learners (
lm,
gam, rf, ranger,
SuperLearner) with optional cross-fitting.
- Provides small-sample t inference via
summary.dcee_fit(), consistent with wcls() and
emee().
- New synthetic dataset
data_distal_continuous for
examples and testing.
- Added vignette: Exploratory Analysis for MRT: Distal Outcomes.
- Minor bug fixes and improvements to wcls() and emee()
documentation.
MRTAnalysis 0.1.2
- Fixed a bug in wcls when the randomization probability is
time-varying.
- Now all variable inputs need to be in quotation marks; for example,
from now on one should specify id = “userid” instead of id = userid.
This is to allow dynamically specified column names.
MRTAnalysis 0.1.1
- Updated vignette to improve clarify.
MRTAnalysis 0.1.0