MRTAnalysis 0.4.1

• Updated the standardized-effect-size vignette author list.

MRTAnalysis 0.4.0

• Added standardized proximal effect size estimation for continuous proximal outcomes:
  • New function calculate_mrt_effect_size() with optional LOESS smoothing and participant-level bootstrap CIs.
  • New S3 methods summary.mrt_effect_size() and plot.mrt_effect_size() for concise summaries and plotting with CIs.
  • New example dataset data_example_for_standardized_effect to illustrate usage.
  • New vignette: “Standardized Proximal Effect Size in MRTAnalysis”.
  • Updated documentation and README examples to use the new summary/plot methods.

MRTAnalysis 0.3.1

• Fixed a bug in wcls() where input data with unordered ID may cause dimension errors in matrix operations. Specifically, the split() function was sorting IDs alphabetically while cluster sizes and working covariance matrices used the order IDs appeared in the data. Now uses factor() with explicit levels to preserve ID ordering across all internal functions (wcls_bread(), leverage(), wcls_estfun(), wcls_meat(), working.covariance()).

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

• Initial release.