{
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  "Title": "Assessing Proximal, Distal, and Mediated Causal Excursion\nEffects for Micro-Randomized Trials",
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  "Date": "2026-01-24",
  "Authors@R": "c(\nperson(\"Tianchen\", \"Qian\", , \"t.qian@uci.edu\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-4282-7826\")),\nperson(\"Shaolin\", \"Xiang\", role = \"aut\"),\nperson(\"Zhaoxi\", \"Cheng\", role = \"aut\"),\nperson(\"Xinyi\", \"Song\", , \"songx12@uci.edu\", role = \"aut\"),\nperson(\"John\", \"Dziak\", , \"dziakj1@gmail.com\", role = \"aut\"),\nperson(\"Audrey\", \"Boruvka\", role = \"ctb\")\n)",
  "Description": "Provides methods to analyze micro-randomized trials (MRTs)\nwith binary treatment options.  Supports four types of\nanalyses: (1) proximal causal excursion effects, including\nweighted and centered least squares (WCLS) for continuous\nproximal outcomes by Boruvka et al. (2018)\n<doi:10.1080/01621459.2017.1305274> and the estimator for\nmarginal excursion effect (EMEE) for binary proximal outcomes\nby Qian et al. (2021) <doi:10.1093/biomet/asaa070>; (2) distal\ncausal excursion effects (DCEE) for continuous distal outcomes\nusing a two-stage estimator by Qian (2025)\n<doi:10.1093/biomtc/ujaf134>; (3) mediated causal excursion\neffects (MCEE) for continuous distal outcomes, estimating\nnatural direct and indirect excursion effects in the presence\nof time-varying mediators by Qian (2025)\n<doi:10.48550/arXiv.2506.20027>; and (4) standardized proximal\neffect size estimation for continuous proximal outcomes,\ngeneralizing the approach in Luers et al. (2019)\n<doi:10.1007/s11121-017-0862-5> to allow adjustment for\nbaseline and time-varying covariates for improved efficiency.",
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  "Author": "Tianchen Qian [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4282-7826>), Shaolin Xiang [aut],\nZhaoxi Cheng [aut], Xinyi Song [aut], John Dziak [aut], Audrey\nBoruvka [ctb]",
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    "mcee_general",
    "mcee_userfit_nuisance",
    "wcls"
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      "title": "A synthetic data set of an MRT with binary proximal outcomes",
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        "time",
        "time_var1",
        "time_var2",
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        "A",
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        "rand_prob"
      ],
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      "table": true,
      "tojson": true
    },
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      "title": "A synthetic data set of an MRT with continuous distal outcome",
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        "Y"
      ],
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      "table": true,
      "tojson": true
    },
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      "class": [
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        "decision_point",
        "availability",
        "prob_treatment",
        "treatment",
        "covariate1",
        "covariate2",
        "treatment_effect",
        "sigma",
        "outcome"
      ],
      "rows": 5000,
      "table": true,
      "tojson": true
    },
    {
      "name": "data_mimicHeartSteps",
      "title": "A synthetic data set that mimics the HeartSteps V1 data structure to illustrate the use of [wcls()] function for continuous proximal outcomes",
      "object": "data_mimicHeartSteps",
      "class": [
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      ],
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        "decision_point",
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        "logstep_30min",
        "logstep_30min_lag1",
        "logstep_pre30min",
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        "intervention",
        "rand_prob",
        "avail"
      ],
      "rows": 7770,
      "table": true,
      "tojson": true
    },
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      "name": "data_time_varying_mediator_distal_outcome",
      "title": "Example longitudinal dataset with time-varying mediator and distal outcome",
      "object": "data_time_varying_mediator_distal_outcome",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "dp",
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        "I_prev",
        "A_prev",
        "M_prev",
        "X",
        "I",
        "A",
        "M",
        "mu_X",
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        "p_A",
        "mu_M",
        "mu_Y",
        "Y"
      ],
      "rows": 500,
      "table": true,
      "tojson": true
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      "page": "dot-mcee_assert_df",
      "title": "Assert that input is a data frame",
      "topics": [
        ".mcee_assert_df"
      ]
    },
    {
      "page": "dot-mcee_build_f_matrix",
      "title": "Build basis matrix f(t) from time-varying effect formula",
      "topics": [
        ".mcee_build_f_matrix"
      ]
    },
    {
      "page": "dot-mcee_build_weights",
      "title": "Build per-row weights omega(i,t) for MCEE estimation",
      "topics": [
        ".mcee_build_weights"
      ]
    },
    {
      "page": "dot-mcee_check_binary_col",
      "title": "Validate binary column coding",
      "topics": [
        ".mcee_check_binary_col"
      ]
    },
    {
      "page": "dot-mcee_check_control_formula",
      "title": "Validate control formula excludes treatment and outcome",
      "topics": [
        ".mcee_check_control_formula"
      ]
    },
    {
      "page": "dot-mcee_check_dp_strictly_increasing",
      "title": "Check that decision points are strictly increasing within each subject",
      "topics": [
        ".mcee_check_dp_strictly_increasing"
      ]
    },
    {
      "page": "dot-mcee_check_formula_mediator",
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      "topics": [
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    },
    {
      "page": "dot-mcee_check_id_rows_grouped",
      "title": "Check that rows for each subject appear in contiguous blocks",
      "topics": [
        ".mcee_check_id_rows_grouped"
      ]
    },
    {
      "page": "dot-mcee_check_no_missing_vars",
      "title": "Check data frame columns for missing/infinite values",
      "topics": [
        ".mcee_check_no_missing_vars"
      ]
    },
    {
      "page": "dot-mcee_check_no_missing_vec",
      "title": "Check numeric vector for missing/infinite values",
      "topics": [
        ".mcee_check_no_missing_vec"
      ]
    },
    {
      "page": "dot-mcee_check_outcome_constant_within_id",
      "title": "Check that outcome is constant within each subject (required for distal outcomes)",
      "topics": [
        ".mcee_check_outcome_constant_within_id"
      ]
    },
    {
      "page": "dot-mcee_check_time_varying_effect_form",
      "title": "Validate time-varying effect formula structure",
      "topics": [
        ".mcee_check_time_varying_effect_form"
      ]
    },
    {
      "page": "dot-mcee_compact_model_info",
      "title": "Generate compact one-line description of nuisance model object",
      "topics": [
        ".mcee_compact_model_info"
      ]
    },
    {
      "page": "dot-mcee_core_rows",
      "title": "Numerical core implementing MCEE estimation mathematics",
      "topics": [
        ".mcee_core_rows"
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    },
    {
      "page": "dot-mcee_default_family",
      "title": "Select default GLM family based on nuisance parameter type",
      "topics": [
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      ]
    },
    {
      "page": "dot-mcee_drop_var_from_rhs",
      "title": "Remove a variable from RHS-only formula",
      "topics": [
        ".mcee_drop_var_from_rhs"
      ]
    },
    {
      "page": "dot-mcee_fit_nuisance",
      "title": "Fit a single nuisance component with flexible learner support",
      "topics": [
        ".mcee_fit_nuisance"
      ]
    },
    {
      "page": "dot-mcee_message_if_no_availability_provided",
      "title": "Print informative message if no availability column provided",
      "topics": [
        ".mcee_message_if_no_availability_provided"
      ]
    },
    {
      "page": "dot-mcee_print_coef_table",
      "title": "Print formatted coefficient table for MCEE results",
      "topics": [
        ".mcee_print_coef_table"
      ]
    },
    {
      "page": "dot-mcee_require_cols",
      "title": "Check that required columns exist in data frame",
      "topics": [
        ".mcee_require_cols"
      ]
    },
    {
      "page": "dot-mcee_resolve_rand_prob",
      "title": "Resolve randomization probability from column name or scalar",
      "topics": [
        ".mcee_resolve_rand_prob"
      ]
    },
    {
      "page": "dot-mcee_validate_clipping",
      "title": "Validate clipping bounds for probability predictions",
      "topics": [
        ".mcee_validate_clipping"
      ]
    },
    {
      "page": "dot-mcee_validate_method",
      "title": "Validate that learning method is supported",
      "topics": [
        ".mcee_validate_method"
      ]
    },
    {
      "page": "dot-mcee_vars_in_config",
      "title": "Extract variables from nuisance configuration formula",
      "topics": [
        ".mcee_vars_in_config"
      ]
    },
    {
      "page": "dot-mcee_vars_in_rhs",
      "title": "Extract variable names from RHS-only formula",
      "topics": [
        ".mcee_vars_in_rhs"
      ]
    },
    {
      "page": "calculate_mrt_effect_size",
      "title": "Calculate standardized proximal treatment effect across time (continuous outcomes)",
      "topics": [
        "calculate_mrt_effect_size"
      ]
    },
    {
      "page": "data_binary",
      "title": "A synthetic data set of an MRT with binary proximal outcomes",
      "topics": [
        "data_binary"
      ]
    },
    {
      "page": "data_distal_continuous",
      "title": "A synthetic data set of an MRT with continuous distal outcome",
      "topics": [
        "data_distal_continuous"
      ]
    },
    {
      "page": "data_example_for_standardized_effect",
      "title": "Example micro-randomized trial (MRT) data for standardized effect size",
      "topics": [
        "data_example_for_standardized_effect"
      ]
    },
    {
      "page": "data_mimicHeartSteps",
      "title": "A synthetic data set that mimics the HeartSteps V1 data structure to illustrate the use of [wcls()] function for continuous proximal outcomes",
      "topics": [
        "data_mimicHeartSteps"
      ]
    },
    {
      "page": "data_time_varying_mediator_distal_outcome",
      "title": "Example longitudinal dataset with time-varying mediator and distal outcome",
      "topics": [
        "data_time_varying_mediator_distal_outcome"
      ]
    },
    {
      "page": "dcee",
      "title": "Distal Causal Excursion Effect (DCEE) Estimation",
      "topics": [
        "dcee"
      ]
    },
    {
      "page": "emee",
      "title": "Estimates the causal excursion effect for binary outcome MRT",
      "topics": [
        "emee"
      ]
    },
    {
      "page": "emee2",
      "title": "Estimates the causal excursion effect for binary outcome MRT",
      "topics": [
        "emee2"
      ]
    },
    {
      "page": "mcee",
      "title": "Mediated Causal Excursion Effects for MRTs (streamlined)",
      "topics": [
        "mcee"
      ]
    },
    {
      "page": "mcee_config_gam",
      "title": "Configure GAM for MCEE nuisance parameters",
      "topics": [
        "mcee_config_gam"
      ]
    },
    {
      "page": "mcee_config_glm",
      "title": "Configure GLM for MCEE nuisance parameters",
      "topics": [
        "mcee_config_glm"
      ]
    },
    {
      "page": "mcee_config_known",
      "title": "Configure known constant values for MCEE nuisance parameters",
      "topics": [
        "mcee_config_known"
      ]
    },
    {
      "page": "mcee_config_lm",
      "title": "Configure linear model for MCEE nuisance parameters",
      "topics": [
        "mcee_config_lm"
      ]
    },
    {
      "page": "mcee_config_maker",
      "title": "Build a nuisance-configuration object for 'mcee_general()'",
      "topics": [
        "mcee_config_maker"
      ]
    },
    {
      "page": "mcee_config_ranger",
      "title": "Configure Ranger Random Forest for MCEE nuisance parameters",
      "topics": [
        "mcee_config_ranger"
      ]
    },
    {
      "page": "mcee_config_rf",
      "title": "Configure Random Forest for MCEE nuisance parameters",
      "topics": [
        "mcee_config_rf"
      ]
    },
    {
      "page": "mcee_config_sl",
      "title": "Configure SuperLearner for MCEE nuisance parameters",
      "topics": [
        "mcee_config_sl"
      ]
    },
    {
      "page": "mcee_config_sl_user",
      "title": "Configure SuperLearner with user-specified library for MCEE nuisance parameters",
      "topics": [
        "mcee_config_sl_user"
      ]
    },
    {
      "page": "mcee_general",
      "title": "Mediated Causal Excursion Effects (configurable nuisance models)",
      "topics": [
        "mcee_general"
      ]
    },
    {
      "page": "mcee_helper_2stage_estimation",
      "title": "Two-stage helper for mediated causal excursion effects (MCEE)",
      "topics": [
        "mcee_helper_2stage_estimation"
      ]
    },
    {
      "page": "mcee_helper_stage1_fit_nuisance",
      "title": "Fit all nuisance models for MCEE Stage 1",
      "topics": [
        "mcee_helper_stage1_fit_nuisance"
      ]
    },
    {
      "page": "mcee_helper_stage2_estimate_mcee",
      "title": "Stage-2 MCEE parameter estimation given nuisance predictions",
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      "author": "Tianchen Qian (t.qian@uci.edu)",
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      "headings": [
        "Introduction",
        "Data Structure of MRT with Distal Outcomes",
        "Distal Causal Excursion Effects",
        "Example Dataset",
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      "engine": "knitr::rmarkdown",
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        "4. A More Complex Data Example",
        "Columns (Variables)",
        "Peek at the included dataset",
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