{ "_id": "6a1e6d321d7bb097a0a62936", "Package": "manymome", "Title": "Mediation, Moderation and Moderated-Mediation After Model\nFitting", "Version": "0.3.5", "Authors@R": "c(person(given = \"Shu Fai\",\nfamily = \"Cheung\",\nrole = c(\"aut\", \"cre\"),\nemail = \"shufai.cheung@gmail.com\",\ncomment = c(ORCID = \"0000-0002-9871-9448\")),\nperson(given = \"Sing-Hang\",\nfamily = \"Cheung\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-5182-0752\")),\nperson(given = \"Rong Wei\",\nfamily = \"Sun\",\nrole = c(\"ctb\"),\ncomment = c(ORCID = \"0000-0003-0034-1422\")))", "Description": "Computes indirect effects, conditional effects, and\nconditional indirect effects in a structural equation model or\npath model after model fitting, with no need to define any user\nparameters or label any paths in the model syntax, using the\napproach presented in Cheung and Cheung (2024)\n. Can also form bootstrap\nconfidence intervals by doing bootstrapping only once and\nreusing the bootstrap estimates in all subsequent computations.\nSupports bootstrap confidence intervals for standardized\n(partially or completely) indirect effects, conditional\neffects, and conditional indirect effects as described in\nCheung (2009) and Cheung, Cheung,\nLau, Hui, and Vong (2022) . Model\nfitting can be done by structural equation modeling using\nlavaan() or regression using lm().", "URL": "https://sfcheung.github.io/manymome/", "BugReports": "https://github.com/sfcheung/manymome/issues", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "Config/testthat/parallel": "true", "Config/testthat/start-first": "cond_indirect_*", "LazyData": "true", "VignetteBuilder": "knitr", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "libglpk-dev libxml2-dev", "Repository": "https://sfcheung.r-universe.dev", "Date/Publication": "2026-06-02 00:47:36 UTC", "RemoteUrl": "https://github.com/sfcheung/manymome", "RemoteRef": "HEAD", "RemoteSha": "2a712a1a9a6a5eb879bed9477f30cffeab96792d", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 05:35:04 UTC", "User": "root" }, "Author": "Shu Fai Cheung [aut, cre] (ORCID:\n),\nSing-Hang Cheung [aut] (ORCID: ),\nRong Wei Sun [ctb] (ORCID: )", "Maintainer": "Shu Fai Cheung ", "MD5sum": "066c23d2eb46a5c213701293da1f4cc6", "_user": "sfcheung", "_type": "src", "_file": "manymome_0.3.5.tar.gz", "_fileid": "9a1599f1be5d08af3ecd2925e56d510336416d65401099e32793e2b425bf168b", "_filesize": 3237354, "_sha256": "9a1599f1be5d08af3ecd2925e56d510336416d65401099e32793e2b425bf168b", "_created": "2026-06-02T05:35:04.000Z", "_published": "2026-06-02T05:42:10.467Z", "_distro": "noble", "_jobs": [ { "job": 79006371284, "time": 354, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7349842973" }, { "job": 79006371283, "time": 376, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7349847640" }, { "job": 79006371313, "time": 173, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "ERROR", "artifact": "7349807555" }, { "job": 79006371301, "time": 235, "config": "macos-release-arm64", "r": "4.6.0", "check": "ERROR", "artifact": "7349822206" }, { "job": 79005857930, "time": 289, "config": "source", "r": "4.6.0", 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"login": "sfcheung", "linkedin": "in/shufaicheung", "twitter": "@blogonresearch", "description": "", "uuid": 7019263, "orcid": "0000-0002-9871-9448" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "lavaan", "role": "Imports" }, { "package": "boot", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "pbapply", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "igraph", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "lmhelprs", "role": "Imports" }, { "package": "psych", "role": "Imports" }, { "package": "semTools", "version": ">= 0.5-8", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "lavaan.mi", "role": "Suggests" }, { "package": "Amelia", "role": "Suggests" }, { 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"all_paths_to_df", "check_path", "cond_effects", "cond_indirect", "cond_indirect_diff", "cond_indirect_effects", "delta_med", "do_boot", "do_mc", "factor2var", "fill_wlevels", "fit2boot_out", "fit2boot_out_do_boot", "fit2mc_out", "gen_mc_est", "get_fit", "get_one_cond_effect", "get_one_cond_indirect_effect", "get_prod", "index_of_mome", "index_of_momome", "indirect_effect", "indirect_effects_from_list", "indirect_i", "indirect_on_plot", "indirect_proportion", "johnson_neyman", "lm_from_lavaan_list", "lm2boot_out", "lm2boot_out_parallel", "lm2list", "many_indirect_effects", "merge_mod_levels", "mod_levels", "mod_levels_list", "plot_effect_vs_w", "print_all_cond_effects", "print_all_cond_indirect_effects", "pseudo_johnson_neyman", "q_mediation", "q_parallel_mediation", "q_serial_mediation", "q_simple_mediation", "total_indirect_effect" ], "_datasets": [ { "name": "data_indicators", "title": "Sample Dataset: With Reverse Items", "object": "data_indicators", "class": [ "data.frame" ], "fields": [ "x_1", "x_2", "x_3", "x_4", "m1_1", "m1_2", "m1_3", "m1_4", "m2_1", "m2_2", "m2_3", "m2_4", "m3_1", "m3_2", "m3_3", "m3_4", "y_1", "y_2", "y_3", "y_4", "c1_1", "c1_2", "c1_3", "c1_4", "c2_1", "c2_2", "c2_3", "c2_4", "x", "m1", "m2", "m3", "y", "c1", "c2" ], "rows": 600, "table": true, "tojson": true }, { "name": "data_med", "title": "Sample Dataset: Simple Mediation", "object": "data_med", "class": [ "data.frame" ], "fields": [ "x", "m", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_complicated", "title": "Sample Dataset: A Complicated Mediation Model", "object": "data_med_complicated", "class": [ "data.frame" ], "fields": [ "x1", "x2", "m11", "m12", "m2", "y1", "y2", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_complicated_mg", "title": "Sample Dataset: A Complicated Mediation Model With Two Groups", "object": "data_med_complicated_mg", "class": [ "data.frame" ], "fields": [ "m11", "m12", "m2", "y1", "y2", "x1", "x2", "c1", "c2", "group" ], "rows": 200, "table": true, "tojson": true }, { "name": "data_med_mg", "title": "Sample Dataset: Simple Mediation With Two Groups", "object": "data_med_mg", "class": [ "data.frame" ], "fields": [ "x", "m", "y", "c1", "c2", "group" ], "rows": 250, "table": true, "tojson": true }, { "name": "data_med_mod_a", "title": "Sample Dataset: Simple Mediation with a-Path Moderated", "object": "data_med_mod_a", "class": [ "data.frame" ], "fields": [ "x", "w", "m", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_mod_ab", "title": "Sample Dataset: Simple Mediation with Both Paths Moderated (Two Moderators)", "object": "data_med_mod_ab", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "m", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_mod_ab1", "title": "Sample Dataset: Simple Mediation with Both Paths Moderated By a Moderator", "object": "data_med_mod_ab1", "class": [ "data.frame" ], "fields": [ "x", "w", "m", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_mod_b", "title": "Sample Dataset: Simple Mediation with b-Path Moderated", "object": "data_med_mod_b", "class": [ "data.frame" ], "fields": [ "x", "w", "m", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_mod_b_mod", "title": "Sample Dataset: A Simple Mediation Model with b-Path Moderated-Moderation", "object": "data_med_mod_b_mod", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "m", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_mod_parallel", "title": "Sample Dataset: Parallel Mediation with Two Moderators", "object": "data_med_mod_parallel", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "m1", "m2", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_mod_parallel_cat", "title": "Sample Dataset: Parallel Moderated Mediation with Two Categorical Moderators", "object": "data_med_mod_parallel_cat", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "m1", "m2", "y", "c1", "c2" ], "rows": 300, "table": true, "tojson": true }, { "name": "data_med_mod_serial", "title": "Sample Dataset: Serial Mediation with Two Moderators", "object": "data_med_mod_serial", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "m1", "m2", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_mod_serial_cat", "title": "Sample Dataset: Serial Moderated Mediation with Two Categorical Moderators", "object": "data_med_mod_serial_cat", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "m1", "m2", "y", "c1", "c2" ], "rows": 300, "table": true, "tojson": true }, { "name": "data_med_mod_serial_parallel", "title": "Sample Dataset: Serial-Parallel Mediation with Two Moderators", "object": "data_med_mod_serial_parallel", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "m11", "m12", "m2", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_med_mod_serial_parallel_cat", "title": "Sample Dataset: Serial-Parallel Moderated Mediation with Two Categorical Moderators", "object": "data_med_mod_serial_parallel_cat", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "m11", "m12", "m2", "y", "c1", "c2" ], "rows": 300, "table": true, "tojson": true }, { "name": "data_mod", "title": "Sample Dataset: One Moderator", "object": "data_mod", "class": [ "data.frame" ], "fields": [ "x", "w", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_mod_cat", "title": "Sample Dataset: Moderation with One Categorical Moderator", "object": "data_mod_cat", "class": [ "data.frame" ], "fields": [ "x", "w", "y", "c1", "c2" ], "rows": 300, "table": true, "tojson": true }, { "name": "data_mod2", "title": "Sample Dataset: Two Moderators", "object": "data_mod2", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_mome_demo", "title": "Sample Dataset: A Complicated Moderated-Mediation Model", "object": "data_mome_demo", "class": [ "data.frame" ], "fields": [ "x1", "x2", "m1", "m2", "m3", "y1", "y2", "w1", "w2", "c1", "c2" ], "rows": 200, "table": true, "tojson": true }, { "name": "data_mome_demo_missing", "title": "Sample Dataset: A Complicated Moderated-Mediation Model With Missing Data", "object": "data_mome_demo_missing", "class": [ "data.frame" ], "fields": [ "x1", "x2", "m1", "m2", "m3", "y1", "y2", "w1", "w2", "c1", "c2" ], "rows": 200, "table": true, "tojson": true }, { "name": "data_parallel", "title": "Sample Dataset: Parallel Mediation", "object": "data_parallel", "class": [ "data.frame" ], "fields": [ "x", "m1", "m2", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_sem", "title": "Sample Dataset: A Latent Variable Mediation Model With 4 Factors", "object": "data_sem", "class": [ "data.frame" ], "fields": [ "x01", "x02", "x03", "x04", "x05", "x06", "x07", "x08", "x09", "x10", "x11", "x12", "x13", "x14" ], "rows": 200, "table": true, "tojson": true }, { "name": "data_sem_mome", "title": "Sample Dataset: A Latent Variable Moderated Mediation Model With 4 Factors", "object": "data_sem_mome", "class": [ "data.frame" ], "fields": [ "x1", "x2", "x3", "x4", "w1", "w2", "w3", "w4", "m1", "m2", "m3", "m4", "y1", "y2", "y3", "y4" ], "rows": 500, "table": true, "tojson": true }, { "name": "data_serial", "title": "Sample Dataset: Serial Mediation", "object": "data_serial", "class": [ "data.frame" ], "fields": [ "x", "m1", "m2", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_serial_parallel", "title": "Sample Dataset: Serial-Parallel Mediation", "object": "data_serial_parallel", "class": [ "data.frame" ], "fields": [ "x", "m11", "m12", "m2", "y", "c1", "c2" ], "rows": 100, "table": true, "tojson": true }, { "name": "data_serial_parallel_latent", "title": "Sample Dataset: A Latent Mediation Model With Three Mediators", "object": "data_serial_parallel_latent", "class": [ "data.frame" ], "fields": [ "x1", "x2", "x3", "x4", "x5", "x6", "m11a", "m11b", "m11c", "m12a", "m12b", "m12c", "m2a", "m2b", "m2c", "y1", "y2", "y3", "y4", "y5", "y6" ], "rows": 500, "table": true, "tojson": true }, { "name": "modmed_x1m3w4y1", "title": "Sample Dataset: Moderated Serial Mediation", "object": "modmed_x1m3w4y1", "class": [ "data.frame" ], "fields": [ "x", "w1", "w2", "w3", "w4", "m1", "m2", "m3", "y", "gp", "city" ], "rows": 200, "table": true, "tojson": true }, { "name": "simple_mediation_latent", "title": "Sample Dataset: A Simple Latent Mediation Model", "object": "simple_mediation_latent", "class": [ "data.frame" ], "fields": [ "x1", "x2", "x3", "m1", "m2", "m3", "y2", "y1", "y3" ], "rows": 200, "table": true, "tojson": true } ], "_help": [ { "page": "all_indirect_paths", "title": "Enumerate All Indirect Effects in a Model", "topics": [ "all_indirect_paths", "all_paths_to_df" ] }, { "page": "check_path", "title": "Check a Path Exists in a Model", "topics": [ "check_path" ] }, { "page": "coef.cond_indirect_diff", "title": "Print the Output of 'cond_indirect_diff()'", "topics": [ "coef.cond_indirect_diff" ] }, { "page": "coef.cond_indirect_effects", "title": "Estimates of Conditional Indirect Effects or Conditional Effects", "topics": [ "coef.cond_indirect_effects" ] }, { "page": "coef.delta_med", "title": "Delta_Med in a 'delta_med'-Class Object", "topics": [ "coef.delta_med" ] }, { "page": "coef.indirect", "title": "Extract the Indirect Effect or Conditional Indirect Effect", "topics": [ "coef.indirect" ] }, { "page": "coef.indirect_list", "title": "Extract the Indirect Effects from a 'indirect_list' Object", "topics": [ "coef.indirect_list" ] }, { "page": "coef.indirect_proportion", "title": "Extract the Proportion of Effect Mediated", "topics": [ "coef.indirect_proportion" ] }, { "page": "coef.lm_from_lavaan", "title": "Coefficients of an 'lm_from_lavaan'-Class Object", "topics": [ "coef.lm_from_lavaan" ] }, { "page": "cond_indirect", "title": "Conditional, Indirect, and Conditional Indirect Effects", "topics": [ "cond_effects", "cond_indirect", "cond_indirect_effects", "indirect_effect", "many_indirect_effects" ] }, { "page": "cond_indirect_diff", "title": "Differences In Conditional Indirect Effects", "topics": [ "cond_indirect_diff" ] }, { "page": "confint.cond_indirect_diff", "title": "Confidence Interval of the Output of 'cond_indirect_diff()'", "topics": [ "confint.cond_indirect_diff" ] }, { "page": "confint.cond_indirect_effects", "title": "Confidence Intervals of Indirect Effects or Conditional Indirect Effects", "topics": [ "confint.cond_indirect_effects" ] }, { "page": "confint.delta_med", "title": "Confidence Interval for Delta_Med in a 'delta_med'-Class Object", "topics": [ "confint.delta_med" ] }, { "page": "confint.indirect", "title": "Confidence Interval of Indirect Effect or Conditional Indirect 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