{ "_id": "6a290105732311cd8759098c", "Package": "doseSens", "Title": "Conduct Sensitivity Analysis with Continuous Exposures and\nBinary or Continuous Outcomes", "Version": "1.0.0", "Authors@R": "person(\"Jeffrey\", \"Zhang\", email = \"jeffreyzhang1226@gmail.com\", role = c(\"aut\", \"cre\"))", "Description": "Performs sensitivity analysis for the sharp null,\nattributable effects, and weak nulls in matched studies with\ncontinuous exposures and binary or continuous outcomes as\ndescribed in Zhang, Small, Heng (2024)\n and Zhang, Heng (2024)\n. Two of the functions require\ninstallation of the 'Gurobi' optimizer. Please see\n\nfor guidance.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.2.1", "LazyData": "true", "Config/pak/sysreqs": "cmake", "Repository": "https://jzhang1937.r-universe.dev", "Date/Publication": "2025-10-11 19:42:31 UTC", "RemoteUrl": "https://github.com/jzhang1937/dosesens", "RemoteRef": "HEAD", "RemoteSha": "5fb6dea1a5f6cc997fc80fca518de0a1370535d5", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-10 06:09:41 UTC", "User": "root" }, "Author": "Jeffrey Zhang [aut, cre]", "Maintainer": "Jeffrey Zhang ", "MD5sum": "cd598913a29ec8c88059b367c2bf784f", "_user": "jzhang1937", "_type": "src", "_file": "doseSens_1.0.0.tar.gz", "_fileid": "904a0e37348e9e79969bc53575aa8cce937f2fd35d547eea84d5283402de1d67", "_filesize": 309085, "_sha256": "904a0e37348e9e79969bc53575aa8cce937f2fd35d547eea84d5283402de1d67", "_created": "2026-06-10T06:09:41.000Z", "_published": "2026-06-10T06:15:33.456Z", "_distro": "noble", "_jobs": [ 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"jzhang1937", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-41", "n": 1 } ], "_tags": [], "_stars": 0, "_contributors": [ { "user": "jzhang1937", "count": 7, "uuid": 89609625 } ], "_userbio": { "uuid": 89609625, "type": "user", "name": "jzhang1937" }, "_downloads": { "count": 143, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/doseSens" }, "_devurl": "https://github.com/jzhang1937/dosesens", "_searchresults": 0, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/doseSens.html", "manual.pdf" ], "_homeurl": "https://github.com/jzhang1937/dosesens", "_realowner": "jzhang1937", "_cranurl": true, "_releases": [ { "version": "0.1.0", "date": "2024-01-17" }, { "version": "1.0.0", "date": "2025-10-10" } ], "_exports": [ "apply_permutation_to_matrix", "binary_thresh_attribute", "constant_effects_test", "dev_TV", "dose_attributable_general", 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"uric_acid_mg_dL_missing", "phosphorus_mg_dL_missing", "calcium_mg_dL_missing", "matched_sets", "lumbar_spine_bmd", "log_lead", "race" ], "rows": 1436, "table": true, "tojson": true }, { "name": "lead_crime", "title": "Matched lead crime dataset", "object": "lead_crime", "class": [ "grouped_df", "tbl_df", "tbl", "data.frame" ], "fields": [ "age_months_continuous", "BLL_test_year", "gender", "year_of_construction", "log_home_value_testyr", "blood_draw_type", "birth_year", "pct_black_BLL", "log_med_inc_BLL", "season_2", "season_3", "season_4", "black", "matched_sets", "log_lead", "complain", "serious" ], "rows": 4134, "table": true, "tojson": true } ], "_help": [ { "page": "apply_permutation_to_matrix", "title": "A function that returns a permuted vector according to a matrix and permutation vector.", "topics": [ "apply_permutation_to_matrix" ] }, { "page": "binary_thresh_attribute", "title": "Separable algorithm for threshold attributable effect in a sensitivity analysis with at most one over-exposed unit in each matched set. For a greater than alternative, finds the 'a' matched sets that most decrease the mean and/or variance.", "topics": [ "binary_thresh_attribute" ] }, { "page": "change_Delta", "title": "A helper that takes a gurobi model object outputted from dose_attributable_general or dose_thresh_attributable_one_sided and changes the Delta parameter. Saves computation time by directly editing the constraint involving Delta without having to reinitialize the other constraints that are kept the same. Outputs a list analogous to output from dose_attributable_general or dose_thresh_attributable_one_sided.", "topics": [ "change_Delta" ] }, { "page": "constant_effects_test", "title": "Asymptotic sensitivity analysis for weak nulls with continuous exposures assuming constant effects across matched sets.", "topics": [ "constant_effects_test" ] }, { "page": "dev_TV", "title": "Computes deviation from uniform distribution in total variation distance for a given amount of unmeasured confounding and a greater than alternative with a binary outcome.", "topics": [ "dev_TV" ] }, { "page": "dose_attributable_general", "title": "Inference for general attributable effects in sensitivity analysis with continuous exposures and binary outcomes. Gurobi must be installed to use this function.", "topics": [ "dose_attributable_general" ] }, { "page": "dose_sensitivity_mc_gen", "title": "Sharp null monte-carlo sensitivity analysis for continuous exposures and binary outcomes.", "topics": [ "dose_sensitivity_mc_gen" ] }, { "page": "dose_thresh_attributable_one_sided", "title": "Inference for threshold attributable effects in sensitivity analysis with continuous exposures and binary outcomes. Gurobi must be installed to use this function.", "topics": [ "dose_thresh_attributable_one_sided" ] }, { "page": "extract_above_threshold_vs_baseline", "title": "Compute average of outcomes above dose threshold minus average of outcomes.", "topics": [ "extract_above_threshold_vs_baseline" ] }, { "page": "extract_below_threshold_vs_baseline", "title": "Compute average of outcomes below dose threshold minus average of outcomes.", "topics": [ "extract_below_threshold_vs_baseline" ] }, { "page": "extract_max_vs_baseline", "title": "Compute largest dose outcome minus average of other outcomes.", "topics": [ "extract_max_vs_baseline" ] }, { "page": "extract_min_vs_baseline", "title": "Compute smallest dose outcome minus average of other outcomes.", "topics": [ "extract_min_vs_baseline" ] }, { "page": "extract_OLS", "title": "A function that returns the coefficient from regressing an outcome vector on a dose vector.", "topics": [ "extract_OLS" ] }, { "page": "extract_stochastic_intervention", "title": "Compute weighted sum of outcomes.", "topics": [ "extract_stochastic_intervention" ] }, { "page": "extract_threshold_effect", "title": "Compute difference in average outcomes above and below a dose threshold.", "topics": [ "extract_threshold_effect" ] }, { "page": "extract_threshold_effect_function", "title": "Function factory for extract_threshold_effect.", "topics": [ "extract_threshold_effect_function" ] }, { "page": "lead_bmd", "title": "Matched lead bone mineral density dataset", "topics": [ "lead_bmd" ] }, { "page": "lead_crime", "title": "Matched lead crime dataset", "topics": [ "lead_crime" ] }, { "page": "max_expectation", "title": "A function to compute a conservative upper bound on the worst-case expectation under the sharp null", "topics": [ "max_expectation" ] }, { "page": "max_ratio", "title": "Find the max ratio of probabilities between two permutations.", "topics": [ "max_ratio" ] }, { "page": "max_ratio_new", "title": "Find the max ratio of probabilities 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