{ "_id": "6a20eeeacd65a98ecbd1b3cd", "Package": "drugDemand", "Type": "Package", "Title": "Drug Demand Forecasting", "Version": "0.1.3", "Date": "2024-02-27", "Authors@R": "person(\"Kaifeng\", \"Lu\", , \"kaifenglu@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-6160-7119\"))", "Description": "Performs drug demand forecasting by modeling drug\ndispensing data while taking into account predicted enrollment\nand treatment discontinuation dates. The gap time between\nrandomization and the first drug dispensing visit is modeled\nusing interval-censored exponential, Weibull, log-logistic, or\nlog-normal distributions (Anderson-Bergman (2017)\n). The number of skipped visits is\nmodeled using Poisson, zero-inflated Poisson, or negative\nbinomial distributions (Zeileis, Kleiber & Jackman (2008)\n). The gap time between two\nconsecutive drug dispensing visits given the number of skipped\nvisits is modeled using linear regression based on least\nsquares or least absolute deviations (Birkes & Dodge (1993,\nISBN:0-471-56881-3)). The number of dispensed doses is modeled\nusing linear or linear mixed-effects models (McCulloch & Searle\n(2001, ISBN:0-471-19364-X)).", "License": "GPL (>= 2)", "RoxygenNote": "7.3.0", "Roxygen": "list(markdown = TRUE)", "Encoding": "UTF-8", "NeedsCompilation": "yes", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev\nzlib1g-dev", "Repository": "https://kaifenglu.r-universe.dev", "Date/Publication": "2024-02-27 23:11:29 UTC", "RemoteUrl": "https://github.com/kaifenglu/drugdemand", "RemoteRef": "HEAD", "RemoteSha": "9ccfcbc0d55bfb9a630c084ecdb3b18c9ee28652", "Packaged": { "Date": "2026-06-04 03:14:41 UTC", "User": "root" }, "Author": "Kaifeng Lu [aut, cre] (ORCID: )", "Maintainer": "Kaifeng Lu ", "MD5sum": "0ef1494bf0bf50e556799109db4db481", "_user": "kaifenglu", "_type": "src", "_file": "drugDemand_0.1.3.tar.gz", "_fileid": "431172903bd2138dd0d9969406ad8a82059eb8a03f5fab293792f46274d325ac", "_filesize": 106188, "_sha256": "431172903bd2138dd0d9969406ad8a82059eb8a03f5fab293792f46274d325ac", "_created": "2026-06-04T03:14:41.000Z", "_published": "2026-06-04T03:20:09.958Z", "_distro": "noble", "_jobs": [ { "job": 79442121829, "time": 177, "config": "linux-devel-arm64", "r": "4.7.0", "check": "WARNING", "artifact": "7402174687" }, { "job": 79442121842, "time": 194, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "WARNING", "artifact": "7402178187" }, { "job": 79442121825, "time": 181, "config": "linux-release-arm64", "r": "4.6.0", "check": "WARNING", "artifact": "7402175525" }, { "job": 79442121819, "time": 214, "config": "linux-release-x86_64", "r": "4.6.0", "check": "WARNING", "artifact": "7402182340" }, { "job": 79442121828, "time": 118, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "WARNING", "artifact": "7402161895" }, { "job": 79442121815, "time": 277, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "WARNING", "artifact": "7402196002" }, { "job": 79442121844, "time": 115, 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"https://github.com/kaifenglu/drugdemand", "_commit": { "id": "9ccfcbc0d55bfb9a630c084ecdb3b18c9ee28652", "author": "Kaifeng Lu ", "committer": "Kaifeng Lu ", "message": "- remove the dependence on pscl and icenReg packages\n- add stringr to imported packages\n- add survival::survreg and stats::optim to imported functions\n- use the Brent method for optimization to fit an interval-censored exponential distribution to the time from randomization to the first drug dispensing visit\n- check the number of unique combinations of time and k1 in f_fit_ti\n- use the VarCorr function from the nlme package to obtain the variance of random effects from the lme function call\n- use data from all kit types to inform the common time model\n- add the pred_pp_only parameter to f_drug_demand to make protocol-based predictions only\n- ensure that df and visitview have all the required columns and none of the required columns have missing values\n- combine dosing_pred_df and dosing_pred_pp in the f_drug_demand output\n- handle cases where all patients in a treatment arm discontinued before the cutoff\n- replace round with formatC to retain the zeros after the decimal point\n- rename fit_xx to xx_fit, where xx = k0, t0, t1, ki, ti, di to be consistent with enroll_fit and event_fit naming convention\n- add kit and kit_name to handle cases when a drug has different kit types depending on the site\n- add the prior probability of different kit types within a drug at the design stage\n- streamline the examples through the use of function calls\n- update the initial parameter values in f_fit_ki for zero-inflated Poisson model\n- update the p.fit calculation in f_fit_ti when using the least absolute deviations model\n- convert kit_name to a factor to ensure the correct order when creating a plot\n- add kit_name as the sub plot title in f_dispensing_models.R\n- add the vf_kit parameter to f_dose_draw_1 to get the kit information for each subject in each simulation draw\n- add the f_ongoing_new function to prepare the dosing data sets to impute for ongoing and new subjects\n- add kit_description_df to the output of f_dose_observed\n- add the rdirichlet function to generate cell probab", "time": 1709075489 }, "_maintainer": { "name": "Kaifeng Lu", "email": "kaifenglu@gmail.com", "login": "kaifenglu", "description": "", "uuid": 95114218, "orcid": "0000-0002-6160-7119" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "Rcpp", "version": ">= 1.0.10", "role": "Imports" }, { "package": "dplyr", "version": ">= 1.1.0", "role": "Imports" }, { "package": "rlang", "version": ">= 1.1.0", "role": "Imports" }, { "package": "purrr", "version": ">= 1.0.2", "role": "Imports" }, { "package": "stringr", "version": ">= 1.4.0", "role": "Imports" }, { "package": "plotly", "version": ">= 4.10.1", "role": "Imports" }, { "package": "survival", "version": ">= 2.41-3", "role": 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