{ "_id": "6a104088acfb0bcc41c9d23d", "Package": "baker", "Type": "Package", "Title": "\"Nested Partially Latent Class Models\"", "Version": "1.0.4.9000", "Date": "2025-12-11", "Authors@R": "c(\nperson(\"Zhenke\", \"Wu\", email=\"zhenkewu@gmail.com\",role=c(\"cre\",\"aut\",\"cph\"),\ncomment = c(ORCID = \"0000-0001-7582-669X\")),\nperson(\"Scott\", \"Zeger\", email=\"sz@jhu.edu\",role=\"aut\",\ncomment = c(ORCID = \"0000-0001-8907-1603\")),\nperson(\"John\", \"Muschelli\", email=\"muschellij2@gmail.com\", role=\"ctb\",\ncomment = c(ORCID = \"0000-0001-6469-1750\")),\nperson(\"Irena\", \"Chen\", email=\"irena@umich.edu\", role=\"ctb\",\ncomment = c(ORCID = \"0000-0002-9366-8506\"))\n)", "Description": "Provides functions to specify, fit and visualize nested\npartially-latent class models ( Wu, Deloria-Knoll, Hammitt, and\nZeger (2016) ; Wu, Deloria-Knoll, and\nZeger (2017) ; Wu and Chen\n(2021) ) for inference of population\ndisease etiology and individual diagnosis. In the motivating\nPneumonia Etiology Research for Child Health (PERCH) study,\nbecause both quantities of interest sum to one hundred percent,\nthe PERCH scientists frequently refer to them as population\netiology pie and individual etiology pie, hence the name of the\npackage.", "License": "MIT + file LICENSE", "Language": "en-US", "SystemRequirements": "JAGS (>= 4.3.2) (http://mcmc-jags.sourceforge.net)", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.3", "Encoding": "UTF-8", "URL": "https://github.com/zhenkewu/baker, https://zhenkewu.com/baker/", "BugReports": "https://github.com/zhenkewu/baker/issues", "Roxygen": "list(markdown = TRUE)", "Config/pak/sysreqs": "cmake make jags libicu-dev libuv1-dev libssl-dev\nzlib1g-dev", "Repository": "https://zhenkewu.r-universe.dev", "Date/Publication": "2025-12-12 19:29:25 UTC", "RemoteUrl": "https://github.com/zhenkewu/baker", "RemoteRef": "HEAD", "RemoteSha": "43c1dde892347e0b1908b67f06da66544eda881d", "NeedsCompilation": "no", "Packaged": 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are the same across 'Jcause' classes for each person (people may have different weights.)", "topics": [ "compute_marg_PR_nested_reg_array" ] }, { "page": "create_bugs_regressor_Eti", "title": "create regressor summation equation used in regression for etiology", "topics": [ "create_bugs_regressor_Eti" ] }, { "page": "create_bugs_regressor_FPR", "title": "create regressor summation equation used in regression for FPR", "topics": [ "create_bugs_regressor_FPR" ] }, { "page": "data_nplcm_noreg", "title": "Simulated dataset that is structured in the format necessary for an 'nplcm()' without regression", "topics": [ "data_nplcm_noreg" ] }, { "page": "data_nplcm_reg_nest", "title": "Simulated dataset that is structured in the format necessary for an 'nplcm()' with regression", "topics": [ "data_nplcm_reg_nest" ] }, { "page": "delete_start_with", "title": "Deletes a pattern from the start of a string, or each of a vector of strings.", "topics": [ "delete_start_with" ] }, { "page": 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}, { "page": "I2symb", "title": "Convert 0/1 coding to pathogen/combinations", "topics": [ "I2symb" ] }, { "page": "Imat2cat", "title": "Convert a matrix of binary indicators to categorical variables", "topics": [ "Imat2cat" ] }, { "page": "init_latent_jags_multipleSS", "title": "Initialize individual latent status (for 'JAGS')", "concept": [ "initialization functions" ], "topics": [ "init_latent_jags_multipleSS" ] }, { "page": "insert_bugfile_chunk_noreg_etiology", "title": "insert distribution for latent status code chunk into .bug file", "topics": [ "insert_bugfile_chunk_noreg_etiology" ] }, { "page": "insert_bugfile_chunk_noreg_meas", "title": "Insert measurement likelihood (without regression) code chunks into .bug model file", "topics": [ "insert_bugfile_chunk_noreg_meas" ] }, { "page": "insert_bugfile_chunk_reg_discrete_predictor_etiology", "title": "insert etiology regression for latent status code chunk into .bug file; discrete predictors", "topics": [ "insert_bugfile_chunk_reg_discrete_predictor_etiology" ] }, { "page": "insert_bugfile_chunk_reg_discrete_predictor_nonest_meas", "title": "Insert measurement likelihood (with regression; discrete) code chunks into .bug model file", "topics": [ "insert_bugfile_chunk_reg_discrete_predictor_nonest_meas" ] }, { "page": "insert_bugfile_chunk_reg_etiology", "title": "insert etiology regression for latent status code chunk into .bug file", "topics": [ "insert_bugfile_chunk_reg_etiology" ] }, { "page": "insert_bugfile_chunk_reg_nest_meas", "title": "Insert measurement likelihood (nested model+regression) code chunks into .bug model file", "topics": [ "insert_bugfile_chunk_reg_nest_meas" ] }, { "page": "insert_bugfile_chunk_reg_nonest_meas", "title": "Insert measurement likelihood (with regression) code chunks into .bug model file", "topics": [ "insert_bugfile_chunk_reg_nonest_meas" ] }, { "page": "is_discrete", "title": "Check if covariates are discrete", "topics": [ "is_discrete" ] }, { "page": "is_intercept_only", "title": "check if the formula is intercept only", "topics": [ "is_intercept_only" ] }, { "page": "is_jags_folder", "title": "See if a result folder is obtained by JAGS", "topics": [ "is_jags_folder" ] }, { "page": "is_length_all_one", "title": "check if a list has elements all of length one", "topics": [ "is_length_all_one" ] }, { "page": "is.error", "title": "Test for 'try-error' class", "topics": [ "is.error" ] }, { "page": "jags2_baker", "title": "Run 'JAGS' from R", "topics": [ "jags2_baker" ] }, { "page": "line2user", "title": "convert line to user coordinates", "topics": [ "line2user" ] }, { "page": "loadOneName", "title": "load an object from .RDATA file", "topics": [ "loadOneName" ] }, { "page": "logit", "title": "logit function", "topics": [ "logit" ] }, { "page": "logOR", "title": "calculate pairwise log odds ratios", "topics": [ "logOR" ] }, { "page": "logsumexp", "title": "log sum exp trick", "topics": [ "logsumexp" ] }, { "page": "lookup_quality", "title": "Get position to store in data_nplcm$Mobs:", "topics": [ "lookup_quality" ] }, { "page": "make_filename", "title": "Create new file name", "topics": [ "make_filename" ] }, { "page": "make_foldername", "title": "Create new folder name", "topics": [ "make_foldername" ] }, { "page": "make_list", "title": "Takes any number of R objects as arguments and returns a list whose names are derived from the names of the R objects.", "topics": [ "make_list" ] }, { "page": "make_meas_object", "title": "Make measurement slice", "concept": [ "data standardization functions" ], "topics": [ "make_meas_object" ] }, { "page": "make_numbered_list", "title": "Make a list with numbered names", "topics": [ "make_numbered_list" ] }, { "page": "make_template", "title": "make a mapping template for model fitting", "topics": [ "make_template" ] }, { "page": "marg_H", "title": "Shannon entropy for binary data", "topics": [ "marg_H" ] }, { "page": "match_cause", "title": "Match latent causes that might have the same combo but different specifications", "topics": [ "match_cause" ] }, { "page": "merge_lists", "title": "For a list of many sublists each of which has matrices as its member, we combine across the many sublists to produce a final list", "concept": [ "data operation functions" ], "topics": [ "merge_lists" ] }, { "page": "my_reorder", "title": "Reorder the measurement dimensions to match the order for display", "topics": [ "my_reorder" ] }, { "page": "NA2dot", "title": "convert 'NA' to '.'", "topics": [ "NA2dot" ] }, { "page": "nplcm", "title": "Fit nested partially-latent class models (highest-level wrapper function)", "topics": [ "nplcm" ] }, { "page": "nplcm_fit_NoReg", "title": "Fit nested partially-latent class model (low-level)", "concept": [ "model fitting functions" ], "topics": [ "nplcm_fit_NoReg" ] }, { "page": "nplcm_fit_Reg_discrete_predictor_NoNest", "title": "Fit nested partially-latent class model with regression (low-level)", "concept": [ "model fitting functions" ], "topics": [ "nplcm_fit_Reg_discrete_predictor_NoNest" ] }, { "page": "nplcm_fit_Reg_Nest", "title": "Fit nested partially-latent class model with regression (low-level)", "concept": [ "model fitting functions" ], "topics": [ "nplcm_fit_Reg_Nest" ] }, { "page": "nplcm_fit_Reg_NoNest", "title": "Fit nested partially-latent class model with regression (low-level)", "concept": [ "model fitting functions" ], "topics": [ "nplcm_fit_Reg_NoNest" ] }, { "page": "nplcm_read_folder", "title": "Read data and other model information from a folder that stores model results.", "topics": [ "nplcm_read_folder" ] }, { "page": "null_as_zero", "title": "Convert 'NULL' to zero.", "topics": [ "null_as_zero" ] }, { "page": "order_post_eti", "title": "order latent status by posterior mean", "topics": [ "order_post_eti" ] }, { "page": "overall_uniform", "title": "specify overall uniform (symmetric Dirichlet distribution) for etiology prior", "concept": [ "prior specification functions" ], "topics": [ "overall_uniform" ] }, { "page": "parse_nplcm_reg", "title": "parse regression components (either false positive rate or etiology regression) for fitting npLCM; Only use this when formula is not 'NULL'.", "topics": [ "parse_nplcm_reg" ] }, { "page": "pathogen_category_perch", "title": "pathogens and their categories in PERCH study (virus or bacteria)", "topics": [ "pathogen_category_perch" ] }, { "page": "pathogen_category_simulation", "title": "Hypothetical pathogens and their categories (virus or bacteria)", "topics": [ "pathogen_category_simulation" ] }, { "page": "plot_BrS_panel", "title": "Plot bronze-standard (BrS) panel", "concept": [ "visualization functions" ], "topics": [ "plot_BrS_panel" ] }, { "page": "plot_case_study", "title": "visualize the PERCH etiology regression with a continuous covariate", "topics": [ "plot_case_study" ] }, { "page": "plot_check_common_pattern", "title": "Posterior predictive checking for the nested partially class models - frequent patterns in the BrS data. (for multiple folders)", "concept": [ "model generating functions", "visualization functions" ], "topics": [ "plot_check_common_pattern" ] }, { "page": "plot_check_pairwise_SLORD", "title": "Posterior predictive checking for nested partially latent class models - pairwise log odds ratio (only for bronze-standard data)", "concept": [ "model checking functions", "visualization functions" ], "topics": [ "plot_check_pairwise_SLORD" ] }, { "page": "plot_etiology_regression", "title": "visualize the etiology regression with a continuous covariate", "concept": [ "visualization functions" ], "topics": [ "plot_etiology_regression" ] }, { "page": "plot_etiology_strat", "title": "visualize the etiology estimates for each discrete levels", "concept": [ "visualization functions" ], "topics": [ "plot_etiology_strat" ] }, { "page": "plot_leftmost", "title": "plotting the labels on the left margin for panels plot", "topics": [ "plot_leftmost" ] }, { "page": "plot_logORmat", "title": "Visualize pairwise log odds ratios (LOR) for data that are available in both cases and controls", "concept": [ "exploratory data analysis functions" ], "topics": [ "plot_logORmat" ] }, { "page": "plot_panels", "title": "Plot three-panel figures for nested partially-latent model results", "concept": [ "visualization functions" ], "topics": [ "plot_panels" ] }, { "page": "plot_pie_panel", "title": "Plot etiology (pie) panel", "concept": [ "visualization functions" ], "topics": [ "plot_pie_panel" ] }, { "page": "plot_SS_panel", "title": "Plot silver-standard (SS) panel", "concept": [ "visualization functions" ], "topics": [ "plot_SS_panel" ] }, { "page": "plot_subwt_regression", "title": "visualize the subclass weight regression with a continuous covariate", "concept": [ "visualization functions" ], "topics": [ "plot_subwt_regression" ] }, { "page": "plot.nplcm", "title": "'plot.nplcm' plot the results from 'nplcm()'.", "concept": [ "visualization functions" ], "topics": [ "plot.nplcm" ] }, { "page": "print.nplcm", "title": "'print.nplcm' summarizes the results from 'nplcm()'.", "concept": [ "nplcm results" ], "topics": [ "print.nplcm" ] }, { "page": "print.summary.nplcm.no_reg", "title": "Compact printing of 'nplcm()' model fits", "concept": [ "nplcm results" ], "topics": [ "print.summary.nplcm.no_reg" ] }, { "page": "print.summary.nplcm.reg_nest", "title": "Compact printing of 'nplcm()' model fits", "concept": [ "nplcm results" ], "topics": [ "print.summary.nplcm.reg_nest" ] }, { "page": "print.summary.nplcm.reg_nest_strat", "title": "Compact printing of 'nplcm()' model fits", "concept": [ "nplcm results" ], "topics": [ "print.summary.nplcm.reg_nest_strat" ] }, { "page": "print.summary.nplcm.reg_nonest", "title": "Compact printing of 'nplcm()' model fits", "concept": [ "nplcm results" ], "topics": [ "print.summary.nplcm.reg_nonest" ] }, { "page": "print.summary.nplcm.reg_nonest_strat", "title": "Compact printing of 'nplcm()' model fits", "concept": [ "nplcm results" ], "topics": [ "print.summary.nplcm.reg_nonest_strat" ] }, { "page": "read_meas_object", "title": "Read measurement slices", "concept": [ "raw data importing functions" ], "topics": [ "read_meas_object" ] }, { "page": "rvbern", "title": "Sample a vector of Bernoulli variables.", "topics": [ "rvbern" ] }, { "page": "s_date_Eti", "title": "Make Etiology design matrix for dates with R format.", "topics": [ "s_date_Eti" ] }, { "page": "s_date_FPR", "title": "Make false positive rate (FPR) design matrix for dates with R format.", "topics": [ "s_date_FPR" ] }, { "page": "set_prior_tpr_BrS_NoNest", "title": "Set true positive rate (TPR) prior ranges for bronze-standard (BrS) data", "concept": [ "prior specification functions" ], "topics": [ "set_prior_tpr_BrS_NoNest" ] }, { "page": "set_prior_tpr_SS", "title": "Set true positive rate (TPR) prior ranges for silver-standard data.", "concept": [ "prior specification functions" ], "topics": [ "set_prior_tpr_SS" ] }, { "page": "set_strat", "title": "Stratification setup by covariates", "topics": [ "set_strat" ] }, { "page": "show_dep", "title": "Show function dependencies", "topics": [ "show_dep" ] }, { "page": "show_individual", "title": "get an individual's data from the output of 'clean_perch_data()'", "concept": [ "exploratory data analysis functions" ], "topics": [ "show_individual" ] }, { "page": "simulate_brs", "title": "Simulate Bronze-Standard (BrS) Data", "concept": [ "internal simulation functions" ], "topics": [ "simulate_brs" ] }, { "page": "simulate_latent", "title": "Simulate Latent Status:", "concept": [ "internal simulation functions" ], "topics": [ "simulate_latent" ] }, { "page": "simulate_nplcm", "title": "Simulate data from nested partially-latent class model (npLCM) family", "concept": [ "simulation functions" ], "topics": [ "simulate_nplcm" ] }, { "page": "simulate_ss", "title": "Simulate Silver-Standard (SS) Data", "concept": [ "internal simulation functions" ], "topics": [ "simulate_ss" ] }, { "page": "softmax", "title": "softmax", "topics": [ "softmax" ] }, { "page": "subset_data_nplcm_by_index", "title": "subset data from the output of 'clean_perch_data()'", "concept": [ "data operation functions" ], "topics": [ "subset_data_nplcm_by_index" ] }, { "page": "summarize_BrS", "title": "summarize bronze-standard data", "concept": [ "exploratory data analysis functions" ], "topics": [ "summarize_BrS" ] }, { "page": "summarize_SS", "title": "silver-standard data summary", "concept": [ "exploratory data analysis functions" ], "topics": [ "summarize_SS" ] }, { "page": "summary.nplcm", "title": "'summary.nplcm' summarizes the results from 'nplcm()'.", "concept": [ "nplcm results" ], "topics": [ "summary.nplcm" ] }, { "page": "sym_diff_month", "title": "get symmetric difference of months from two vector of R-format dates", "topics": [ "sym_diff_month" ] }, { "page": "symb2I", "title": "Convert names of pathogen/combinations into 0/1 coding", "topics": [ "symb2I" ] }, { "page": "tsb", "title": "generate stick-breaking prior (truncated) from a vector of random probabilities", "topics": [ "tsb" ] }, { "page": "unfactor", "title": "Convert factor to numeric without losing information on the label", "topics": [ "unfactor" ] }, { "page": "unique_cause", "title": "get unique causes, regardless of the actual order in combo", "topics": [ "unique_cause" ] }, { "page": "unique_month", "title": "Get unique month from Date", "topics": [ "unique_month" ] }, { "page": "visualize_case_control_matrix", "title": "Visualize matrix for a quantity measured on cases and controls (a single number)", "topics": [ "visualize_case_control_matrix" ] }, { "page": "visualize_season", "title": "visualize trend of pathogen observation rate for NPPCR data (both cases and controls)", "concept": [ "exploratory data analysis functions" ], "topics": [ "visualize_season" ] }, { "page": "write_model_NoReg", "title": "Write .bug model file for model without regression", "concept": [ "model generation functions" ], "topics": [ "write_model_NoReg" ] }, { "page": "write_model_Reg_discrete_predictor_NoNest", "title": "Write .bug model file for regression model without nested subclasses", "concept": [ "model generation functions" ], "topics": [ "write_model_Reg_discrete_predictor_NoNest" ] }, { "page": "write_model_Reg_Nest", "title": "Write '.bug' model file for regression model WITH nested subclasses", "concept": [ "model generation functions" ], "topics": [ "write_model_Reg_Nest" ] }, { "page": "write_model_Reg_NoNest", "title": "Write .bug model file for regression model without nested subclasses", "concept": [ "model generation functions" ], "topics": [ "write_model_Reg_NoNest" ] }, { "page": "write.model", "title": "function to write bugs model (copied from R2WinBUGS)", "topics": [ "write.model" ] } ], "_readme": "https://github.com/zhenkewu/baker/raw/HEAD/README.md", "_rundeps": [ "abind", "ash", "backports", "base64enc", "bbotk", "binom", "bitops", "boot", "broom", "bslib", 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"version": "4.3.2-2.2404.0", "name": "jags", "homepage": "https://mcmc-jags.sourceforge.io", "description": "Just Another Gibbs Sampler for Bayesian MCMC - binary\nJAGS is Just Another Gibbs Sampler. It is a program for analysis of\nBayesian hierarchical models using Markov Chain Monte Carlo (MCMC)\nsimulation not wholly unlike BUGS.\n\nJAGS was written with three aims in mind:\n* To have an engine for the BUGS language that runs on Unix\n* To be extensible, allowing users to write their own functions,\ndistributions and samplers.\n* To be a plaftorm for experimentation with ideas in Bayesian modelling\n\nThis package contains the 'jags' binary as well as the associated\nshared library modules loaded by the binary." }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "baker_demo.Rmd", "filename": "baker_demo.html", "title": "Vignettes for baker: An R package for fitting nested partially latent class models", "author": "Zhenke Wu", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Setup and Simulate Measurements", "Exploratory data analysis", "Model specification", "Fitting the model", "Visualizations", "plot data, prior, and posteriors for all causative pathogens:", "plot data, prior, and posterior for selected causes:", "plot the joint posterior of the etiology fraction for any three causes:", "model checking by comparing observed pairwise log odds ratios (LOR)", "model checking by comparing observed frequencies of binary patterns to the model-predicted ones" ], "created": "2017-02-16 19:05:57", "modified": "2022-02-01 16:55:46", "commits": 13 } ], "_score": 5.7737864449811935, "_indexed": true, "_nocasepkg": "baker", "_universes": [ "zhenkewu" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.0.4.9000", "date": "2026-05-11T08:24:14.000Z", "distro": "noble", "commit": "43c1dde892347e0b1908b67f06da66544eda881d", "fileid": "0b6208074a67de89f41435f81a83488d7b3fbff1d9215f841d00710ffa778f52", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/zhenkewu/actions/runs/25658468903" }, { "r": "4.6.0", "os": "linux", "version": "1.0.4.9000", "date": "2026-05-11T08:23:34.000Z", "distro": "noble", "commit": "43c1dde892347e0b1908b67f06da66544eda881d", "fileid": "9c491c923a2295983d37512c1e4f27d28c0174eab1d4591a505f0701ce529816", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/zhenkewu/actions/runs/25658468903" }, { "r": "4.5.3", "os": "mac", "version": "1.0.4.9000", "date": "2026-05-11T08:23:19.000Z", "commit": "43c1dde892347e0b1908b67f06da66544eda881d", "fileid": "e0696f121bcdabb074c5753263ad6b077b12d488f704da0c8646047fb477c446", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/zhenkewu/actions/runs/25658468903" }, { "r": "4.6.0", "os": "mac", "version": "1.0.4.9000", "date": "2026-05-11T08:22:31.000Z", "commit": "43c1dde892347e0b1908b67f06da66544eda881d", "fileid": "9894c79637195f738c8a5fbe81e5d7145c5853ac60ce538385faefcd6a1c7f22", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/zhenkewu/actions/runs/25658468903" }, { "r": "4.7.0", "os": "win", "version": "1.0.4.9000", "date": "2026-05-11T08:22:58.000Z", "commit": "43c1dde892347e0b1908b67f06da66544eda881d", "fileid": "0676f80474e7869c4ba85b22202d0f50b72b45b5d286b398f0778abc4827b922", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/zhenkewu/actions/runs/25658468903" }, { "r": "4.5.3", "os": "win", "version": "1.0.4.9000", "date": "2026-05-11T08:22:00.000Z", "commit": "43c1dde892347e0b1908b67f06da66544eda881d", "fileid": "dbda8268f8fc5907c94865984cf84439ec790164ab303355795ad37b5c716f39", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/zhenkewu/actions/runs/25658468903" }, { "r": "4.6.0", "os": "win", "version": "1.0.4.9000", "date": "2026-05-11T08:22:02.000Z", "commit": "43c1dde892347e0b1908b67f06da66544eda881d", "fileid": "ed580f2dab0e1d2360a89bc32c67101b2265ce4337078dabb50ee2fc4adbc726", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/zhenkewu/actions/runs/25658468903" }, { "r": "4.6.0", "os": "wasm", "version": "1.0.4.9000", "date": "2026-05-22T11:39:26.000Z", "commit": "43c1dde892347e0b1908b67f06da66544eda881d", "fileid": "d0866e00118d724e2ee3fc31e4d91f59e524d918f01c7b4ef60dbc6e0f714d87", "status": "success", "buildurl": "https://github.com/r-universe/zhenkewu/actions/runs/25658468903" } ] }