{ "_id": "6a2a75f77b7a29ca60045263", "Package": "misc", "Title": "Miscellaneous Useful R Functions", "Version": "0.10.4", "Date": "2026-02-11", "Authors@R": "person(\"T.\", \"Moudiki\", , \"thierry.moudiki@gmail.com\", role = c(\"aut\", \"cre\"))", "Description": "Miscellaneous Useful R Functions.", "License": "MIT", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "Repository": "https://techtonique.r-universe.dev", "Date/Publication": "2026-02-11 06:42:08 UTC", "RemoteUrl": "https://github.com/thierrymoudiki/misc", "RemoteRef": "HEAD", "RemoteSha": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-11 08:42:19 UTC", "User": "root" }, "Author": "T. Moudiki [aut, cre]", "Maintainer": "T. Moudiki ", "MD5sum": "a8504cbccd79dc011afcd2e162ae0fd4", "_user": "techtonique", "_type": "src", "_file": "misc_0.10.4.tar.gz", "_fileid": "5abcb9732ee7dc122cb043f43fd47f07d21a173c5f1d461762924984d89ec3d9", "_filesize": 797582, "_sha256": "5abcb9732ee7dc122cb043f43fd47f07d21a173c5f1d461762924984d89ec3d9", "_created": "2026-06-11T08:42:19.000Z", "_published": "2026-06-11T08:46:47.288Z", "_distro": "noble", "_jobs": [ { "job": 80756309626, "time": 122, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "WARNING", "artifact": "7559168659" }, { "job": 80756309691, "time": 125, "config": "linux-release-x86_64", "r": "4.6.0", "check": "WARNING", "artifact": "7559170107" }, { "job": 80756309634, "time": 230, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "WARNING", "artifact": "7559209639" }, { "job": 80756309669, "time": 109, "config": "macos-release-arm64", "r": "4.6.0", "check": "WARNING", "artifact": "7559165343" }, { "job": 80755725530, "time": 192, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7559118989" }, { "job": 80756309633, "time": 109, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7559163568" }, { "job": 80756309662, "time": 89, "config": "windows-devel", "r": "4.7.0", "check": "WARNING", "artifact": "7559155462" }, { "job": 80756309673, "time": 80, "config": "windows-oldrel", "r": "4.5.3", "check": "WARNING", "artifact": "7559152296" }, { "job": 80756309676, "time": 96, "config": "windows-release", "r": "4.6.0", "check": "WARNING", "artifact": "7559158561" } ], "_buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/thierrymoudiki/misc", "_commit": { "id": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "author": "Thierry Moudiki ", "committer": "Thierry Moudiki ", "message": "add glmnet to suggests\n", "time": 1770792128 }, "_maintainer": { "name": "T. Moudiki", "email": "thierry.moudiki@gmail.com", "login": "thierrymoudiki", "linkedin": "in/thierry-moudiki", "twitter": "@sippingrizzly", "description": "Like everything related to tech, statistical/machine learning, computer simulation, numerical optimization. Other things too, not mentioned in this bio!", "uuid": 8895513 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "MASS", "role": "Depends" }, { "package": "stats", "role": "Depends" }, { "package": "utils", "role": "Depends" }, { "package": "foreach", "role": "Depends" }, { "package": "doSNOW", "role": "Depends" }, { "package": "snow", "role": "Depends" }, { "package": "knitr", "role": "Suggests" }, { "package": "ranger", "role": "Suggests" }, { "package": "xgboost", "role": "Suggests" }, { "package": "lightgbm", "role": "Suggests" }, { "package": "scam", "role": "Suggests" }, { "package": "survival", "role": "Suggests" }, { "package": "quantreg", "role": "Suggests" }, { "package": "tseries", "role": "Suggests" }, { "package": "glmnet", "role": "Suggests" } ], "_owner": "thierrymoudiki", "_selfowned": false, "_usedby": 1, "_updates": [ { "week": "2025-26", "n": 3 }, { "week": "2025-30", "n": 4 }, { "week": "2025-31", "n": 2 }, { "week": "2025-32", "n": 3 }, { "week": "2025-33", "n": 2 }, { "week": "2025-40", "n": 2 }, { "week": "2025-49", "n": 2 }, { "week": "2026-07", "n": 3 } ], "_tags": [], "_stars": 0, "_contributors": [ { "user": "thierrymoudiki", "count": 52, "uuid": 8895513 } ], "_userbio": { "uuid": 69663678, "type": "organization", "name": "Techtonique", "description": "tech, stats, machine learning, computer simulation, numerical optimization" }, "_downloads": { "count": 516, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/misc" }, "_devurl": "https://github.com/thierrymoudiki/misc", "_searchresults": 69, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/misc.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/kguidonimartins/misc", "_realowner": "kguidonimartins", "_cranurl": false, "_releases": [ { "version": "0.0.5", "date": "2026-04-04" } ], "_exports": [ "conformalize", "coverage_score", "debug_print", "direct_sampling", "fit_param_dist", "fit_predict_shap", "get_next_date", "gradient_shap", "integrate", "integrated_gradients", "is_package_available", "is_wholenumber", "KL_divergence_hist", "local_linear_shap", "monotonic_regression", "mspl", "one_hot_encode", "parfor", "plot_prediction_interval", "predict.conformalize", "print_shap_summary", "residuals.conformalize", "rm_zero_cols", "scale_matrix", "sims_to_forecast_object", "simulate.conformalize", "sort_df", "split_data", "splitts", "time_series_accuracy", "timeit", "vlookup", "winkler_score", "winkler_score2" ], "_help": [ { "page": "compute_finite_difference_gradients", "title": "Compute finite difference gradients", "topics": [ "compute_finite_difference_gradients" ] }, { "page": "compute_linear_shap", "title": "Compute SHAP values for linear models", "topics": [ "compute_linear_shap" ] }, { "page": "conformalize", "title": "Conformal Prediction for Regression Models", "topics": [ "conformalize" ] }, { "page": "correlation_aware_shap", "title": "Correlation-aware SHAP computation using sampling approach", "topics": [ "correlation_aware_shap" ] }, { "page": "coverage_score", "title": "Calculate Coverage Score for Prediction Intervals", "topics": [ "coverage_score" ] }, { "page": "debug_print", "title": "Debug print", "topics": [ "debug_print" ] }, { "page": "direct_sampling", "title": "Direct sampling", "topics": [ "direct_sampling" ] }, { "page": "extract_coefficients", "title": "Extract coefficients and intercept from various model types", "topics": [ "extract_coefficients" ] }, { "page": "fit_linear_model", "title": "Fit linear model based on specified type", "topics": [ "fit_linear_model" ] }, { "page": "fit_param_dist", "title": "Fit multiple parametric distributions, compute KL divergence, simulate best fit", "topics": [ "fit_param_dist" ] }, { "page": "fit_predict_shap", "title": "Fit a linear model and compute SHAP values with response centering", "topics": [ "fit_predict_shap" ] }, { "page": "get_model_predictions", "title": "Get predictions from various model types", "topics": [ "get_model_predictions" ] }, { "page": "get_model_type", "title": "Get model type string", "topics": [ "get_model_type" ] }, { "page": "get_next_date", "title": "Get next date for a time series object", "topics": [ "get_next_date" ] }, { "page": "gradient_shap", "title": "Gradient-Based SHAP for Any Differentiable Function", "topics": [ "gradient_shap" ] }, { "page": "integrate", "title": "Numerical Integration Using Trapezoidal or Simpson's Rule", "topics": [ "integrate" ] }, { "page": "integrated_gradients", "title": "Integrated Gradients for Model Explanations", "topics": [ "integrated_gradients" ] }, { "page": "is_package_available", "title": "Check if a package is available", "topics": [ "is_package_available" ] }, { "page": "is_wholenumber", "title": "Check if a number is a whole number", "topics": [ "is_wholenumber" ] }, { "page": "KL_divergence_hist", "title": "Function to calculate KL divergence for continuous distributions using histograms", "topics": [ "KL_divergence_hist" ] }, { "page": "local_linear_shap", "title": "LIME-style local linear approximation using gradients", "topics": [ "local_linear_shap" ] }, { "page": "monotonic_regression", "title": "Monotonic Regression using XGBoost, LightGBM, or SCAM", "topics": [ "monotonic_regression" ] }, { "page": "mspl", "title": "Mean Scaled Pinball Loss for Quantile Forecasts", "topics": [ "mspl" ] }, { "page": "one_hot_encode", "title": "One-hot encoding", "topics": [ "one_hot_encode" ] }, { "page": "parfor", "title": "Parallel For Loop with Progress Bar", "topics": [ "parfor" ] }, { "page": "plot_prediction_interval", "title": "Plot Prediction Interval With Optional Past Observations", "topics": [ "plot_prediction_interval" ] }, { "page": "predict.conformalize", "title": "Predict method for conformalize objects", "topics": [ "predict.conformalize" ] }, { "page": "print_shap_summary", "title": "Print summary of SHAP results", "topics": [ "print_shap_summary" ] }, { "page": "residuals.conformalize", "title": "Residuals method for conformalize objects", "topics": [ "residuals.conformalize" ] }, { "page": "rm_zero_cols", "title": "Removing columns containing only zeros", "topics": [ "rm_zero_cols" ] }, { "page": "rmultivariate", "title": "Simulate multivariate data", "topics": [ "rmultivariate" ] }, { "page": "scale_matrix", "title": "Scale matrix", "topics": [ "scale_matrix" ] }, { "page": "sims_to_forecast_object", "title": "Convert Simulation Matrix to Forecast Object", "topics": [ "sims_to_forecast_object" ] }, { "page": "simulate.conformalize", "title": "simulate method for conformalize objects", "topics": [ "simulate.conformalize" ] }, { "page": "sort_df", "title": "Sort data frame", "topics": [ "sort_df" ] }, { "page": "split_data", "title": "Split a dataset", "topics": [ "split_data" ] }, { "page": "splitts", "title": "Partition a time series object", "topics": [ "splitts" ] }, { "page": "time_series_accuracy", "title": "Calculate Multiple Accuracy Metrics for Forecasts", "topics": [ "time_series_accuracy" ] }, { "page": "timeit", "title": "Timing an expression", "topics": [ "timeit" ] }, { "page": "validate_shap_decomposition", "title": "Validate SHAP decomposition", "topics": [ "validate_shap_decomposition" ] }, { "page": "vlookup", "title": "VLOOKUP", "topics": [ "vlookup" ] }, { "page": "winkler_score", "title": "Winkler score for probabilistic forecasts", "topics": [ "winkler_score" ] }, { "page": "winkler_score2", "title": "Calculate Winkler Score for Prediction Intervals", "topics": [ "winkler_score2" ] } ], "_readme": "https://github.com/thierrymoudiki/misc/raw/HEAD/README.md", "_rundeps": [ "codetools", "doSNOW", "foreach", "iterators", "MASS", "snow" ], "_vignettes": [ { "source": "conformalize.Rmd", "filename": "conformalize.html", "title": "conformalize", "engine": "knitr::rmarkdown", "headings": [ "Example: Conformal Prediction with Out-of-Sample Coverage", "Simulated Data", "Fit Conformal Model", "Generate Predictions and Prediction Intervals", "Calculate Out-of-Sample Coverage Rate", "Results", "Example: Conformal Prediction with the MASS::Boston Dataset", "Load the Data", "Split the Data", "Fit Conformal Model 1", "Generate Predictions and Prediction Intervals 1", "Calculate Out-of-Sample Coverage Rate 1", "Fit Conformal Model 2", "Generate Predictions and Prediction Intervals 2", "Calculate Out-of-Sample Coverage Rate 2" ], "created": "2025-03-21 15:03:39", "modified": "2025-08-05 01:47:07", "commits": 5 }, { "source": "conformalize-matrix-interface.Rmd", "filename": "conformalize-matrix-interface.html", "title": "conformalize matrix interface", "engine": "knitr::rmarkdown", "headings": [ "Example: Conformal Prediction with Out-of-Sample Coverage", "Simulated Data", "Fit Conformal Model", "Generate Predictions and Prediction Intervals", "Calculate Out-of-Sample Coverage Rate", "Results", "Example: Conformal Prediction with the MASS::Boston Dataset", "Load the Data", "Split the Data", "Calculate Out-of-Sample Coverage Rate 1" ], "created": "2025-04-28 18:20:58", "modified": "2025-06-23 07:13:44", "commits": 2 }, { "source": "fit_param_dist.Rmd", "filename": "fit_param_dist.html", "title": "Fitting multiple parametric distributions to data and simulate best-fitting distribution", "engine": "knitr::rmarkdown", "headings": [ "Example usage 1", "Example usage 2", "Example usage 3", "Example usage 4" ], "created": "2024-10-01 18:27:15", "modified": "2024-10-01 18:27:15", "commits": 1 }, { "source": "getting-started.Rmd", "filename": "getting-started.html", "title": "Getting started", "engine": "knitr::rmarkdown", "headings": [], "created": "2024-09-14 16:33:59", "modified": "2024-09-14 16:33:59", "commits": 1 }, { "source": "getting-started-parfor.Rmd", "filename": "getting-started-parfor.html", "title": "Getting started with misc::parfor", "engine": "knitr::rmarkdown", "headings": [], "created": "2024-09-14 16:33:59", "modified": "2025-08-05 01:47:07", "commits": 2 }, { "source": "grad_shap.Rmd", "filename": "grad_shap.html", "title": "Gradient SHAP", "engine": "knitr::rmarkdown", "headings": [ "Example usage" ], "created": "2025-08-06 17:26:59", "modified": "2025-08-06 17:26:59", "commits": 1 }, { "source": "linear_shap.Rmd", "filename": "linear_shap.html", "title": "Linear SHAP", "engine": "knitr::rmarkdown", "headings": [ "Example usage" ], "created": "2025-08-05 07:43:12", "modified": "2025-08-05 07:43:12", "commits": 1 }, { "source": "monotonic.Rmd", "filename": "monotonic.html", "title": "Monotonic regression", "engine": "knitr::rmarkdown", "headings": [ "Example usage 1" ], "created": "2025-07-31 07:02:49", "modified": "2025-08-05 01:47:07", "commits": 2 }, { "source": "plot_pi.Rmd", "filename": "plot_pi.html", "title": "Plot prediction interval", "engine": "knitr::rmarkdown", "headings": [], "created": "2025-07-21 11:40:34", "modified": "2025-07-21 11:40:34", "commits": 1 }, { "source": "timeit.Rmd", "filename": "timeit.html", "title": "Timing R code", "engine": "knitr::rmarkdown", "headings": [ "Example usage 1", "Example usage 2" ], "created": "2024-10-15 07:25:34", "modified": "2024-10-15 07:25:34", "commits": 1 } ], "_score": 5.918030336784881, "_indexed": false, "_nocasepkg": "misc", "_universes": [ "techtonique" ], "_indexurl": "https://kguidonimartins.r-universe.dev/misc", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.10.4", "date": "2026-06-11T08:44:18.000Z", "distro": "noble", "commit": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "fileid": "b17ebfacaf9456e0035a482abc9cc2577ee53ed4fd6de794eff1f992e3d0b646", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677" }, { "r": "4.6.0", "os": "linux", "version": "0.10.4", "date": "2026-06-11T08:44:17.000Z", "distro": "noble", "commit": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "fileid": "c5e522b780ec3e54134193f571644abdc4e30bc1fa9224a6b8580a861de78266", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677" }, { "r": "4.5.3", "os": "mac", "version": "0.10.4", "date": "2026-06-11T08:44:18.000Z", "commit": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "fileid": "daba6ae956f8f974ae72b60e418dcdc5e907e7be84891404b1e529a6c43d48ac", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677" }, { "r": "4.6.0", "os": "mac", "version": "0.10.4", "date": "2026-06-11T08:44:11.000Z", "commit": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "fileid": "39db2acda1ed909bb7ce2c37dc97f23cad4cec9abf159f531765a0873dcf24f9", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677" }, { "r": "4.6.0", "os": "wasm", "version": "0.10.4", "date": "2026-06-11T08:44:23.000Z", "commit": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "fileid": "d1e5c580e13769528804b321d8e70a83ae84ff9b1ce658a306d46ea77169cc4b", "status": "success", "buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677" }, { "r": "4.7.0", "os": "win", "version": "0.10.4", "date": "2026-06-11T08:43:36.000Z", "commit": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "fileid": "d5dd168cd257d0dc318e945ff36a975a4158aec5acee882dca99d9198f89250c", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677" }, { "r": "4.5.3", "os": "win", "version": "0.10.4", "date": "2026-06-11T08:43:26.000Z", "commit": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "fileid": "4657412b8710a142ae9b0f8071626febb20f4810cc974abf1617f606f9fd3401", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677" }, { "r": "4.6.0", "os": "win", "version": "0.10.4", "date": "2026-06-11T08:43:42.000Z", "commit": "1f8759cfa700e61f75f270b78f5170422b9d8d0b", "fileid": "df81e6f72ffd4504daf9196a1c85868a48e43e95cfa19225abf48a3299ad1e8a", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/techtonique/actions/runs/27334683677" } ] }