{ "_id": "6a47aafce06c18f0a651a02f", "Package": "rtemisalpha", "Version": "0.98.3", "Title": "Machine Learning and Visualization", "Date": "2025-01-26", "Authors@R": "person(given = \"E.D.\", family = \"Gennatas\", role = c(\"aut\", \"cre\"), email = \"gennatas@gmail.com\", comment = c(ORCID = \"0000-0001-9280-3609\"))", "Description": "Advanced Machine Learning and Visualization. Unsupervised\nLearning (Clustering, Decomposition), Supervised Learning\n(Classification, Regression), Cross-Decomposition, Bagging,\nBoosting, Meta-models. Static and interactive graphics.", "License": "GPL (>= 3)", "URL": "https://www.rtemis.org", "BugReports": "https://github.com/rtemis-org/rtemis-legacy/issues", "ByteCompile": "yes", "Encoding": "UTF-8", "Config/testthat/edition": "3", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "LazyData": "true", "Repository": "https://egenn.r-universe.dev", "Date/Publication": "2025-07-09 18:14:06 UTC", "RemoteUrl": "https://github.com/rtemis-org/rtemis-legacy", "RemoteRef": "HEAD", "RemoteSha": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-03 12:19:21 UTC", "User": "root" }, "Author": "E.D. Gennatas [aut, cre] (ORCID:\n)", "Maintainer": "E.D. Gennatas ", "_user": "egenn", "_type": "src", "_file": "rtemisalpha_0.98.3.tar.gz", "_fileid": "https://r2.ropensci.org/aadfb7b7fa4f659ed4623c4e1c1a5eb0bed3928a7cf45bc0ed1974cdc59f1111", "_filesize": 3013850, "_sha256": "aadfb7b7fa4f659ed4623c4e1c1a5eb0bed3928a7cf45bc0ed1974cdc59f1111", "_expires": "2026-10-11T12:28:43.000Z", "_created": "2026-07-03T12:19:21.000Z", "_published": "2026-07-03T12:28:44.099Z", "_jobs": [ { "job": 84998603538, "time": 379, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "WARNING", "artifact": "8066438624" }, { "job": 84998603513, "time": 365, "config": "linux-release-x86_64", "r": "4.6.1", "check": "WARNING", "artifact": "8066433697" }, { "job": 84998603543, "time": 160, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "WARNING", "artifact": "8066385523" }, { "job": 84998603561, "time": 279, "config": "macos-release-arm64", "r": "4.6.1", "check": "WARNING", "artifact": "8066466676" }, { "job": 84997565356, "time": 389, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "8066314854" }, { "job": 84998603507, "time": 278, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "8066404716" }, { "job": 84998603558, "time": 379, "config": "windows-devel", "r": "4.7.0", "check": "WARNING", "artifact": "8066438444" }, { "job": 84998603590, "time": 309, "config": "windows-oldrel", "r": "4.5.3", "check": "WARNING", "artifact": "8066415337" }, { "job": 84998603568, "time": 301, "config": "windows-release", "r": "4.6.1", "check": "WARNING", "artifact": "8066412259" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115", "_status": "success", "_upstream": "https://github.com/rtemis-org/rtemis-legacy", "_commit": { "id": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "author": "Stathis Gennatas ", "committer": "Stathis Gennatas ", "message": "cleanup\n", "time": 1752084846 }, "_maintainer": { "name": "E.D. Gennatas", "email": "gennatas@gmail.com", "login": "egenn", "description": "AI/ML for Precision Medicine & Public Health, Health Data Science; Neuroscience; Visualization; Music", "uuid": 6411723, "orcid": "0000-0001-9280-3609" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "grDevices", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "data.table", "role": "Imports" }, { "package": "R6", "role": "Imports" }, { "package": "future", "role": "Imports" }, { "package": "htmltools", "role": "Imports" }, { "package": "ada", "role": "Suggests" }, { "package": "arm", "role": "Suggests" }, { "package": "arrow", "role": "Suggests" }, { "package": "bartMachine", "role": "Suggests" }, { "package": "base64enc", "role": "Suggests" }, { "package": "bit64", "role": "Suggests" }, { "package": "dbscan", "role": "Suggests" }, { "package": "car", "role": "Suggests" }, { "package": "cluster", "role": "Suggests" }, { "package": "colorspace", "role": "Suggests" }, { "package": "C50", "role": "Suggests" }, { "package": "data.tree", "role": "Suggests" }, { "package": "DBI", "role": "Suggests" }, { "package": "dendextend", "version": ">= 0.18.0", "role": "Suggests" }, { "package": "DiagrammeR", "role": "Suggests" }, { "package": "DiagrammeRsvg", "role": "Suggests" }, { "package": "doParallel", "role": "Suggests" }, { "package": "dplyr", "role": "Suggests" }, { "package": "duckdb", "role": "Suggests" }, { "package": "earth", "role": "Suggests" }, { "package": "EMCluster", "role": "Suggests" }, { "package": "evtree", "role": "Suggests" }, { "package": "e1071", "role": "Suggests" }, { "package": "fansi", "role": "Suggests" }, { "package": "fastICA", "role": "Suggests" }, { "package": "flexclust", "role": "Suggests" }, { "package": "FNN", "role": "Suggests" }, { "package": "fpc", "role": "Suggests" }, { "package": "fs", "role": "Suggests" }, { "package": "future.apply", "role": "Suggests" }, { "package": "gbm", "role": "Suggests" }, { "package": "geojsonio", "role": "Suggests" }, { "package": "geosphere", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "glmnet", "role": "Suggests" }, { "package": "GPArotation", "role": "Suggests" }, { "package": "grid", "role": "Suggests" }, { "package": "gsubfn", "role": "Suggests" }, { "package": "hal9001", "role": "Suggests" }, { "package": "haven", "role": "Suggests" }, { "package": "heatmaply", "role": "Suggests" }, { "package": "hopach", "role": "Suggests" }, { "package": "htmlwidgets", "role": "Suggests" }, { "package": "h2o", "role": "Suggests" }, { "package": "ica", "role": "Suggests" }, { "package": "igraph", "role": "Suggests" }, { "package": "imager", "role": "Suggests" }, { "package": "inTrees", "role": "Suggests" }, { "package": "JuliaCall", "role": "Suggests" }, { "package": "jsonlite", "role": "Suggests" }, { "package": "keras", "role": "Suggests" }, { "package": "kernlab", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "lars", "role": "Suggests" }, { "package": "lavaan", "role": "Suggests" }, { "package": "leaflet", "role": "Suggests" }, { "package": "leaps", "role": "Suggests" }, { "package": "lightgbm", "role": "Suggests" }, { "package": "matrixStats", "role": "Suggests" }, { "package": "MASS", "role": "Suggests" }, { "package": "mda", "role": "Suggests" }, { "package": "meanShiftR", "role": "Suggests" }, { "package": "mgcv", "role": "Suggests" }, { "package": "mlbench", "role": "Suggests" }, { "package": "mice", "role": "Suggests" }, { "package": "missForest", "role": "Suggests" }, { "package": "missRanger", "role": "Suggests" }, { "package": "networkD3", "role": "Suggests" }, { "package": "nlme", "role": "Suggests" }, { "package": "nnet", "role": "Suggests" }, { "package": "NMF", "role": "Suggests" }, { "package": "np", "role": "Suggests" }, { "package": "nsprcomp", "role": "Suggests" }, { "package": "openxlsx", "role": "Suggests" }, { "package": "partykit", "role": "Suggests" }, { "package": "pbapply", "role": "Suggests" }, { "package": "plotly", "role": "Suggests" }, { "package": "polspline", "role": "Suggests" }, { "package": "pROC", "role": "Suggests" }, { "package": "progress", "role": "Suggests" }, { "package": "progressr", "role": "Suggests" }, { "package": "psych", "role": "Suggests" }, { "package": "PMA", "role": "Suggests" }, { "package": "png", "role": "Suggests" }, { "package": "plotrix", "role": "Suggests" }, { "package": "plyr", "role": "Suggests" }, { "package": "pmml", "role": "Suggests" }, { "package": "PRROC", "role": "Suggests" }, { "package": "pvclust", "role": "Suggests" }, { "package": "qrnn", "role": "Suggests" }, { "package": "randomForest", "role": "Suggests" }, { "package": "randomForestSRC", "role": "Suggests" }, { "package": "ranger", "role": "Suggests" }, { "package": "RDRToolbox", "role": "Suggests" }, { "package": "reactable", "role": "Suggests" }, { "package": "RColorBrewer", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "ROCR", "role": "Suggests" }, { "package": "rpart", "role": "Suggests" }, { "package": "rpart.plot", "role": "Suggests" }, { "package": "rstudioapi", "role": "Suggests" }, { "package": "Rtsne", "role": "Suggests" }, { "package": "rsvg", "role": "Suggests" }, { "package": "R.utils", "role": "Suggests" }, { "package": "scales", "version": ">= 0.2.5", "role": "Suggests" }, { "package": "seqinr", "role": "Suggests" }, { "package": "sgd", "role": "Suggests" }, { "package": "sparklyr", "role": "Suggests" }, { "package": "sparseLDA", "role": "Suggests" }, { "package": "splines2", "role": "Suggests" }, { "package": "spls", "role": "Suggests" }, { "package": "survival", "role": "Suggests" }, { "package": "tensorflow", "role": "Suggests" }, { "package": "tgp", "role": "Suggests" }, { "package": "threejs", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "usmap", "role": "Suggests" }, { "package": "uwot", "role": "Suggests" }, { "package": "vegan", "role": "Suggests" }, { "package": "visNetwork", "role": "Suggests" }, { "package": "vroom", "role": "Suggests" }, { "package": "xgboost", "role": "Suggests" } ], "_owner": "rtemis-org", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-28", "n": 2 } ], "_tags": [], "_stars": 0, "_contributors": [ { "user": "egenn", "count": 4, "uuid": 6411723 } ], "_userbio": { "uuid": 6411723, "type": "user", "name": "Stathis Gennatas", "followers": 66, "description": "AI/ML for Precision Medicine & Public Health, Health Data Science; Neuroscience; Visualization; Music" }, "_downloads": { "count": 0, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/rtemisalpha" }, "_devurl": "https://github.com/rtemis-org/rtemis-legacy", "_searchresults": 0, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/rtemisalpha.html", "manual.pdf" ], "_cranurl": false, "_exports": [ "%BC%", "any_constant", "as.data.tree.rpart", "auc", "auc_pairs", "bacc", "betas.lihad", "bias_variance", "binmat2vec", "boost", "bootstrap", "brier_score", "c_CMeans", "c_DBSCAN", "c_EMC", "c_H2OKMeans", "c_HARDCL", "c_HOPACH", "c_KMeans", "c_MeanShift", "c_NGAS", "c_PAM", "c_PAMK", "c_SPEC", "calibrate", "calibrate_cv", "cartLiteBoostTV", "catrange", "catsize", "check_data", "check_files", "checkpoint_earlystop", "chill", "class_error", "class_imbalance", "clean_colnames", "clean_names", "clust", "col2grayscale", "col2hex", "colMax", "color_fade", "color_invertRGB", "color_mean", "color_order", "color_separate", "color_sqdist", "colorAdjust", "colorGrad", "colorGrad.x", "colorgradient.x", "colorMix", "colorOp", "cols2list", "crules", "d_H2OAE", "d_H2OGLRM", "d_ICA", "d_Isomap", "d_KPCA", "d_LLE", "d_MDS", "d_NMF", "d_PCA", "d_SPCA", "d_SVD", "d_TSNE", "d_UMAP", "dat2bsplinemat", "dat2poly", "date2factor", "date2ym", "date2yq", "dates2features", "ddb_collect", "ddb_data", "ddSci", "decom", "dependency_check", "desaturate", "describe", "df_movecolumn", "distillTreeRules", "dplot3_addtree", "dplot3_bar", "dplot3_box", "dplot3_calibration", "dplot3_cart", "dplot3_conf", "dplot3_fit", "dplot3_graphd3", "dplot3_graphjs", "dplot3_heatmap", "dplot3_leaflet", "dplot3_linad", "dplot3_pie", "dplot3_protein", "dplot3_pvals", "dplot3_spectrogram", "dplot3_table", "dplot3_ts", "dplot3_varimp", "dplot3_volcano", "dplot3_x", "dplot3_xt", "dplot3_xy", "dplot3_xyz", "drange", "dt_check_unique", "dt_describe", "dt_get_column_attr", "dt_get_duplicates", "dt_get_factor_levels", "dt_index_attr", "dt_inspect_type", "dt_keybin_reshape", "dt_merge", "dt_names_by_attr", "dt_names_by_class", "dt_pctmatch", "dt_pctmissing", "dt_set_autotypes", "dt_set_clean_all", "dt_set_cleanfactorlevels", "dt_set_logical2factor", "dt_set_oneHot", "earlystop", "expand.boost", "explain", "f1", "factor_harmonize", "factor_NA2missing", "factoryze", "fct_describe", "formatLightRules", "formatRules", "fwhm2sigma", "get_loaded_pkg_version", "get_mode", "get_rules", "get_vars_from_rules", "getcharacternames", "getdatenames", "getfactornames", "getlogicalnames", "getnames", "getnamesandtypes", "getnumericnames", "ggtheme_dark", "ggtheme_light", "glmLite", "gmean", "gp", "graph_node_metrics", "gtTable", "htest", "inspect_type", "invlogit", "is_constant", "is_discrete", "kfold", "labelify", "lincoef", "list2csv", "logistic", "logit", "logloss", "loocv", "lotri2edgeList", "lsapply", "mae", "make_key", "massGAM", "massGLAM", "massGLM", "massUni", "matchcases", "meta_mod", "mgetnames", "mhist", "mlegend", "mod_error", "mplot_AGGTEobj", "mplot_hsv", "mplot_raster", "mplot3_adsr", "mplot3_bar", "mplot3_box", "mplot3_conf", "mplot3_confbin", "mplot3_decision", "mplot3_fit", "mplot3_fret", "mplot3_graph", "mplot3_harmonograph", "mplot3_heatmap", "mplot3_img", "mplot3_laterality", "mplot3_lolli", "mplot3_missing", "mplot3_mosaic", "mplot3_pr", "mplot3_prp", "mplot3_res", "mplot3_roc", "mplot3_surv", "mplot3_survfit", "mplot3_varimp", "mplot3_x", "mplot3_xy", "mplot3_xym", "mse", "msew", "multigplot", "nCr", "nlareg", "nunique_perfeat", "oddsratio", "oddsratiotable", "oneHot", "onehot2factor", "palettize", "permute", "pfread", "plotly.heat", "precision", "preprocess", "preprocess_", "present", "present_gridsearch", "previewcolor", "prob2categorical", "prune.addtree", "psd", "qstat", "read", "read_config", "recycle", "relu", "resample", "reverseLevels", "revfactorlevels", "rfVarSelect", "rmse", "rnormmat", "rowMax", "rsd", "rsq", "rstudio_theme_rtemis", "rt_reactable", "rt_save", "rtemis_palette", "rtInitProjectDir", "rtlayout", "rtModLog", "rtModLogger", "rtpalette", "rtROC", "rtversion", "rtXDecom", "ruleDist", "rules2medmod", "runifmat", "s_AdaBoost", "s_AddTree", "s_BART", "s_BayesGLM", "s_BRUTO", "s_C50", "s_CART", "s_CTree", "s_EVTree", "s_GAM", "s_GBM", "s_GLM", "s_GLMNET", "s_GLMTree", "s_GLS", "s_H2ODL", "s_H2OGBM", "s_H2ORF", "s_HAL", "s_Isotonic", "s_KNN", "s_LDA", "s_LightCART", "s_LightGBM", "s_LightRF", "s_LightRuleFit", "s_LIHAD", "s_LIHADBoost", "s_LINAD", "s_LINOA", "s_LM", "s_LMTree", "s_LOESS", "s_Logistic", "s_MARS", "s_MLRF", "s_MULTINOM", "s_NBayes", "s_NLA", "s_NLS", "s_NW", "s_POLY", "s_PolyMARS", "s_PPR", "s_PSurv", "s_QDA", "s_QRNN", "s_Ranger", "s_RF", "s_RFSRC", "s_RLM", "s_RuleFit", "s_SDA", "s_SGD", "s_SPLS", "s_SVM", "s_TFN", "s_TLS", "s_XGBoost", "s_XRF", "savePMML", "se", "select_clust", "select_decom", "select_learn", "selectiter", "sensitivity", "seql", "setdiffsym", "setup.bag.resample", "setup.color", "setup.cv.resample", "setup.decompose", "setup.earlystop", "setup.GBM", "setup.grid.resample", "setup.LightRuleFit", "setup.LIHAD", "setup.lincoef", "setup.MARS", "setup.meta.resample", "setup.preprocess", "setup.Ranger", "setup.resample", "sge_submit", "sigmoid", "size", "softmax", "softplus", "sortedlines", "sparsernorm", "specificity", "stderror", "strat.boot", "strat.sub", "strata2factor", "strict", "summarize", "surv_error", "svd1", "synth_multimodal", "synth_reg_data", "table1", "theme_black", "theme_blackgrid", "theme_blackigrid", "theme_darkgray", "theme_darkgraygrid", "theme_darkgrayigrid", "theme_lightgraygrid", "theme_mediumgraygrid", "theme_white", "theme_whitegrid", "theme_whiteigrid", "themes", "timeProc", "tohtml", "train_cv", "tunable", "typeset", "uniprot_get", "uniquevalsperfeat", "winsorize", "x_CCA", "xlsx2list", "xselect_decom", "xtdescribe", "zipdist" ], "_datasets": [ { "name": "uci_heart_failure", "title": "UCI Heart Failure Data", "object": "uci_heart_failure", "class": [ "data.table", "data.frame" ], "fields": [ "age", "anaemia", "creatinine_phosphokinase", "diabetes", "ejection_fraction", "high_blood_pressure", "platelets", "serum_creatinine", "serum_sodium", "sex", "smoking", "time", "DEATH_EVENT" ], "rows": 299, "table": true, "tojson": true } ], "_help": [ { "page": "rtemisalpha-package", "title": "'rtemis': Machine Learning and Visualization", "topics": [ "rtemisalpha-package", "rtemisalpha" ] }, { "page": "grapes-BC-grapes", "title": "Binary matrix times character vector", "topics": [ "%BC%" ] }, { "page": "any_constant", "title": "Check for constant columns", "topics": [ "any_constant" ] }, { "page": "as.data.tree.linadleaves", "title": "Convert 'linadleaves' to 'data.tree' object", "topics": [ "as.data.tree.linadleaves" ] }, { "page": "as.data.tree.rpart", "title": "Convert 'rpart' rules to 'data.tree' object", "topics": [ "as.data.tree.rpart" ] }, { "page": "as.data.tree.shyoptleaves", "title": "Convert 'shyoptleaves' to 'data.tree' object", "topics": [ "as.data.tree.shyoptleaves" ] }, { "page": "auc", "title": "Area under the ROC Curve", "topics": [ "auc" ] }, { "page": "auc_pairs", "title": "Area under the Curve by pairwise concordance", "topics": [ "auc_pairs" ] }, { "page": "bacc", "title": "Balanced Accuracy", "topics": [ "bacc" ] }, { "page": "betas.lihad", "title": "Extract coefficients from Additive Tree leaves", "topics": [ "betas.lihad" ] }, { "page": "bias_variance", "title": "Bias-Variance Decomposition", "topics": [ "bias_variance" ] }, { "page": "binmat2vec", "title": "Binary matrix times character vector", "topics": [ "binmat2vec" ] }, { "page": "strng", "title": "String formatting utilities", "topics": [ "bold", "cyan", "gray", "green", "hilite", "hilitebig", "italic", "magenta", "orange", "red", "reset", "underline" ] }, { "page": "boost", "title": "Boost an 'rtemis' learner for regression", "topics": [ "boost" ] }, { "page": "bootstrap", "title": "Bootstrap Resampling", "topics": [ "bootstrap" ] }, { "page": "brier_score", "title": "Brier Score", "topics": [ "brier_score" ] }, { "page": "c_CMeans", "title": "Fuzzy C-means Clustering", "concept": [ "Clustering" ], "topics": [ "c_CMeans" ] }, { "page": "c_DBSCAN", "title": "Density-based spatial clustering of applications with noise", "concept": [ "Clustering" ], "topics": [ "c_DBSCAN" ] }, { "page": "c_EMC", "title": "Expectation Maximization Clustering", "concept": [ "Clustering" ], "topics": [ "c_EMC" ] }, { "page": "c_H2OKMeans", "title": "K-Means Clustering with H2O", "concept": [ "Clustering" ], "topics": [ "c_H2OKMeans" ] }, { "page": "c_HARDCL", "title": "Clustering by Hard Competitive Learning", "concept": [ "Clustering" ], "topics": [ "c_HARDCL" ] }, { "page": "c_HOPACH", "title": "Hierarchical Ordered Partitioning and Collapsing Hybrid", "concept": [ "Clustering" ], "topics": [ "c_HOPACH" ] }, { "page": "c_KMeans", "title": "K-means Clustering", "concept": [ "Clustering" ], "topics": [ "c_KMeans" ] }, { "page": "c_MeanShift", "title": "Mean Shift Clustering", "concept": [ "Clustering" ], "topics": [ "c_MeanShift" ] }, { "page": "c_NGAS", "title": "Neural Gas Clustering", "concept": [ "Clustering" ], "topics": [ "c_NGAS" ] }, { "page": "c_PAM", "title": "Partitioning Around Medoids", "concept": [ "Clustering" ], "topics": [ "c_PAM" ] }, { "page": "c_PAMK", "title": "Partitioning Around Medoids with k Estimation", "concept": [ "Clustering" ], "topics": [ "c_PAMK" ] }, { "page": "c_SPEC", "title": "Spectral Clustering", "concept": [ "Clustering" ], "topics": [ "c_SPEC" ] }, { "page": "calibrate", "title": "Calibrate predicted probabilities", "topics": [ "calibrate" ] }, { "page": "calibrate_cv", "title": "Calibrate cross-validated model", "topics": [ "calibrate_cv" ] }, { "page": "catrange", "title": "Print range of continuous variable", "topics": [ "catrange" ] }, { "page": "catsize", "title": "Print Size", "topics": [ "catsize" ] }, { "page": "check_data", "title": "Check Data", "topics": [ "check_data" ] }, { "page": "check_files", "title": "Check file(s) exist", "topics": [ "check_files" ] }, { "page": "checkpoint_earlystop", "title": "Early stopping check", "topics": [ "checkpoint_earlystop" ] }, { "page": "chill", "title": "Chill", "topics": [ "chill" ] }, { "page": "class_error", "title": "Classification Error", "topics": [ "class_error" ] }, { "page": "class_imbalance", "title": "Class Imbalance", "topics": [ "class_imbalance" ] }, { "page": "clean_colnames", "title": "Clean column names", "topics": [ "clean_colnames" ] }, { "page": "clean_names", "title": "Clean names", "topics": [ "clean_names" ] }, { "page": "clust", "title": "Clustering with 'rtemis'", "topics": [ "clust" ] }, { "page": "coef.lihad", "title": "Extract coefficients from Hybrid Additive Tree leaves", "topics": [ "coef.lihad" ] }, { "page": "col2grayscale", "title": "Color to Grayscale", "topics": [ "col2grayscale" ] }, { "page": "col2hex", "title": "Convert R color to hexadecimal code", "topics": [ "col2hex" ] }, { "page": "colMax", "title": "Collapse data.frame to vector by getting column max", "topics": [ "colMax" ] }, { "page": "color_fade", "title": "Fade color towards target", "topics": [ "color_fade" ] }, { "page": "color_invertRGB", "title": "Invert Color in RGB space", "topics": [ "color_invertRGB" ] }, { "page": "color_mean", "title": "Average colors", "topics": [ "color_mean" ] }, { "page": "color_order", "title": "Order colors", "topics": [ "color_order" ] }, { "page": "color_separate", "title": "Separate colors", "topics": [ "color_separate" ] }, { "page": "color_sqdist", "title": "Squared Color Distance", "topics": [ "color_sqdist" ] }, { "page": "colorAdjust", "title": "Adjust HSV Color", "topics": [ "colorAdjust" ] }, { "page": "colorGrad", "title": "Color Gradient", "topics": [ "colorGrad" ] }, { "page": "colorGrad.x", "title": "Color gradient for continuous variable", "topics": [ "colorGrad.x" ] }, { "page": "colorgradient.x", "title": "Color gradient for continuous variable", "topics": [ "colorgradient.x" ] }, { "page": "colorMix", "title": "Create an alternating sequence of graded colors", "topics": [ "colorMix" ] }, { "page": "colorOp", "title": "Simple Color Operations", "topics": [ "colorOp" ] }, { "page": "cols2list", "title": "Convert data frame columns to list elements", "topics": [ "cols2list" ] }, { "page": "create_config", "title": "Create rtemis configuration file", "topics": [ "create_config" ] }, { "page": "crules", "title": "Combine rules", "topics": [ "crules" ] }, { "page": "d_H2OAE", "title": "Autoencoder using H2O", "concept": [ "Decomposition", "Deep Learning" ], "topics": [ "d_H2OAE" ] }, { "page": "d_H2OGLRM", "title": "Generalized Low-Rank Models (GLRM) on H2O", "concept": [ "Decomposition" ], "topics": [ "d_H2OGLRM" ] }, { "page": "d_ICA", "title": "Independent Component Analysis", "concept": [ "Decomposition" ], "topics": [ "d_ICA" ] }, { "page": "d_Isomap", "title": "Isomap", "concept": [ "Decomposition" ], "topics": [ "d_Isomap" ] }, { "page": "d_KPCA", "title": "Kernel Principal Component Analysis", "concept": [ "Decomposition" ], "topics": [ "d_KPCA" ] }, { "page": "d_LLE", "title": "Locally Linear Embedding", "concept": [ "Decomposition" ], "topics": [ "d_LLE" ] }, { "page": "d_MDS", "title": "Multidimensional Scaling", "concept": [ "Decomposition" ], "topics": [ "d_MDS" ] }, { "page": "d_NMF", "title": "Non-negative Matrix Factorization (NMF)", "concept": [ "Decomposition" ], "topics": [ "d_NMF" ] }, { "page": "d_PCA", "title": "Principal Component Analysis", "concept": [ "Decomposition" ], "topics": [ "d_PCA" ] }, { "page": "d_SPCA", "title": "Sparse Principal Component Analysis", "concept": [ "Decomposition" ], "topics": [ "d_SPCA" ] }, { "page": "d_SVD", "title": "Singular Value Decomposition", "concept": [ "Decomposition" ], "topics": [ "d_SVD" ] }, { "page": "d_TSNE", "title": "t-distributed Stochastic Neighbor Embedding", "concept": [ "Decomposition" ], "topics": [ "d_TSNE" ] }, { "page": "d_UMAP", "title": "Uniform Manifold Approximation and Projection (UMAP)", "concept": [ "Decomposition" ], "topics": [ "d_UMAP" ] }, { "page": "dat2bsplinemat", "title": "B-Spline matrix from dataset", "topics": [ "dat2bsplinemat" ] }, { "page": "dat2poly", "title": "Create n-degree polynomial from data frame", "topics": [ "dat2poly" ] }, { "page": "date2factor", "title": "Date to factor time bin", "topics": [ "date2factor" ] }, { "page": "date2ym", "title": "Date to year-month factor", "topics": [ "date2ym" ] }, { "page": "date2yq", "title": "Date to year-quarter factor", "topics": [ "date2yq" ] }, { "page": "dates2features", "title": "Extract features from dates", "topics": [ "dates2features" ] }, { "page": "ddb_collect", "title": "Collect a lazy-read duckdb table", "topics": [ "ddb_collect" ] }, { "page": "ddb_data", "title": "Read CSV using DuckDB", "topics": [ "ddb_data" ] }, { "page": "ddSci", "title": "Format Numbers for Printing", "topics": [ "ddSci" ] }, { "page": "decom", "title": "Matrix Decomposition with 'rtemis'", "topics": [ "decom" ] }, { "page": "dependency_check", "title": "'rtemis' internal: Dependencies check", "topics": [ "dependency_check" ] }, { "page": "desaturate", "title": "Pastelify a color (make a color more pastel)", "topics": [ "desaturate" ] }, { "page": "describe", "title": "Describe generic", "topics": [ "describe" ] }, { "page": "df_movecolumn", "title": "Move data frame column", "topics": [ "df_movecolumn" ] }, { "page": "distillTreeRules", "title": "Distill rules from trained RF and GBM learners", "topics": [ "distillTreeRules" ] }, { "page": "dplot3_addtree", "title": "Plot AddTree trees", "topics": [ "dplot3_addtree" ] }, { "page": "dplot3_bar", "title": "Interactive Barplots", "topics": [ "dplot3_bar" ] }, { "page": "dplot3_box", "title": "Interactive Boxplots & Violin plots", "topics": [ "dplot3_box" ] }, { "page": "dplot3_calibration", "title": "Draw calibration plot", "topics": [ "dplot3_calibration" ] }, { "page": "dplot3_cart", "title": "Plot 'rpart' decision trees", "topics": [ "dplot3_cart" ] }, { "page": "dplot3_conf", "title": "Plot confusion matrix", "topics": [ "dplot3_conf" ] }, { "page": "dplot3_fit", "title": "True vs. Predicted Plot", "topics": [ "dplot3_fit" ] }, { "page": "dplot3_graphd3", "title": "Plot graph using 'networkD3'", "topics": [ "dplot3_graphd3" ] }, { "page": "dplot3_graphjs", "title": "Plot network using 'threejs::graphjs'", "topics": [ "dplot3_graphjs" ] }, { "page": "dplot3_heatmap", "title": "Interactive Heatmaps", "topics": [ "dplot3_heatmap" ] }, { "page": "dplot3_leaflet", "title": "Plot interactive choropleth map using 'leaflet'", "topics": [ "dplot3_leaflet" ] }, { "page": "dplot3_linad", "title": "Plot a Linear Additive Tree trained by s_LINAD using _visNetwork_", "topics": [ "dplot3_linad" ] }, { "page": "dplot3_pie", "title": "Interactive Pie Chart", "topics": [ "dplot3_pie" ] }, { "page": "dplot3_protein", "title": "Plot the amino acid sequence with annotations", "topics": [ "dplot3_protein" ] }, { "page": "dplot3_pvals", "title": "Barplot p-values using dplot3_bar", "topics": [ "dplot3_pvals" ] }, { "page": "dplot3_spectrogram", "title": "Interactive Spectrogram", "topics": [ "dplot3_spectrogram" ] }, { "page": "dplot3_table", "title": "Simple HTML table", "topics": [ "dplot3_table" ] }, { "page": "dplot3_ts", "title": "Interactive Timeseries Plots", "topics": [ "dplot3_ts" ] }, { "page": "dplot3_varimp", "title": "Interactive Variable Importance Plot", "topics": [ "dplot3_varimp" ] }, { "page": "dplot3_volcano", "title": "Volcano Plot", "topics": [ "dplot3_volcano" ] }, { "page": "dplot3_x", "title": "Interactive Univariate Plots", "topics": [ "dplot3_x" ] }, { "page": "dplot3_xt", "title": "Plot timeseries data", "topics": [ "dplot3_xt" ] }, { "page": "dplot3_xy", "title": "Interactive Scatter Plots", "topics": [ "dplot3_xy" ] }, { "page": "dplot3_xyz", "title": "Interactive 3D Plots", "topics": [ "dplot3_xyz" ] }, { "page": "drange", "title": "Set Dynamic Range", "topics": [ "drange" ] }, { "page": "dt_check_unique", "title": "Check if all levels in a column are unique", "topics": [ "dt_check_unique" ] }, { "page": "dt_describe", "title": "Describe data.table", "topics": [ "dt_describe" ] }, { "page": "dt_get_column_attr", "title": "Tabulate column attributes", "topics": [ "dt_get_column_attr" ] }, { "page": "dt_get_duplicates", "title": "Get index of duplicate values", "topics": [ "dt_get_duplicates" ] }, { "page": "dt_get_factor_levels", "title": "Get factor levels from data.table", "topics": [ "dt_get_factor_levels" ] }, { "page": "dt_index_attr", "title": "Index columns by attribute name & value", "topics": [ "dt_index_attr" ] }, { "page": "dt_inspect_type", "title": "Inspect column types", "topics": [ "dt_inspect_type" ] }, { "page": "dt_keybin_reshape", "title": "Long to wide key-value reshaping", "topics": [ "dt_keybin_reshape" ] }, { "page": "dt_merge", "title": "Merge data.tables", "topics": [ "dt_merge" ] }, { "page": "dt_names_by_attr", "title": "List column names by attribute", "topics": [ "dt_names_by_attr" ] }, { "page": "dt_names_by_class", "title": "List column names by class", "topics": [ "dt_names_by_class" ] }, { "page": "dt_pctmatch", "title": "Get N and percent match of values between two columns of two data.tables", "topics": [ "dt_pctmatch" ] }, { "page": "dt_pctmissing", "title": "Get percent of missing values from every column", "topics": [ "dt_pctmissing" ] }, { "page": "dt_set_autotypes", "title": "Set column types automatically", "topics": [ "dt_set_autotypes" ] }, { "page": "dt_set_clean_all", "title": "Clean column names and factor levels in-place", "topics": [ "dt_set_clean_all" ] }, { "page": "dt_set_cleanfactorlevels", "title": "Clean factor levels of data.table in-place", "topics": [ "dt_set_cleanfactorlevels" ] }, { "page": "dt_set_logical2factor", "title": "Convert data.table logical columns to factor with custom labels in-place", "topics": [ "dt_set_logical2factor" ] }, { "page": "earlystop", "title": "Early stopping", "topics": [ "earlystop" ] }, { "page": "expand.boost", "title": "Expand boosting series", "topics": [ "expand.boost" ] }, { "page": "explain", "title": "Explain individual-level model predictions", "topics": [ "explain" ] }, { "page": "f1", "title": "F1 score", "topics": [ "f1" ] }, { "page": "factor_harmonize", "title": "Factor harmonize", "topics": [ "factor_harmonize" ] }, { "page": "factor_NA2missing", "title": "Factor NA to \"missing\" level", "topics": [ "factor_NA2missing" ] }, { "page": "factoryze", "title": "Factor Analysis", "topics": [ "factoryze" ] }, { "page": "fct_describe", "title": "Decribe factor", "topics": [ "fct_describe" ] }, { "page": "format.call", "title": "Format method for 'call' objects", "topics": [ "format.call" ] }, { "page": "formatLightRules", "title": "Format LightRuleFit rules", "topics": [ "formatLightRules" ] }, { "page": "formatRules", "title": "Format rules", "topics": [ "formatRules" ] }, { "page": "fwhm2sigma", "title": "FWHM to Sigma", "topics": [ "fwhm2sigma" ] }, { "page": "get_holidays", "title": "Get holidays from date vector", "topics": [ "get_holidays" ] }, { "page": "get_loaded_pkg_version", "title": "Get version of all loaded packages (namespaces)", "topics": [ "get_loaded_pkg_version" ] }, { "page": "get_mode", "title": "Get the mode of a factor or integer", "topics": [ "get_mode" ] }, { "page": "get_rules", "title": "Get RuleFit rules", "topics": [ "get_rules" ] }, { "page": "get_vars_from_rules", "title": "Extract variable names from rules", "topics": [ "get_vars_from_rules" ] }, { "page": "get-names", "title": "Get factor/numeric/logical/character names from data.frame/data.table", "topics": [ "get-names", "getfactornames" ] }, { "page": "getnames", "title": "Get names by string matching", "topics": [ "getcharacternames", "getdatenames", "getlogicalnames", "getnames", "getnumericnames" ] }, { "page": "getnamesandtypes", "title": "Get data.frame names and types", "topics": [ "getnamesandtypes" ] }, { "page": "ggtheme_dark", "title": "'rtemis' 'ggplot2' dark theme", "topics": [ "ggtheme_dark" ] }, { "page": "ggtheme_light", "title": "'rtemis' 'ggplot2' light theme", "topics": [ "ggtheme_light" ] }, { "page": "glmLite", "title": "Bare bones decision tree derived from 'rpart'", "topics": [ "glmLite" ] }, { "page": "gmean", "title": "Geometric mean", "topics": [ "gmean" ] }, { "page": "gp", "title": "Bayesian Gaussian Processes [R]", "topics": [ "gp" ] }, { "page": "graph_node_metrics", "title": "Node-wise (i.e. vertex-wise) graph metrics", "topics": [ "graph_node_metrics" ] }, { "page": "gridCheck", "title": "'rtemis' internal: Grid check", "topics": [ "gridCheck" ] }, { "page": "gtTable", "title": "Greater-than Table", "topics": [ "gtTable" ] }, { "page": "htest", "title": "Basic Bivariate Hypothesis Testing and Plotting", "topics": [ "htest" ] }, { "page": "inspect_type", "title": "Inspect character and factor vector", "topics": [ "inspect_type" ] }, { "page": "invlogit", "title": "Inverse Logit", "topics": [ "invlogit" ] }, { "page": "is_constant", "title": "Check if vector is constant", "topics": [ "is_constant" ] }, { "page": "is_discrete", "title": "Check if variable is discrete (factor or integer)", "topics": [ "is_discrete" ] }, { "page": "kfold", "title": "K-fold Resampling", "topics": [ "kfold" ] }, { "page": "labelify", "title": "Format text for label printing", "topics": [ "labelify" ] }, { "page": "lincoef", "title": "Linear Model Coefficients", "topics": [ "lincoef" ] }, { "page": "list2csv", "title": "Write list elements to CSV files", "topics": [ "list2csv" ] }, { "page": "logistic", "title": "Logistic function", "topics": [ "logistic" ] }, { "page": "logit", "title": "Logit transform", "topics": [ "logit" ] }, { "page": "logloss", "title": "Log Loss for a binary classifier", "topics": [ "logloss" ] }, { "page": "loocv", "title": "Leave-one-out Resampling", "topics": [ "loocv" ] }, { "page": "lotri2edgeList", "title": "Connectivity Matrix to Edge List", "topics": [ "lotri2edgeList" ] }, { "page": "lsapply", "title": "'lsapply'", "topics": [ "lsapply" ] }, { "page": "make_key", "title": "Make key from data.table id - description columns", "topics": [ "make_key" ] }, { "page": "massGAM", "title": "Mass-univariate GAM Analysis", "topics": [ "massGAM" ] }, { "page": "massGLAM", "title": "Mass-univariate GLM Analysis", "topics": [ "massGLAM" ] }, { "page": "massGLM", "title": "Mass-univariate GLM Analysis", "topics": [ "massGLM" ] }, { "page": "massUni", "title": "Mass-univariate Analysis", "topics": [ "massUni" ] }, { "page": "matchcases", "title": "Match cases by covariates", "topics": [ "matchcases" ] }, { "page": "mergelongtreatment", "title": "Merge panel data treatment and outcome data", "topics": [ "mergelongtreatment" ] }, { "page": "meta_mod", "title": "Meta Models for Regression (Model Stacking)", "topics": [ "meta_mod" ] }, { "page": "mgetnames", "title": "Get names by string matching multiple patterns", "topics": [ "mgetnames" ] }, { "page": "mhist", "title": "Histograms", "topics": [ "mhist" ] }, { "page": "mlegend", "title": "Add legend to 'mplot3' plot", "topics": [ "mlegend" ] }, { "page": "mod_error", "title": "Error Metrics for Supervised Learning", "topics": [ "mod_error" ] }, { "page": "mplot_AGGTEobj", "title": "Plot AGGTEobj object", "topics": [ "mplot_AGGTEobj" ] }, { "page": "mplot_hsv", "title": "Plot HSV color range", "topics": [ "mplot_hsv" ] }, { "page": "mplot_raster", "title": "Plot Array as Raster Image", "topics": [ "mplot_raster" ] }, { "page": "mplot3_adsr", "title": "'mplot3': ADSR Plot", "topics": [ "mplot3_adsr" ] }, { "page": "mplot3_bar", "title": "'mplot3': Barplot", "topics": [ "mplot3_bar" ] }, { "page": "mplot3_box", "title": "'mplot3': Boxplot", "topics": [ "mplot3_box" ] }, { "page": "mplot3_conf", "title": "Plot confusion matrix", "topics": [ "mplot3_conf" ] }, { "page": "mplot3_confbin", "title": "Plot extended confusion matrix for binary classification", "topics": [ "mplot3_confbin" ] }, { "page": "mplot3_decision", "title": "'mplot3': Decision boundaries", "topics": [ "mplot3_decision" ] }, { "page": "mplot3_fit", "title": "True vs. Fitted plot", "topics": [ "mplot3_fit" ] }, { "page": "mplot3_fret", "title": "'mplot3': Guitar Fretboard", "topics": [ "mplot3_fret" ] }, { "page": "mplot3_graph", "title": "Plot 'igraph' networks", "topics": [ "mplot3_graph" ] }, { "page": "mplot3_harmonograph", "title": "Plot a harmonograph", "topics": [ "mplot3_harmonograph" ] }, { "page": "mplot3_heatmap", "title": "'mplot3' Heatmap ('image'; modified 'heatmap')", "topics": [ "mplot3_heatmap" ] }, { "page": "mplot3_img", "title": "Draw image (False color 2D)", "topics": [ "mplot3_img" ] }, { "page": "mplot3_laterality", "title": "Laterality scatter plot", "topics": [ "mplot3_laterality" ] }, { "page": "mplot3_lolli", "title": "'mplot3' Lollipop Plot", "topics": [ "mplot3_lolli" ] }, { "page": "mplot3_missing", "title": "Plot missingness", "topics": [ "mplot3_missing" ] }, { "page": "mplot3_mosaic", "title": "Mosaic plot", "topics": [ "mplot3_mosaic" ] }, { "page": "mplot3_pr", "title": "'mplot3' Precision Recall curves", "topics": [ "mplot3_pr" ] }, { "page": "mplot3_prp", "title": "Plot CART Decision Tree", "topics": [ "mplot3_prp" ] }, { "page": "mplot3_res", "title": "'mplot3' Plot 'resample'", "topics": [ "mplot3_res" ] }, { "page": "mplot3_roc", "title": "'mplot3' ROC curves", "topics": [ "mplot3_roc" ] }, { "page": "mplot3_surv", "title": "'mplot3': Survival Plots", "topics": [ "mplot3_surv" ] }, { "page": "mplot3_survfit", "title": "'mplot3': Plot 'survfit' objects", "topics": [ "mplot3_survfit" ] }, { "page": "mplot3_varimp", "title": "'mplot3': Variable Importance", "topics": [ "mplot3_varimp" ] }, { "page": "mplot3_x", "title": "'mplot3': Univariate plots: index, histogram, density, QQ-line", "topics": [ "mplot3_x" ] }, { "page": "mplot3_xy", "title": "'mplot3': XY Scatter and line plots", "topics": [ "mplot3_xy" ] }, { "page": "mplot3_xym", "title": "Scatter plot with marginal density and/or histogram", "topics": [ "mplot3_xym" ] }, { "page": "error", "title": "Error functions", "topics": [ "mae", "mse", "msew", "rmse" ] }, { "page": "multigplot", "title": "Multipanel *ggplot2* plots", "topics": [ "multigplot" ] }, { "page": "nCr", "title": "n Choose r", "topics": [ "nCr" ] }, { "page": "nunique_perfeat", "title": "Number of unique values per feature", "topics": [ "nunique_perfeat" ] }, { "page": "oddsratio", "title": "Calculate odds ratio for a 2x2 contingency table", "topics": [ "oddsratio" ] }, { "page": "oddsratiotable", "title": "Odds ratio table from logistic regression", "topics": [ "oddsratiotable" ] }, { "page": "oneHot", "title": "One hot encoding", "topics": [ "dt_set_oneHot", "oneHot", "oneHot.data.frame", "oneHot.data.table", "oneHot.default" ] }, { "page": "onehot2factor", "title": "Convert one-hot encoded matrix to factor", "topics": [ "onehot2factor" ] }, { "page": "palettize", "title": "Palettize colors", "topics": [ "palettize" ] }, { "page": "permute", "title": "Create permutations", "topics": [ "permute" ] }, { "page": "pfread", "title": "fread delimited file in parts", "topics": [ "pfread" ] }, { "page": "plot.massGAM", "title": "Plot 'massGAM' object", "topics": [ "plot.massGAM" ] }, { "page": "plot.massGLM", "title": "Plot 'massGLM' object", "topics": [ "plot.massGLM" ] }, { "page": "plot.resample", "title": "'plot' method for 'resample' object", "topics": [ "plot.resample" ] }, { "page": "plot.rtModCVCalibration", "title": "Plot 'rtModCVCalibration' object", "topics": [ "plot.rtModCVCalibration" ] }, { "page": "plot.rtTest", "title": "Plot 'rtTest' object", "topics": [ "plot.rtTest" ] }, { "page": "plotly.heat", "title": "Heatmap with 'plotly'", "topics": [ "plotly.heat" ] }, { "page": "precision", "title": "Precision (aka PPV)", "topics": [ "precision" ] }, { "page": "predict.addtree", "title": "Predict Method for MediBoost Model", "topics": [ "predict.addtree" ] }, { "page": "predict.boost", "title": "Predict method for 'boost' object", "topics": [ "predict.boost" ] }, { "page": "predict.cartLite", "title": "Predict method for 'cartLite' object", "topics": [ "predict.cartLite" ] }, { "page": "predict.cartLiteBoostTV", "title": "Predict method for 'cartLiteBoostTV' object", "topics": [ "predict.cartLiteBoostTV" ] }, { "page": "predict.glmLite", "title": "Predict method for 'glmLite' object", "topics": [ "predict.glmLite" ] }, { "page": "predict.glmLiteBoostTV", "title": "Predict method for 'glmLiteBoostTV' object", "topics": [ "predict.glmLiteBoostTV" ] }, { "page": "predict.hytboost", "title": "Predict method for 'hytboost' object", "topics": [ "predict.hytboost" ] }, { "page": "predict.hytboostnow", "title": "Predict method for 'hytboostnow' object", "topics": [ "predict.hytboostnow" ] }, { "page": "predict.hytreenow", "title": "Predict method for 'hytreeLite' object", "topics": [ "predict.hytreenow" ] }, { "page": "predict.hytreew", "title": "Predict method for 'hytreew' object", "topics": [ "predict.hytreew" ] }, { "page": "predict.LightRuleFit", "title": "'predict' method for 'LightRuleFit' object", "topics": [ "predict.LightRuleFit" ] }, { "page": "predict.lihad", "title": "Predict method for 'lihad' object", "topics": [ "predict.lihad" ] }, { "page": "predict.linadleaves", "title": "Predict method for 'linadleaves' object", "topics": [ "predict.linadleaves" ] }, { "page": "predict.nlareg", "title": "Predict method for 'nlareg' object", "topics": [ "predict.nlareg" ] }, { "page": "predict.nullmod", "title": "'rtemis' internal: predict for an object of class 'nullmod'", "topics": [ "predict.nullmod" ] }, { "page": "predict.rtBSplines", "title": "Predict S3 method for 'rtBSplines'", "topics": [ "predict.rtBSplines" ] }, { "page": "predict.rtModCVCalibration", "title": "Predict using calibrated model", "topics": [ "predict.rtModCVCalibration" ] }, { "page": "predict.rtTLS", "title": "'predict.rtTLS': 'predict' method for 'rtTLS' object", "topics": [ "predict.rtTLS" ] }, { "page": "predict.rulefit", "title": "'predict' method for 'rulefit' object", "topics": [ "predict.rulefit" ] }, { "page": "preprocess", "title": "Data preprocessing", "topics": [ "preprocess" ] }, { "page": "preprocess_", "title": "Data preprocessing (in-place)", "topics": [ "preprocess_" ] }, { "page": "present", "title": "Present elevate models", "topics": [ "present" ] }, { "page": "present_gridsearch", "title": "Present gridsearch results", "topics": [ "present_gridsearch" ] }, { "page": "previewcolor", "title": "Preview color v2.0", "topics": [ "previewcolor" ] }, { "page": "print.addtree", "title": "Print method for 'addtree' object created using s_AddTree", "topics": [ "print.addtree" ] }, { "page": "print.boost", "title": "Print method for boost object", "topics": [ "print.boost" ] }, { "page": "print.cartLiteBoostTV", "title": "Print method for cartLiteBoostTV object", "topics": [ "print.cartLiteBoostTV" ] }, { "page": "print.CheckData", "title": "Print 'CheckData' object", "topics": [ "print.CheckData" ] }, { "page": "print.class_error", "title": "Print class_error", "topics": [ "print.class_error" ] }, { "page": "print.glmLiteBoostTV", "title": "Print method for 'glmLiteBoostTV' object", "topics": [ "print.glmLiteBoostTV" ] }, { "page": "print.gridSearch", "title": "'print' method for 'gridSearch' object", "topics": [ "print.gridSearch" ] }, { "page": "print.hytboost", "title": "Print method for 'hytboost' object", "topics": [ "print.hytboost" ] }, { "page": "print.hytboostnow", "title": "Print method for 'boost' object", "topics": [ "print.hytboostnow" ] }, { "page": "print.lihad", "title": "Print method for 'lihad' object", "topics": [ "print.lihad" ] }, { "page": "print.linadleaves", "title": "Print method for 'linadleaves' object", "topics": [ "print.linadleaves" ] }, { "page": "print.massGAM", "title": "'print'massGAM object", "topics": [ "print.massGAM" ] }, { "page": "print.massGLM", "title": "'print'massGLM object", "topics": [ "print.massGLM" ] }, { "page": "print.regError", "title": "Print 'regError' object", "topics": [ "print.regError" ] }, { "page": "print.resample", "title": "'print' method for resample object", "topics": [ "print.resample" ] }, { "page": "print.rtBiasVariance", "title": "Print method for bias_variance", "topics": [ "print.rtBiasVariance" ] }, { "page": "rtDecom-methods", "title": "'print.rtDecom': 'print' method for 'rtDecom' object", "topics": [ "print.rtDecom" ] }, { "page": "print.rtTLS", "title": "'print.rtTLS': 'print' method for 'rtTLS' object", "topics": [ "print.rtTLS" ] }, { "page": "print.surv_error", "title": "Print surv_error", "topics": [ "print.surv_error" ] }, { "page": "prob2categorical", "title": "Convert probabilities to categorical (factor)", "topics": [ "prob2categorical" ] }, { "page": "prune.addtree", "title": "Prune AddTree tree", "topics": [ "prune.addtree" ] }, { "page": "psd", "title": "Population Standard Deviation", "topics": [ "psd" ] }, { "page": "qstat", "title": "SGE qstat", "topics": [ "qstat" ] }, { "page": "read", "title": "Read tabular data from a variety of formats", "topics": [ "read" ] }, { "page": "read_config", "title": "Read rtemis configuration file", "topics": [ "read_config" ] }, { "page": "recycle", "title": "Recycle values of vector to match length of target", "topics": [ "recycle" ] }, { "page": "reg_error", "title": "Regression Error Metrics", "topics": [ "reg_error" ] }, { "page": "relu", "title": "ReLU - Rectified Linear Unit", "topics": [ "relu" ] }, { "page": "resample", "title": "Resampling methods", "topics": [ "resample" ] }, { "page": "reverseLevels", "title": "Reverse factor levels", "topics": [ "reverseLevels" ] }, { "page": "revfactorlevels", "title": "Reverse factor level order", "topics": [ "revfactorlevels" ] }, { "page": "rfVarSelect", "title": "Variable Selection by Random Forest", "topics": [ "rfVarSelect" ] }, { "page": "rnormmat", "title": "Random Normal Matrix", "topics": [ "rnormmat" ] }, { "page": "rowMax", "title": "Collapse data.frame to vector by getting row max", "topics": [ "rowMax" ] }, { "page": "rsd", "title": "Coefficient of Variation (Relative standard deviation)", "topics": [ "rsd" ] }, { "page": "rsq", "title": "R-squared", "topics": [ "rsq" ] }, { "page": "rstudio_theme_rtemis", "title": "Apply rtemis theme for RStudio", "topics": [ "rstudio_theme_rtemis" ] }, { "page": "rt_reactable", "title": "View table using reactable", "topics": [ "rt_reactable" ] }, { "page": "rt_save", "title": "Write 'rtemis' model to RDS file", "topics": [ "rt_save" ] }, { "page": "rtClust-methods", "title": "rtClust S3 methods", "topics": [ "print.rtClust", "rtClust-methods" ] }, { "page": "rtemis_palette", "title": "Access rtemis palette colors", "topics": [ "rtemis_palette" ] }, { "page": "rtInitProjectDir", "title": "Initialize Project Directory", "topics": [ "rtInitProjectDir" ] }, { "page": "rtlayout", "title": "Create multipanel plots with the 'mplot3' family", "topics": [ "rtlayout" ] }, { "page": "rtMeta-methods", "title": "rtMeta S3 methods", "topics": [ "predict.rtMeta", "rtMeta-methods" ] }, { "page": "rtMod-methods", "title": "'rtMod' S3 methods", "topics": [ "coef.rtMod", "fitted.rtMod", "plot.rtMod", "predict.rtMod", "predict.rtModLite", "print.rtMod", "residuals.rtMod", "rtMod-methods", "summary.rtMod" ] }, { "page": "rtModBag-methods", "title": "rtModBag S3 methods", "topics": [ "predict.rtModBag", "rtModBag-methods" ] }, { "page": "rtModClass-class", "title": "'rtemis' Classification Model Class", "topics": [ "rtModClass", "rtModClass-class" ] }, { "page": "rtModCV-methods", "title": "S3 methods for 'rtModCV' class that differ from those of the 'rtMod' superclass", "topics": [ "describe.rtModCV", "plot.rtModCV", "predict.rtModCV", "rtModCV-methods", "summary.rtModCV" ] }, { "page": "rtModLite-methods", "title": "rtModLite S3 methods", "topics": [ "print.rtModLite", "rtModLite-methods" ] }, { "page": "rtModLog-class", "title": "'rtemis' Supervised Model Log Class", "topics": [ "rtModLog", "rtModLog-class" ] }, { "page": "rtModLogger-class", "title": "'rtemis' model logger", "topics": [ "rtModLogger", "rtModLogger-class" ] }, { "page": "rtpalette", "title": "'rtemis' Color Palettes", "topics": [ "rtpalette" ] }, { "page": "rtROC", "title": "Build an ROC curve", "topics": [ "rtROC" ] }, { "page": "rtset", "title": "'rtemis' default-setting functions", "topics": [ "rtset" ] }, { "page": "rtversion", "title": "Get rtemis and OS version info", "topics": [ "rtversion" ] }, { "page": "rtXDecom-class", "title": "R6 class for 'rtemis' cross-decompositions", "topics": [ "rtXDecom", "rtXDecom-class" ] }, { "page": "ruleDist", "title": "Rule distance", "topics": [ "ruleDist" ] }, { "page": "rules2medmod", "title": "Convert rules from cutoffs to median/mode and range", "topics": [ "rules2medmod" ] }, { "page": "runifmat", "title": "Random Uniform Matrix", "topics": [ "runifmat" ] }, { "page": "s_AdaBoost", "title": "Adaboost Binary Classifier C", "concept": [ "Ensembles", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_AdaBoost" ] }, { "page": "s_AddTree", "title": "Additive Tree: Tree-Structured Boosting C", "concept": [ "Interpretable models", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_AddTree" ] }, { "page": "s_BART", "title": "Bayesian Additive Regression Trees (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_BART" ] }, { "page": "s_BayesGLM", "title": "Bayesian GLM", "concept": [ "Bayesian", "Supervised Learning" ], "topics": [ "s_BayesGLM" ] }, { "page": "s_BRUTO", "title": "Projection Pursuit Regression (BRUTO) [R]", "concept": [ "Supervised Learning" ], "topics": [ "s_BRUTO" ] }, { "page": "s_C50", "title": "C5.0 Decision Trees and Rule-Based Models C", "concept": [ "Interpretable models", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_C50" ] }, { "page": "s_CART", "title": "Classification and Regression Trees [C, R, S]", "concept": [ "Interpretable models", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_CART" ] }, { "page": "s_CTree", "title": "Conditional Inference Trees [C, R, S]", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_CTree" ] }, { "page": "s_EVTree", "title": "Evolutionary Learning of Globally Optimal Trees (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_EVTree" ] }, { "page": "s_GAM", "title": "Generalized Additive Model (GAM) (C, R)", "concept": [ "Supervised Learning" ], "topics": [ "s_GAM" ] }, { "page": "s_GBM", "title": "Gradient Boosting Machine [C, R, S]", "concept": [ "Ensembles", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_GBM" ] }, { "page": "s_GLM", "title": "Generalized Linear Model (C, R)", "concept": [ "Interpretable models", "Supervised Learning" ], "topics": [ "s_GLM" ] }, { "page": "s_GLMNET", "title": "GLM with Elastic Net Regularization [C, R, S]", "concept": [ "Interpretable models", "Supervised Learning" ], "topics": [ "s_GLMNET" ] }, { "page": "s_GLMTree", "title": "Generalized Linear Model Tree [R]", "concept": [ "Interpretable models", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_GLMTree" ] }, { "page": "s_GLS", "title": "Generalized Least Squares [R]", "concept": [ "Supervised Learning" ], "topics": [ "s_GLS" ] }, { "page": "s_H2ODL", "title": "Deep Learning on H2O (C, R)", "concept": [ "Deep Learning", "Supervised Learning" ], "topics": [ "s_H2ODL" ] }, { "page": "s_H2OGBM", "title": "Gradient Boosting Machine on H2O (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_H2OGBM" ] }, { "page": "s_H2ORF", "title": "Random Forest on H2O (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_H2ORF" ] }, { "page": "s_HAL", "title": "Highly Adaptive LASSO [C, R, S]", "concept": [ "Supervised Learning" ], "topics": [ "s_HAL" ] }, { "page": "s_Isotonic", "title": "Classification and Regression Trees [C, R, S]", "concept": [ "Supervised Learning" ], "topics": [ "predict.Isotonic", "s_Isotonic" ] }, { "page": "s_KNN", "title": "k-Nearest Neighbors Classification and Regression (C, R)", "concept": [ "Supervised Learning" ], "topics": [ "s_KNN" ] }, { "page": "s_LDA", "title": "Linear Discriminant Analysis", "concept": [ "Supervised Learning" ], "topics": [ "s_LDA" ] }, { "page": "s_LightCART", "title": "LightCART Classification and Regression (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_LightCART" ] }, { "page": "s_LightGBM", "title": "LightGBM Classification and Regression (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_LightGBM" ] }, { "page": "s_LightRF", "title": "Random Forest using LightGBM", "topics": [ "s_LightRF" ] }, { "page": "s_LightRuleFit", "title": "RuleFit with LightGBM (C, R)", "topics": [ "s_LightRuleFit" ] }, { "page": "s_LIHAD", "title": "The Linear Hard Hybrid Tree: Hard Additive Tree (no gamma) with Linear Nodes [R]", "topics": [ "s_LIHAD" ] }, { "page": "s_LIHADBoost", "title": "Boosting of Linear Hard Additive Trees [R]", "topics": [ "s_LIHADBoost" ] }, { "page": "s_LINAD", "title": "Linear Additive Tree (C, R)", "topics": [ "s_LINAD" ] }, { "page": "s_LINOA", "title": "Linear Optimized Additive Tree (C, R)", "topics": [ "s_LINOA" ] }, { "page": "s_LM", "title": "Linear model", "concept": [ "Supervised Learning" ], "topics": [ "s_LM" ] }, { "page": "s_LMTree", "title": "Linear Model Tree [R]", "concept": [ "Interpretable models", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_LMTree" ] }, { "page": "s_LOESS", "title": "Local Polynomial Regression (LOESS) [R]", "topics": [ "s_LOESS" ] }, { "page": "s_Logistic", "title": "Logistic Regression", "topics": [ "s_Logistic" ] }, { "page": "s_MARS", "title": "Multivariate adaptive regression splines (MARS) (C, R)", "concept": [ "Supervised Learning" ], "topics": [ "s_MARS" ] }, { "page": "s_MLRF", "title": "Spark MLlib Random Forest (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_MLRF" ] }, { "page": "s_MULTINOM", "title": "Multinomial Logistic Regression", "topics": [ "s_MULTINOM" ] }, { "page": "s_NBayes", "title": "Naive Bayes Classifier C", "concept": [ "Supervised Learning" ], "topics": [ "s_NBayes" ] }, { "page": "s_NLA", "title": "NonLinear Activation unit Regression (NLA) [R]", "concept": [ "Supervised Learning" ], "topics": [ "s_NLA" ] }, { "page": "s_NLS", "title": "Nonlinear Least Squares (NLS) [R]", "concept": [ "Supervised Learning" ], "topics": [ "s_NLS" ] }, { "page": "s_NW", "title": "Nadaraya-Watson kernel regression [R]", "concept": [ "Supervised Learning" ], "topics": [ "s_NW" ] }, { "page": "s_POLY", "title": "Polynomial Regression", "topics": [ "s_POLY" ] }, { "page": "s_PolyMARS", "title": "Multivariate adaptive polynomial spline regression (POLYMARS) (C, R)", "concept": [ "Supervised Learning" ], "topics": [ "s_PolyMARS" ] }, { "page": "s_PPR", "title": "Projection Pursuit Regression (PPR) [R]", "concept": [ "Supervised Learning" ], "topics": [ "s_PPR" ] }, { "page": "s_PSurv", "title": "Parametric Survival Regression [S]", "concept": [ "Survival Regression" ], "topics": [ "s_PSurv" ] }, { "page": "s_QDA", "title": "Quadratic Discriminant Analysis C", "concept": [ "Supervised Learning" ], "topics": [ "s_QDA" ] }, { "page": "s_QRNN", "title": "Quantile Regression Neural Network [R]", "concept": [ "Supervised Learning" ], "topics": [ "s_QRNN" ] }, { "page": "s_Ranger", "title": "Random Forest Classification and Regression (C, R)", "concept": [ "Ensembles", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_Ranger" ] }, { "page": "s_RF", "title": "Random Forest Classification and Regression (C, R)", "concept": [ "Ensembles", "Supervised Learning", "Tree-based methods" ], "topics": [ "s_RF" ] }, { "page": "s_RFSRC", "title": "Random Forest for Classification, Regression, and Survival [C, R, S]", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_RFSRC" ] }, { "page": "s_RLM", "title": "Robust linear model", "topics": [ "s_RLM" ] }, { "page": "s_RuleFit", "title": "Rulefit [C, R]", "topics": [ "s_RuleFit" ] }, { "page": "s_SDA", "title": "Sparse Linear Discriminant Analysis", "concept": [ "Supervised Learning" ], "topics": [ "s_SDA" ] }, { "page": "s_SGD", "title": "Stochastic Gradient Descent (SGD) (C, R)", "concept": [ "Supervised Learning" ], "topics": [ "s_SGD" ] }, { "page": "s_SPLS", "title": "Sparse Partial Least Squares Regression (C, R)", "concept": [ "Supervised Learning" ], "topics": [ "s_SPLS" ] }, { "page": "s_SVM", "title": "Support Vector Machines (C, R)", "concept": [ "Supervised Learning" ], "topics": [ "s_SVM" ] }, { "page": "s_TFN", "title": "Feedforward Neural Network with 'tensorflow' (C, R)", "concept": [ "Deep Learning", "Supervised Learning" ], "topics": [ "s_TFN" ] }, { "page": "s_TLS", "title": "Total Least Squares Regression [R]", "topics": [ "s_TLS" ] }, { "page": "s_XGBoost", "title": "XGBoost Classification and Regression (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_XGBoost" ] }, { "page": "s_XRF", "title": "XGBoost Random Forest Classification and Regression (C, R)", "concept": [ "Supervised Learning", "Tree-based methods" ], "topics": [ "s_XRF" ] }, { "page": "savePMML", "title": "Save rtemis model to PMML file", "topics": [ "savePMML" ] }, { "page": "se", "title": "Extract standard error of fit from rtemis model", "topics": [ "se" ] }, { "page": "select_clust", "title": "Select 'rtemis' Clusterer", "topics": [ "select_clust" ] }, { "page": "select_decom", "title": "Select 'rtemis' Decomposer", "topics": [ "select_decom" ] }, { "page": "select_learn", "title": "Select 'rtemis' Learner", "topics": [ "select_learn" ] }, { "page": "selectiter", "title": "Select N of learning iterations based on loss", "topics": [ "selectiter" ] }, { "page": "sensitivity", "title": "Sensitivity", "topics": [ "sensitivity" ] }, { "page": "seql", "title": "Sequence generation with automatic cycling", "topics": [ "seql" ] }, { "page": "setdiffsym", "title": "Symmetric Set Difference", "topics": [ "setdiffsym" ] }, { "page": "setup.bag.resample", "title": "Set resample parameters for 'rtMod' bagging", "topics": [ "setup.bag.resample" ] }, { "page": "setup.color", "title": "Set colorGrad parameters", "topics": [ "setup.color" ] }, { "page": "setup.cv.resample", "title": "'setup.cv.resample': resample defaults for cross-validation", "topics": [ "setup.cv.resample" ] }, { "page": "setup.decompose", "title": "Set decomposition parameters for train_cv '.decompose' argument", "topics": [ "setup.decompose" ] }, { "page": "setup.earlystop", "title": "Set earlystop parameters", "topics": [ "setup.earlystop" ] }, { "page": "setup.GBM", "title": "Set s_GBM parameters", "topics": [ "setup.GBM" ] }, { "page": "setup.grid.resample", "title": "Set resample parameters for 'gridSearchLearn'", "topics": [ "setup.grid.resample" ] }, { "page": "setup.LightRuleFit", "title": "Set s_LightRuleFit parameters", "topics": [ "setup.LightRuleFit" ] }, { "page": "setup.LIHAD", "title": "Set s_LIHAD parameters", "topics": [ "setup.LIHAD" ] }, { "page": "setup.lincoef", "title": "Set lincoef parameters", "topics": [ "setup.lincoef" ] }, { "page": "setup.MARS", "title": "Set s_MARS parameters", "topics": [ "setup.MARS" ] }, { "page": "setup.meta.resample", "title": "Set resample parameters for meta model training", "topics": [ "setup.meta.resample" ] }, { "page": "setup.preprocess", "title": "Set preprocess parameters for train_cv '.preprocess' argument", "topics": [ "setup.preprocess" ] }, { "page": "setup.Ranger", "title": "Set s_Ranger parameters", "topics": [ "setup.Ranger" ] }, { "page": "setup.resample", "title": "Set resample settings", "topics": [ "setup.resample" ] }, { "page": "sge_submit", "title": "Submit expression to SGE grid", "topics": [ "sge_submit" ] }, { "page": "sigmoid", "title": "Sigmoid function", "topics": [ "sigmoid" ] }, { "page": "size", "title": "Size of matrix or vector", "topics": [ "size" ] }, { "page": "softmax", "title": "Softmax function", "topics": [ "softmax" ] }, { "page": "softplus", "title": "Softplus function", "topics": [ "softplus" ] }, { "page": "sortedlines", "title": "lines, but sorted", "topics": [ "sortedlines" ] }, { "page": "sparsernorm", "title": "Sparse rnorm", "topics": [ "sparsernorm" ] }, { "page": "sparseVectorSummary", "title": "Sparseness and pairwise correlation of vectors", "topics": [ "sparseVectorSummary" ] }, { "page": "sparsify", "title": "Sparsify a vector", "topics": [ "sparsify" ] }, { "page": "specificity", "title": "Specificity", "topics": [ "specificity" ] }, { "page": "stderror", "title": "Standard Error of the Mean", "topics": [ "stderror" ] }, { "page": "strat.boot", "title": "Stratified Bootstrap Resampling", "topics": [ "strat.boot" ] }, { "page": "strat.sub", "title": "Resample using Stratified Subsamples", "topics": [ "strat.sub" ] }, { "page": "strata2factor", "title": "Convert 'survfit' object's strata to a factor", "topics": [ "strata2factor" ] }, { "page": "summarize", "title": "Summarize numeric variables", "topics": [ "summarize" ] }, { "page": "summary.massGAM", "title": "'massGAM' object summary", "topics": [ "summary.massGAM" ] }, { "page": "summary.massGLM", "title": "'massGLM' object summary", "topics": [ "summary.massGLM" ] }, { "page": "surv_error", "title": "Survival Analysis Metrics", "topics": [ "surv_error" ] }, { "page": "svd1", "title": "'rtemis-internals' Project Variables to First Eigenvector", "topics": [ "svd1" ] }, { "page": "synth_multimodal", "title": "Create \"Multimodal\" Synthetic Data", "topics": [ "synth_multimodal" ] }, { "page": "synth_reg_data", "title": "Synthesize Simple Regression Data", "topics": [ "synth_reg_data" ] }, { "page": "table1", "title": "Table 1", "topics": [ "table1" ] }, { "page": "theme", "title": "Themes for 'mplot3' and 'dplot3' functions", "topics": [ "theme_black", "theme_blackgrid", "theme_blackigrid", "theme_darkgray", "theme_darkgraygrid", "theme_darkgrayigrid", "theme_lightgraygrid", "theme_mediumgraygrid", "theme_white", "theme_whitegrid", "theme_whiteigrid" ] }, { "page": "themes", "title": "Print available rtemis themes", "topics": [ "themes" ] }, { "page": "timeProc", "title": "Time a process", "topics": [ "timeProc" ] }, { "page": "tohtml", "title": "Generate 'CheckData' object description in HTML", "topics": [ "tohtml" ] }, { "page": "train_cv", "title": "Tune, Train, and Test an 'rtemis' Learner by Nested Resampling", "topics": [ "train_cv" ] }, { "page": "tunable", "title": "Print tunable hyperparameters for a supervised learning algorithm", "topics": [ "tunable" ] }, { "page": "typeset", "title": "Set type of columns", "topics": [ "typeset" ] }, { "page": "uci_heart_failure", "title": "UCI Heart Failure Data", "topics": [ "uci_heart_failure" ] }, { "page": "uniprot_get", "title": "Get protein sequence from UniProt", "topics": [ "uniprot_get" ] }, { "page": "uniquevalsperfeat", "title": "Unique values per feature", "topics": [ "uniquevalsperfeat" ] }, { "page": "winsorize", "title": "Winsorize vector", "topics": [ "winsorize" ] }, { "page": "x_CCA", "title": "Sparse Canonical Correlation Analysis (CCA)", "concept": [ "Cross-Decomposition" ], "topics": [ "x_CCA" ] }, { "page": "xlsx2list", "title": "Read all sheets of an XLSX file into a list", "topics": [ "xlsx2list" ] }, { "page": "xselect_decom", "title": "Select 'rtemis' cross-decomposer", "concept": [ "Cross-Decomposition" ], "topics": [ "xselect_decom" ] }, { "page": "xtdescribe", "title": "Describe longitudinal dataset", "topics": [ "xtdescribe" ] }, { "page": "zip2longlat", "title": "Get Longitude and Lattitude for zip code(s)", "topics": [ "zip2longlat" ] }, { "page": "zipdist", "title": "Get distance between pairs of zip codes", "topics": [ "zipdist" ] } ], "_readme": "https://github.com/rtemis-org/rtemis-legacy/raw/HEAD/README.md", "_rundeps": [ "base64enc", "codetools", "data.table", "digest", "fastmap", "future", "globals", "htmltools", "listenv", "parallelly", "R6", "rlang" ], "_score": 1.6989700043360187, "_indexed": false, "_nocasepkg": "rtemisalpha", "_universes": [ "egenn" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.98.3", "date": "2026-07-03T12:25:02.000Z", "distro": "resolute", "commit": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "fileid": "https://r2.ropensci.org/c4b9c30859ba6cc9fa0b4417902ec294f654fe3fc515538c43507d5bfe7c5286", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115" }, { "r": "4.6.1", "os": "linux", "version": "0.98.3", "date": "2026-07-03T12:24:45.000Z", "distro": "resolute", "commit": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "fileid": "https://r2.ropensci.org/a96bf56e9fd5bd9e6830ee3c5135d304a80d049344c85560a4baf7a9aebe101f", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115" }, { "r": "4.5.3", "os": "mac", "version": "0.98.3", "date": "2026-07-03T12:23:01.000Z", "commit": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "fileid": "https://r2.ropensci.org/9594bf0172debf36b2a2954dc730d0f77db49e89510a9dc70a66dda381deca40", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115" }, { "r": "4.6.1", "os": "mac", "version": "0.98.3", "date": "2026-07-03T12:26:14.000Z", "commit": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "fileid": "https://r2.ropensci.org/5571257f4002f6b8301e85f45b72f0d318c0439daed4048002a67ea68c757177", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115" }, { "r": "4.6.0", "os": "wasm", "version": "0.98.3", "date": "2026-07-03T12:25:08.000Z", "commit": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "fileid": "https://r2.ropensci.org/952aa2f04b8efc400daf738963310b70d89665742247713586dafc0fac7e7a73", "status": "success", "buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115" }, { "r": "4.7.0", "os": "win", "version": "0.98.3", "date": "2026-07-03T12:23:37.000Z", "commit": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "fileid": "https://r2.ropensci.org/c28b6d12c72545a47f028dc301c5d180f6ab2de0a947762af9559edb59b640aa", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115" }, { "r": "4.5.3", "os": "win", "version": "0.98.3", "date": "2026-07-03T12:22:38.000Z", "commit": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "fileid": "https://r2.ropensci.org/d7a022b67b2f52ef9b2aef9100bf1d792aba339a4836d7504ded597178d7973e", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115" }, { "r": "4.6.1", "os": "win", "version": "0.98.3", "date": "2026-07-03T12:22:33.000Z", "commit": "803ef6c82ddf19ff65ed0cf0b520aeec114c0557", "fileid": "https://r2.ropensci.org/a91044952352bc0bfab3bb662571f913a88606113674d481c7b3b0585d35e429", "status": "success", "check": "WARNING", "buildurl": "https://github.com/r-universe/egenn/actions/runs/28659824115" } ] }