{
  "_id": "6ab238203d719c1f8408a6de",
  "Package": "riemannianStats",
  "Type": "Package",
  "Title": "Riemannian Methods for Principal Component Analysis, Regression\nand Visualization",
  "Version": "0.2.0",
  "Authors@R": "c(\nperson(\ngiven = \"Oldemar\",\nfamily = \"Rodríguez Rojas\",\nemail = \"oldemar.rodriguez@ucr.ac.cr\",\nrole = c(\"aut\", \"cre\")\n),\nperson(\ngiven = \"Jennifer\",\nfamily = \"Lobo Vásquez\",\nemail = \"jennifer.lobo@promidat.com\",\nrole = c(\"aut\")\n)\n)",
  "Description": "Provides tools for statistical analysis on Riemannian\nmanifolds using local geometry derived from Uniform Manifold\nApproximation and Projection (UMAP), Isometric Mapping\n(Isomap), and Density-Based Spatial Clustering of Applications\nwith Noise (DBSCAN). The package supports dimensionality\nreduction, visualization, Riemannian principal component\nanalysis, and Riemannian linear regression for multivariate\ndata analysis. Methods based on Uniform Manifold Approximation\nand Projection follow McInnes et al. (2018)\n<doi:10.21105/joss.00861>.",
  "License": "BSD_3_clause + file LICENSE",
  "Encoding": "UTF-8",
  "Language": "en-US",
  "Config/testthat/edition": "3",
  "VignetteBuilder": "knitr, rmarkdown",
  "Config/roxygen2/version": "8.0.0",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-09-22 08:07:36 UTC",
    "User": "root"
  },
  "Author": "Oldemar Rodríguez Rojas [aut, cre], Jennifer Lobo Vásquez [aut]",
  "Maintainer": "Oldemar Rodríguez Rojas <oldemar.rodriguez@ucr.ac.cr>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-07-23 16:51:41 UTC",
  "RemoteUrl": "https://github.com/cran/riemannianStats",
  "RemoteRef": "HEAD",
  "RemoteSha": "8a107f82641a3dc016f8b818e0a2f199f0a166ad",
  "_user": "cran",
  "_type": "src",
  "_file": "riemannianStats_0.2.0.tar.gz",
  "_fileid": "https://r2.ropensci.org/f5c132667f313547957d11cb80082d4d8b9e6534de585ca65ebf50a74f386820",
  "_filesize": 1842306,
  "_sha256": "f5c132667f313547957d11cb80082d4d8b9e6534de585ca65ebf50a74f386820",
  "_expires": "2026-12-31T08:11:10.000Z",
  "_created": "2026-09-22T08:07:36.000Z",
  "_published": "2026-09-22T08:11:12.241Z",
  "_jobs": [
    {
      "job": 106665659834,
      "time": 175,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "10683206947"
    },
    {
      "job": 106665659923,
      "time": 172,
      "config": "linux-release-x86_64",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "10682922698"
    },
    {
      "job": 106664277242,
      "time": 249,
      "config": "source",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "10683501153"
    },
    {
      "job": 106665659854,
      "time": 119,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "10683136998"
    },
    {
      "job": 106665659900,
      "time": 106,
      "config": "windows-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "10683890222"
    },
    {
      "job": 106665660010,
      "time": 113,
      "config": "windows-release-x86_64",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "10683271872"
    }
  ],
  "_host": "GitHub-Actions",
  "_buildurl": "https://github.com/r-universe/cran/actions/runs/35702669847",
  "_status": "success",
  "_upstream": "https://github.com/cran/riemannianStats",
  "_commit": {
    "id": "8a107f82641a3dc016f8b818e0a2f199f0a166ad",
    "author": "Oldemar Rodríguez Rojas <oldemar.rodriguez@ucr.ac.cr>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 0.2.0\n",
    "time": 1784825501
  },
  "_maintainer": {
    "name": "Oldemar Rodríguez Rojas",
    "email": "oldemar.rodriguez@ucr.ac.cr"
  },
  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 4.1",
      "role": "Depends"
    },
    {
      "package": "rlang",
      "role": "Imports"
    },
    {
      "package": "ggplot2",
      "role": "Imports"
    },
    {
      "package": "ggrepel",
      "role": "Imports"
    },
    {
      "package": "grid",
      "role": "Imports"
    },
    {
      "package": "uwot",
      "role": "Imports"
    },
    {
      "package": "vegan",
      "role": "Imports"
    },
    {
      "package": "dbscan",
      "role": "Imports"
    },
    {
      "package": "scatterplot3d",
      "role": "Suggests"
    },
    {
      "package": "plotly",
      "role": "Suggests"
    },
    {
      "package": "testthat",
      "version": ">= 3.0.0",
      "role": "Suggests"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    }
  ],
  "_owner": "cran",
  "_selfowned": false,
  "_usedby": 0,
  "_updates": [
    {
      "week": "2026-29",
      "n": 1
    },
    {
      "week": "2026-30",
      "n": 1
    }
  ],
  "_tags": [
    {
      "name": "0.1.1",
      "date": "2026-07-17"
    },
    {
      "name": "0.2.0",
      "date": "2026-07-23"
    }
  ],
  "_stars": 0,
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "followers": 630,
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 283,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/riemannianStats"
  },
  "_searchresults": 12,
  "_rbuild": "4.6.1",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/manual.pdf",
    "extra/riemannianStats.html",
    "LICENSE"
  ],
  "_realowner": "cran",
  "_cranurl": false,
  "_releases": [
    {
      "version": "0.1.1",
      "date": "2026-07-17"
    },
    {
      "version": "0.2.0",
      "date": "2026-07-23"
    }
  ],
  "_exports": [
    "riem.biplot",
    "riem.center.data",
    "riem.cor",
    "riem.cov",
    "riem.diff",
    "riem.dist",
    "riem.ind.coord",
    "riem.inertia",
    "riem.lm",
    "riem.mean.index",
    "riem.norm",
    "riem.original.scale",
    "riem.pca",
    "riem.plot",
    "riem.plot3d",
    "riem.rho",
    "riem.similarities.dbscan",
    "riem.similarities.isomap",
    "riem.similarities.umap",
    "riem.var.coord"
  ],
  "_help": [
    {
      "page": "coef.riem.lm",
      "title": "Extract Riemannian Regression Coefficients",
      "topics": [
        "coef.riem.lm"
      ]
    },
    {
      "page": "fitted.riem.lm",
      "title": "Extract Riemannian Fitted Values",
      "topics": [
        "fitted.riem.lm"
      ]
    },
    {
      "page": "print.riem.lm",
      "title": "Print a Riemannian Linear Regression Model",
      "topics": [
        "print.riem.lm"
      ]
    },
    {
      "page": "print.riem.pca",
      "title": "Print method for Riemannian PCA",
      "topics": [
        "print.riem.pca"
      ]
    },
    {
      "page": "print.summary.riem.lm",
      "title": "Print a Summary of a Riemannian Linear Regression Model",
      "topics": [
        "print.summary.riem.lm"
      ]
    },
    {
      "page": "residuals.riem.lm",
      "title": "Extract Riemannian Residuals",
      "topics": [
        "residuals.riem.lm"
      ]
    },
    {
      "page": "riem.biplot",
      "title": "Riemannian PCA Biplot",
      "topics": [
        "riem.biplot"
      ]
    },
    {
      "page": "riem.biplot.default",
      "title": "Default Method for Riemannian PCA Biplots",
      "topics": [
        "riem.biplot.default"
      ]
    },
    {
      "page": "riem.biplot.riem.pca",
      "title": "Riemannian PCA Biplot",
      "topics": [
        "riem.biplot.riem.pca"
      ]
    },
    {
      "page": "riem.center.data",
      "title": "Center Data Around the Riemannian Mean Observation",
      "topics": [
        "riem.center.data"
      ]
    },
    {
      "page": "riem.cor",
      "title": "Calculate the Riemannian Correlation Matrix",
      "topics": [
        "riem.cor"
      ]
    },
    {
      "page": "riem.cov",
      "title": "Calculate a Riemannian Covariance Matrix",
      "topics": [
        "riem.cov"
      ]
    },
    {
      "page": "riem.diff",
      "title": "Calculate Riemannian Vector Differences",
      "topics": [
        "riem.diff"
      ]
    },
    {
      "page": "riem.dist",
      "title": "Calculate the Distance Matrix",
      "topics": [
        "riem.dist"
      ]
    },
    {
      "page": "riem.ind.coord",
      "title": "Calculate Riemannian Principal Components",
      "topics": [
        "riem.ind.coord"
      ]
    },
    {
      "page": "riem.inertia",
      "title": "Calculate PCA Inertia for Two Components",
      "topics": [
        "riem.inertia"
      ]
    },
    {
      "page": "riem.lm",
      "title": "Riemannian Linear Regression",
      "topics": [
        "riem.lm"
      ]
    },
    {
      "page": "riem.mean.index",
      "title": "Find the Riemannian Mean Observation Index",
      "topics": [
        "riem.mean.index"
      ]
    },
    {
      "page": "riem.norm",
      "title": "Calculate the Euclidean Norm",
      "topics": [
        "riem.norm"
      ]
    },
    {
      "page": "riem.original.scale",
      "title": "Convert a Riemannian Regression Fit to the Original Scale",
      "topics": [
        "riem.original.scale"
      ]
    },
    {
      "page": "riem.pca",
      "title": "Riemannian Principal Component Analysis Riemannian Principal Component Analysis",
      "topics": [
        "riem.pca"
      ]
    },
    {
      "page": "riem.plot",
      "title": "Plot Riemannian PCA Results",
      "topics": [
        "riem.plot"
      ]
    },
    {
      "page": "riem.plot.default",
      "title": "Default Method for Riemannian PCA Plots",
      "topics": [
        "riem.plot.default"
      ]
    },
    {
      "page": "riem.plot.riem.pca",
      "title": "Plot Riemannian PCA Results",
      "topics": [
        "riem.plot.riem.pca"
      ]
    },
    {
      "page": "riem.plot3d",
      "title": "Plot a Three-Dimensional Scatter Plot",
      "topics": [
        "riem.plot3d"
      ]
    },
    {
      "page": "riem.rho",
      "title": "Calculate the Rho Matrix",
      "topics": [
        "riem.rho"
      ]
    },
    {
      "page": "riem.similarities.dbscan",
      "title": "Calculate DBSCAN-Induced Similarities",
      "topics": [
        "riem.similarities.dbscan"
      ]
    },
    {
      "page": "riem.similarities.isomap",
      "title": "Calculate Isomap Graph Similarities",
      "topics": [
        "riem.similarities.isomap"
      ]
    },
    {
      "page": "riem.similarities.umap",
      "title": "Calculate UMAP Graph Similarities",
      "topics": [
        "riem.similarities.umap"
      ]
    },
    {
      "page": "riem.var.coord",
      "title": "Calculate Riemannian Correlations Between Variables and Components",
      "topics": [
        "riem.var.coord"
      ]
    },
    {
      "page": "summary.riem.lm",
      "title": "Summarize a Riemannian Linear Regression Model",
      "topics": [
        "summary.riem.lm"
      ]
    }
  ],
  "_rundeps": [
    "BH",
    "cli",
    "cluster",
    "cpp11",
    "dbscan",
    "dqrng",
    "farver",
    "FNN",
    "generics",
    "ggplot2",
    "ggrepel",
    "glue",
    "gtable",
    "irlba",
    "isoband",
    "labeling",
    "lattice",
    "lifecycle",
    "MASS",
    "Matrix",
    "mgcv",
    "nlme",
    "permute",
    "R6",
    "RColorBrewer",
    "Rcpp",
    "RcppAnnoy",
    "RcppEigen",
    "RcppProgress",
    "rlang",
    "RSpectra",
    "S7",
    "scales",
    "sitmo",
    "uwot",
    "vctrs",
    "vegan",
    "viridisLite",
    "withr"
  ],
  "_vignettes": [
    {
      "source": "riemannian-regression-umap-dbscan-isomap.Rmd",
      "filename": "riemannian-regression-umap-dbscan-isomap.html",
      "title": "Classical and Riemannian Linear Regression with UMAP, ISOMAP, and DBSCAN",
      "author": "Oldemar Rodríguez Rojas & Jennifer Lobo Vásquez",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "1. Load the package",
        "2. Load the data set",
        "3. Fit a classical linear regression model",
        "4. Fit a Riemannian linear regression model using a formula",
        "Formula specification options",
        "Use one explanatory variable",
        "List the explanatory variables explicitly",
        "Use all the remaining variables",
        "Exclude a variable",
        "Include an interaction",
        "Include a mathematical transformation",
        "Include categorical variables",
        "5. Fit the model by passing the variables separately",
        "6. Inspect the Riemannian regression results with UMAP",
        "7. Convert UMAP Riemannian results to the original scale",
        "8. Fit a Riemannian linear regression model using ISOMAP similarities",
        "9. Inspect the Riemannian regression results with ISOMAP",
        "10. Convert ISOMAP Riemannian results to the original scale",
        "11. Fit a Riemannian linear regression model using DBSCAN similarities",
        "12. Inspect the Riemannian regression results with DBSCAN",
        "13. Convert DBSCAN Riemannian results to the original scale",
        "14. Compare the models",
        "15. Summary"
      ],
      "created": "2026-07-23 16:51:41",
      "modified": "2026-07-23 16:51:41",
      "commits": 1
    },
    {
      "source": "student-pca-umap-complete-workflow.Rmd.Rmd",
      "filename": "student-pca-umap-complete-workflow.Rmd.html",
      "title": "Complete Riemannian PCA Workflow with riem.pca",
      "author": "Oldemar Rodríguez Rojas & Jennifer Lobo Vásquez",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "1. Create the data set",
        "2. Prepare the data for the analysis",
        "3. Compute UMAP similarities",
        "4. Compute the Rho matrix",
        "5. Compute Riemannian differences and distances",
        "6. Compute Riemannian covariance and correlation",
        "7. Compute Riemannian principal components",
        "8. Compute explained inertia",
        "9. Interpret the subjects in the reduced space",
        "10. Visualize the principal plane",
        "11. Visualize the correlation circle",
        "12. Visualize the biplot"
      ],
      "created": "2026-07-23 16:51:41",
      "modified": "2026-07-23 16:51:41",
      "commits": 1
    },
    {
      "source": "data10d250-pca-umap-step-by-step.Rmd",
      "filename": "data10d250-pca-umap-step-by-step.html",
      "title": "Step-by-Step Riemannian PCA with UMAP on Synthetic Data",
      "author": "Oldemar Rodríguez Rojas & Jennifer Lobo Vásquez",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "1. Load the data set",
        "2. Prepare the data",
        "3. Choose the number of neighbors",
        "4. Compute the UMAP similarities",
        "5. Compute the Rho matrix",
        "6. Compute the Riemannian vector differences",
        "7. Compute the UMAP-based Riemannian distance matrix",
        "8. Riemannian correlation matrix",
        "10. Riemannian covariance matrix",
        "10. Riemannian principal components",
        "11. Explained inertia",
        "12. Correlations between variables and components",
        "Estan mal los resultado******************* Dan mal los negativos",
        "13. Visualizations",
        "13.1 Principal plane",
        "13.2 Correlation circle",
        "13.3 Biplot",
        "13.4 3D plot"
      ],
      "created": "2026-07-23 16:51:41",
      "modified": "2026-07-23 16:51:41",
      "commits": 1
    },
    {
      "source": "student-pca-umap-step-by-step.Rmd",
      "filename": "student-pca-umap-step-by-step.html",
      "title": "Simple Example of Riemannian STATS: Student DataSet - ACP-UMAP",
      "author": "Oldemar Rodríguez Rojas & Jennifer Lobo Vásquez",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "1. Load the package",
        "2. Create the student data set",
        "3. Prepare the data for the analysis",
        "4. Fit the Riemannian PCA model",
        "5. Inspect the eigenvalues",
        "6. Inspect the UMAP similarity matrix",
        "7. Inspect the Rho matrix",
        "8. Inspect the Riemannian differences",
        "9. Inspect the Riemannian distance matrix",
        "10. Inspect the Riemannian covariance matrix",
        "11. Inspect the Riemannian correlation matrix",
        "12. Inspect the individual coordinates",
        "13. Inspect the variable coordinates",
        "14. Plot individuals using the object interface",
        "15. Plot variables using the object interface",
        "16. Create a Riemannian biplot using the object interface",
        "17. Plot individuals using the manual interface",
        "18. Plot variables using the manual interface",
        "19. Summary"
      ],
      "created": "2026-07-23 16:51:41",
      "modified": "2026-07-23 16:51:41",
      "commits": 1
    }
  ],
  "_score": 2.982271233039569,
  "_indexed": true,
  "_nocasepkg": "riemannianstats",
  "_universes": [
    "cran"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.2.0",
      "date": "2026-09-22T08:10:22.000Z",
      "distro": "resolute",
      "commit": "8a107f82641a3dc016f8b818e0a2f199f0a166ad",
      "fileid": "https://r2.ropensci.org/18c4705c287c13ebfd11e51693c84e3340091ef20329a36b67ebf7693e43a40a",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/35702669847"
    },
    {
      "r": "4.6.1",
      "os": "linux",
      "version": "0.2.0",
      "date": "2026-09-22T08:10:18.000Z",
      "distro": "resolute",
      "commit": "8a107f82641a3dc016f8b818e0a2f199f0a166ad",
      "fileid": "https://r2.ropensci.org/94ad3dc4c9ebcd34c895b6f30aa8d8fbc326be6a8c6ba7fbf2f10087fc6de84f",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/35702669847"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "0.2.0",
      "date": "2026-09-22T08:09:54.000Z",
      "commit": "8a107f82641a3dc016f8b818e0a2f199f0a166ad",
      "fileid": "https://r2.ropensci.org/a32eb0a4e9873b42d0a09264ce509bd1bc1401e5bd7126cebba7efc9838d712e",
      "status": "success",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/35702669847"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "0.2.0",
      "date": "2026-09-22T08:09:11.000Z",
      "commit": "8a107f82641a3dc016f8b818e0a2f199f0a166ad",
      "fileid": "https://r2.ropensci.org/d9ae8e6cf3cd764b0f2f27143fa850032cd46acc74e003de32de25602d6e51f1",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/35702669847"
    },
    {
      "r": "4.6.1",
      "os": "win",
      "version": "0.2.0",
      "date": "2026-09-22T08:09:18.000Z",
      "commit": "8a107f82641a3dc016f8b818e0a2f199f0a166ad",
      "fileid": "https://r2.ropensci.org/9f4a5af9c6596460cceb553d36d3882e2a63ec115b75a365b0f744b55ed26474",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/35702669847"
    }
  ]
}