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  "Title": "Short Asynchronous Time-Series Analysis",
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  "Date": "2024-02-26",
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  "Description": "A graphical and automated pipeline for the analysis of\nshort time-series in R ('santaR'). This approach is designed to\naccommodate asynchronous time sampling (i.e. different time\npoints for different individuals), inter-individual\nvariability, noisy measurements and large numbers of variables.\nBased on a smoothing splines functional model, 'santaR' is able\nto detect variables highlighting significantly different\ntemporal trajectories between study groups. Designed initially\nfor metabolic phenotyping, 'santaR' is also suited for other\nSystems Biology disciplines. Command line and graphical\nanalysis (via a 'shiny' application) enable fast and parallel\nautomated analysis and reporting, intuitive visualisation and\ncomprehensive plotting options for non-specialist users.",
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  "BugReports": "https://github.com/adwolfer/santaR/issues/new",
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  "Repository": "https://adwolfer.r-universe.dev",
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      "date": "2018-01-24"
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      "date": "2022-05-24"
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    "get_eigen_DFoverlay_list",
    "get_eigen_spline",
    "get_grouping",
    "get_ind_time_matrix",
    "get_param_evolution",
    "plot_nbTP_histogram",
    "plot_param_evolution",
    "santaR_auto_fit",
    "santaR_auto_summary",
    "santaR_CBand",
    "santaR_fit",
    "santaR_plot",
    "santaR_pvalue_dist",
    "santaR_pvalue_dist_within",
    "santaR_pvalue_fit",
    "santaR_pvalue_fit_within",
    "santaR_start_GUI"
  ],
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      "name": "acuteInflammation",
      "title": "Measurement of 22 inflammatory mediators across time",
      "object": "acuteInflammation",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "acuteInflammation",
      "title": "Measurement of 22 inflammatory mediators across time",
      "topics": [
        "acuteInflammation"
      ]
    },
    {
      "page": "AIC_smooth_spline",
      "title": "Calculate the Akaike Information Criterion for a smooth.spline",
      "topics": [
        "AIC_smooth_spline"
      ]
    },
    {
      "page": "AICc_smooth_spline",
      "title": "Calculate the Akaike Information Criterion Corrected for small observation numbers for a smooth.spline",
      "topics": [
        "AICc_smooth_spline"
      ]
    },
    {
      "page": "BIC_smooth_spline",
      "title": "Calculate the Bayesian Information Criterion for a smooth.spline",
      "topics": [
        "BIC_smooth_spline"
      ]
    },
    {
      "page": "get_eigen_DF",
      "title": "Compute the optimal df and weighted-df using 5 spline fitting metric",
      "concept": [
        "DFsearch"
      ],
      "topics": [
        "get_eigen_DF"
      ]
    },
    {
      "page": "get_eigen_DFoverlay_list",
      "title": "Plot for each eigenSpline the automatically fitted spline, splines for all df and a spline at a chosen df",
      "concept": [
        "DFsearch"
      ],
      "topics": [
        "get_eigen_DFoverlay_list"
      ]
    },
    {
      "page": "get_eigen_spline",
      "title": "Compute eigenSplines across a dataset",
      "concept": [
        "DFsearch"
      ],
      "topics": [
        "get_eigen_spline"
      ]
    },
    {
      "page": "get_eigen_spline_matrix",
      "title": "Generate a Ind x Time + Var data.frame concatenating all variables from input variable",
      "topics": [
        "get_eigen_spline_matrix"
      ]
    },
    {
      "page": "get_grouping",
      "title": "Generate a matrix of group membership for all individuals",
      "concept": [
        "Analysis"
      ],
      "topics": [
        "get_grouping"
      ]
    },
    {
      "page": "get_ind_time_matrix",
      "title": "Generate a Ind x Time DataFrame from input data",
      "concept": [
        "Analysis"
      ],
      "topics": [
        "get_ind_time_matrix"
      ]
    },
    {
      "page": "get_param_evolution",
      "title": "Compute the value of different fitting metrics over all possible df for each eigenSpline",
      "concept": [
        "DFsearch"
      ],
      "topics": [
        "get_param_evolution"
      ]
    },
    {
      "page": "loglik_smooth_spline",
      "title": "Calculate the penalised loglikelihood of a smooth.spline",
      "topics": [
        "loglik_smooth_spline"
      ]
    },
    {
      "page": "plot_nbTP_histogram",
      "title": "Plot an histogram of the number of time-trajectories with a given number of time-points",
      "concept": [
        "DFsearch"
      ],
      "topics": [
        "plot_nbTP_histogram"
      ]
    },
    {
      "page": "plot_param_evolution",
      "title": "Plot the evolution of different fitting parameters across all possible df for each eigenSpline",
      "concept": [
        "DFsearch"
      ],
      "topics": [
        "plot_param_evolution"
      ]
    },
    {
      "page": "santaR",
      "title": "santaR: A package for Short AsyNchronous Time-series Analysis in R",
      "topics": [
        "santaR-package",
        "SANTAR",
        "santaR"
      ]
    },
    {
      "page": "santaR_auto_fit",
      "title": "Automate all steps of santaR fitting, Confidence bands estimation and p-values calculation for one or multiple variables",
      "concept": [
        "Analysis",
        "AutoProcess"
      ],
      "topics": [
        "santaR_auto_fit"
      ]
    },
    {
      "page": "santaR_auto_summary",
      "title": "Summarise, report and save the results of a santaR analysis",
      "concept": [
        "Analysis",
        "AutoProcess"
      ],
      "topics": [
        "santaR_auto_summary"
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    },
    {
      "page": "santaR_CBand",
      "title": "Compute Group Mean Curve Confidence Bands",
      "concept": [
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      ],
      "topics": [
        "santaR_CBand"
      ]
    },
    {
      "page": "santaR_fit",
      "title": "Generate a SANTAObj for a variable",
      "concept": [
        "Analysis"
      ],
      "topics": [
        "santaR_fit"
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    },
    {
      "page": "santaR_plot",
      "title": "Plot a SANTAObj",
      "concept": [
        "Analysis",
        "AutoProcess"
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      "topics": [
        "santaR_plot"
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    {
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      "concept": [
        "Analysis"
      ],
      "topics": [
        "santaR_pvalue_dist"
      ]
    },
    {
      "page": "santaR_pvalue_dist_within",
      "title": "Evaluate difference between a group mean curve and a constant model",
      "topics": [
        "santaR_pvalue_dist_within"
      ]
    },
    {
      "page": "santaR_pvalue_fit",
      "title": "Evaluate difference in group trajectories based on the comparison of model fit (F-test) between one and two groups",
      "concept": [
        "Analysis"
      ],
      "topics": [
        "santaR_pvalue_fit"
      ]
    },
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      ]
    },
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      "concept": [
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        "AutoProcess"
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      "topics": [
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    },
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      "title": "Unit-Variance scaling of each column",
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      "filename": "advanced-command-line-functions.html",
      "title": "Advanced command line functions",
      "author": "Arnaud Wolfer",
      "engine": "knitr::rmarkdown",
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      "title": "santaR Theoretical Background",
      "author": "Arnaud Wolfer",
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        "Concept of Functional Data Analysis",
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        "Automated model assessment and selection approaches",
        "Latent time-trajectories for df selection",
        "Smoothness and experimental design",
        "See Also"
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