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  "Title": "Electric Vehicle Charging Sessions Profiling and Modelling",
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  "Maintainer": "Marc Cañigueral <marccanyigueral@gmail.com>",
  "Description": "Tools for modelling electric vehicle charging sessions\ninto generic groups with similar connection patterns called\n\"user profiles\", using Gaussian Mixture Models clustering. The\nclustering and profiling methodology is described in Cañigueral\nand Meléndez (2021, ISBN:0142-0615)\n<doi:10.1016/j.ijepes.2021.107195>.",
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  "Repository": "https://resourcefully-dev.r-universe.dev",
  "Date/Publication": "2025-12-22 11:11:45 UTC",
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    "cluster_sessions",
    "cut_sessions",
    "define_clusters",
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    "divide_by_timecycle",
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    "get_daily_n_sessions",
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    "get_energy_models",
    "get_ev_model",
    "plot_bivarGMM",
    "plot_connection_models",
    "plot_density_2D",
    "plot_density_3D",
    "plot_division_lines",
    "plot_energy_models",
    "plot_histogram",
    "plot_histogram_grid",
    "plot_kNNdist",
    "plot_outliers",
    "plot_points",
    "read_ev_model",
    "round_to_interval",
    "save_clustering_iterations",
    "save_ev_model",
    "set_profiles",
    "summarise_sessions"
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      "title": "EV model example",
      "object": "california_ev_model",
      "class": [
        "evmodel"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "california_ev_sessions",
      "title": "EV charging sessions example",
      "object": "california_ev_sessions",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
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        "Session",
        "ConnectionStartDateTime",
        "ConnectionEndDateTime",
        "ChargingStartDateTime",
        "ChargingEndDateTime",
        "Power",
        "Energy",
        "ConnectionHours",
        "ChargingHours",
        "FlexibilityHours",
        "ChargingStation",
        "UserID"
      ],
      "rows": 30114,
      "table": true,
      "tojson": true
    },
    {
      "name": "california_ev_sessions_profiles",
      "title": "Clustered EV charging sessions example",
      "object": "california_ev_sessions_profiles",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "Profile",
        "Session",
        "ConnectionStartDateTime",
        "ConnectionEndDateTime",
        "ChargingStartDateTime",
        "ChargingEndDateTime",
        "Power",
        "Energy",
        "ConnectionHours",
        "ChargingHours",
        "FlexibilityHours",
        "ChargingStation",
        "UserID",
        "Timecycle",
        "Cluster"
      ],
      "rows": 28447,
      "table": true,
      "tojson": true
    },
    {
      "name": "california_GMM",
      "title": "Gaussian Mixture Models examples",
      "object": "california_GMM",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "sessions_feature_names",
      "title": "Names of standard features of a sessions dataset",
      "object": "sessions_feature_names",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "sessions_summary_feature_names",
      "title": "Names of features to summarise in evprof functions",
      "object": "sessions_summary_feature_names",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
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  "_help": [
    {
      "page": "choose_k_GMM",
      "title": "Visualize BIC indicator to choose the number of clusters",
      "topics": [
        "choose_k_GMM"
      ]
    },
    {
      "page": "cluster_sessions",
      "title": "Cluster sessions with 'mclust' package",
      "topics": [
        "cluster_sessions"
      ]
    },
    {
      "page": "cut_sessions",
      "title": "Cut outliers based on minimum and maximum limits of ConnectionHours and ConnectionStartDateTime variables",
      "topics": [
        "cut_sessions"
      ]
    },
    {
      "page": "define_clusters",
      "title": "Define each cluster with a user profile interpretation",
      "topics": [
        "define_clusters"
      ]
    },
    {
      "page": "detect_outliers",
      "title": "Detect outliers",
      "topics": [
        "detect_outliers"
      ]
    },
    {
      "page": "divide_by_disconnection",
      "title": "Divide sessions by disconnection day",
      "topics": [
        "divide_by_disconnection"
      ]
    },
    {
      "page": "divide_by_timecycle",
      "title": "Divide sessions by time-cycle",
      "topics": [
        "divide_by_timecycle"
      ]
    },
    {
      "page": "drop_outliers",
      "title": "Drop outliers",
      "topics": [
        "drop_outliers"
      ]
    },
    {
      "page": "get_charging_rates_distribution",
      "title": "Get charging rates distribution in percentages",
      "topics": [
        "get_charging_rates_distribution"
      ]
    },
    {
      "page": "get_connection_models",
      "title": "Get a tibble of connection GMM for every user profile",
      "topics": [
        "get_connection_models"
      ]
    },
    {
      "page": "get_daily_avg_n_sessions",
      "title": "Get the daily average number of sessions given a range of years, months and weekdays",
      "topics": [
        "get_daily_avg_n_sessions"
      ]
    },
    {
      "page": "get_daily_n_sessions",
      "title": "Get daily number of sessions given a range of years, months and weekdays",
      "topics": [
        "get_daily_n_sessions"
      ]
    },
    {
      "page": "get_dbscan_params",
      "title": "Get the minPts and eps values for DBSCAN to label only a specific percentage as noise",
      "topics": [
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      ]
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    {
      "page": "get_energy_models",
      "title": "Get a tibble of energy GMM for every user profile",
      "topics": [
        "get_energy_models"
      ]
    },
    {
      "page": "get_ev_model",
      "title": "Get the EV model object of class 'evmodel'",
      "topics": [
        "get_ev_model"
      ]
    },
    {
      "page": "plot_bivarGMM",
      "title": "Plot Bivariate Gaussian Mixture Models",
      "topics": [
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      ]
    },
    {
      "page": "plot_connection_models",
      "title": "Plot all bi-variable GMM (clusters) with the colors corresponding to the assigned user profile. This shows which clusters correspond to which user profile, and the proportion of every user profile.",
      "topics": [
        "plot_connection_models"
      ]
    },
    {
      "page": "plot_density_2D",
      "title": "Density plot in 2D, considering Start time and Connection duration as variables",
      "topics": [
        "plot_density_2D"
      ]
    },
    {
      "page": "plot_density_3D",
      "title": "Density plot in 3D, considering Start time and Connection duration as variables",
      "topics": [
        "plot_density_3D"
      ]
    },
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      "page": "plot_division_lines",
      "title": "Iteration over evprof::plot_division_line function to plot multiple lines",
      "topics": [
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      "page": "plot_energy_models",
      "title": "Compare density of estimated energy with density of real energy vector",
      "topics": [
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    {
      "page": "plot_histogram",
      "title": "Histogram of a variable from sessions data set",
      "topics": [
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    },
    {
      "page": "plot_histogram_grid",
      "title": "Grid of multiple variable histograms",
      "topics": [
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    {
      "page": "plot_kNNdist",
      "title": "Plot kNNdist",
      "topics": [
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    {
      "page": "plot_outliers",
      "title": "Plot outlying sessions",
      "topics": [
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      "page": "plot_points",
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      "topics": [
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      "topics": [
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      "page": "round_to_interval",
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      "topics": [
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      "page": "set_profiles",
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      "topics": [
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    {
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