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  "Title": "Simulating Climate Data for Research and Modelling",
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  "Description": "Advanced climate simulation, forecasting, visualization,\nexport, and machine learning tools. Generates synthetic climate\ndatasets for single or multiple weather stations using\nstochastic weather generation techniques. 'CDSimX' simulates\ndaily climate variables including minimum and maximum\ntemperature, rainfall, relative humidity, solar radiation, wind\nspeed, wind direction, dew point temperature, and potential\nevapotranspiration. The package incorporates seasonal harmonic\nmodels, Markov chain rainfall occurrence processes,\nGamma-distributed rainfall amounts, copula-based dependence\nstructures, bias-correction procedures, and physical\nconsistency constraints. 'CDSimX' supports climate data\ngeneration, environmental modeling, machine learning\nbenchmarking, sensitivity analysis, and educational\napplications. Methods are based on established stochastic\nweather generation approaches described in Richardson (1981)\n<doi:10.1029/WR017i001p00182>, Wilks (1999)\n<doi:10.1016/S0168-1923(99)00037-4>, and Osei et al. (2026)\n<doi:10.5334/jors.666>.",
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      "page": "apply_physical_constraints",
      "title": "Apply Physical Constraints to Climate Data",
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    {
      "page": "bias_correction",
      "title": "Bias Correction for Simulated Climate Data",
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      "title": "Apply Copula Dependence Structure to Climate Variables",
      "topics": [
        "copula_dependence"
      ]
    },
    {
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      "topics": [
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    },
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      "title": "Export CDSimX Data to CSV",
      "topics": [
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      ]
    },
    {
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      "title": "Export CDSimX Data to NetCDF",
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