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  "Package": "midasr",
  "Title": "Mixed Data Sampling Regression",
  "Description": "Methods and tools for mixed frequency time series data\nanalysis. Allows estimation, model selection and forecasting\nfor MIDAS regressions.",
  "URL": "http://mpiktas.github.io/midasr/",
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  "Authors@R": "c(\nperson(given = \"Vaidotas\",\nfamily = \"Zemlys-Balevičius\",\nemail = \"zemlys@gmail.com\",\nrole = \"cre\"),\nperson(given = \"Virmantas\",\nfamily = \"Kvedaras\",\nemail = \"virmantas.kvedaras@ec.europa.eu\",\nrole = \"aut\"),\nperson(given = \"Vaidotas\",\nfamily= \"Zemlys-Balevičius\",\nemail = \"zemlys@gmail.com\",\nrole = \"aut\"))",
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  "Author": "Vaidotas Zemlys-Balevičius [cre],\nVirmantas Kvedaras [aut],\nVaidotas Zemlys-Balevičius [aut]",
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    "extra/contents.json",
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  "_homeurl": "https://github.com/mpiktas/midasr",
  "_realowner": "mpiktas",
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  "_releases": [
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      "date": "2013-10-10"
    },
    {
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      "date": "2014-01-07"
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    {
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  "_exports": [
    "agk.test",
    "almonp",
    "almonp_gradient",
    "amidas_table",
    "amweights",
    "average_forecast",
    "check_mixfreq",
    "deriv_tests",
    "dmls",
    "expand_amidas",
    "expand_weights_lags",
    "extract.midas_r",
    "fmls",
    "forecast",
    "genexp",
    "genexp_gradient",
    "get_estimation_sample",
    "gompertzp",
    "gompertzp_gradient",
    "hAh_test",
    "hAhr_test",
    "harstep",
    "harstep_gradient",
    "hf_lags_table",
    "imidas_r",
    "lcauchyp",
    "lcauchyp_gradient",
    "lf_lags_table",
    "lstr",
    "midas_auto_sim",
    "midas_lstr_plain",
    "midas_lstr_sim",
    "midas_mmm_plain",
    "midas_mmm_sim",
    "midas_nlpr",
    "midas_pl_plain",
    "midas_pl_sim",
    "midas_qr",
    "midas_r",
    "midas_r_ic_table",
    "midas_r_np",
    "midas_r_plain",
    "midas_si_plain",
    "midas_si_sim",
    "midas_sim",
    "midas_sp",
    "midas_u",
    "mls",
    "mlsd",
    "mmm",
    "modsel",
    "nakagamip",
    "nakagamip_gradient",
    "nbeta",
    "nbeta_gradient",
    "nbetaMT",
    "nbetaMT_gradient",
    "nealmon",
    "nealmon_gradient",
    "plot_lstr",
    "plot_midas_coef",
    "plot_sp",
    "polystep",
    "polystep_gradient",
    "select_and_forecast",
    "simulate",
    "split_data",
    "update_weights",
    "weights_table"
  ],
  "_datasets": [
    {
      "name": "oos_prec",
      "title": "Out-of-sample prediction precision data on simulation example",
      "object": "oos_prec",
      "file": "oos_prec.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "n",
        "Constraint",
        "value",
        "Type"
      ],
      "rows": 42,
      "table": true,
      "tojson": true
    },
    {
      "name": "rvsp500",
      "title": "Realized volatility of S&P500 index",
      "object": "rvsp500",
      "file": "rvsp500.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "DateID",
        "SPX2.rv"
      ],
      "rows": 3459,
      "table": true,
      "tojson": true
    },
    {
      "name": "USpayems",
      "title": "United States total employment non-farms payroll, monthly, seasonally adjusted.",
      "object": "USpayems",
      "file": "USpayems.RData",
      "class": [
        "ts"
      ],
      "fields": [
        "PAYEMS"
      ],
      "rows": 903,
      "table": true,
      "tojson": true
    },
    {
      "name": "USqgdp",
      "title": "United States gross domestic product, quarterly, seasonaly adjusted annual rate.",
      "object": "USqgdp",
      "file": "USqgdp.RData",
      "class": [
        "ts"
      ],
      "fields": [
        "GDP"
      ],
      "rows": 268,
      "table": true,
      "tojson": true
    },
    {
      "name": "USrealgdp",
      "title": "US annual gross domestic product in billions of chained 2005 dollars",
      "object": "USrealgdp",
      "file": "USrealgdp.RData",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "USunempr",
      "title": "US monthly unemployment rate",
      "object": "USunempr",
      "file": "USunempr.RData",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "lws_table-add",
      "title": "Combine 'lws_table' objects",
      "topics": [
        "+.lws_table"
      ]
    },
    {
      "page": "agk.test",
      "title": "Andreou, Ghysels, Kourtellos LM test",
      "topics": [
        "agk.test"
      ]
    },
    {
      "page": "almonp",
      "title": "Almon polynomial MIDAS weights specification",
      "topics": [
        "almonp"
      ]
    },
    {
      "page": "almonp_gradient",
      "title": "Gradient function for Almon polynomial MIDAS weights",
      "topics": [
        "almonp_gradient"
      ]
    },
    {
      "page": "amidas_table",
      "title": "Weight and lag selection table for aggregates based MIDAS regression model",
      "topics": [
        "amidas_table"
      ]
    },
    {
      "page": "amweights",
      "title": "Weights for aggregates based MIDAS regressions",
      "topics": [
        "amweights"
      ]
    },
    {
      "page": "average_forecast",
      "title": "Average forecasts of MIDAS models",
      "topics": [
        "average_forecast"
      ]
    },
    {
      "page": "check_mixfreq",
      "title": "Check data for MIDAS regression",
      "topics": [
        "check_mixfreq"
      ]
    },
    {
      "page": "coef.midas_nlpr",
      "title": "Extract coefficients of MIDAS regression",
      "topics": [
        "coef.midas_nlpr"
      ]
    },
    {
      "page": "coef.midas_r",
      "title": "Extract coefficients of MIDAS regression",
      "topics": [
        "coef.midas_r"
      ]
    },
    {
      "page": "coef.midas_sp",
      "title": "Extract coefficients of MIDAS regression",
      "topics": [
        "coef.midas_sp"
      ]
    },
    {
      "page": "deriv_tests",
      "title": "Check whether non-linear least squares restricted MIDAS regression problem has converged",
      "topics": [
        "deriv_tests",
        "deriv_tests.midas_r"
      ]
    },
    {
      "page": "deviance.midas_nlpr",
      "title": "Non-linear parametric MIDAS regression model deviance",
      "topics": [
        "deviance.midas_nlpr"
      ]
    },
    {
      "page": "deviance.midas_r",
      "title": "MIDAS regression model deviance",
      "topics": [
        "deviance.midas_r"
      ]
    },
    {
      "page": "deviance.midas_sp",
      "title": "Semi-parametric MIDAS regression model deviance",
      "topics": [
        "deviance.midas_sp"
      ]
    },
    {
      "page": "dmls",
      "title": "MIDAS lag structure for unit root processes",
      "topics": [
        "dmls"
      ]
    },
    {
      "page": "expand_amidas",
      "title": "Create table of weights, lags and starting values for Ghysels weight schema",
      "topics": [
        "expand_amidas"
      ]
    },
    {
      "page": "expand_weights_lags",
      "title": "Create table of weights, lags and starting values",
      "topics": [
        "expand_weights_lags"
      ]
    },
    {
      "page": "extract.midas_r",
      "title": "Extract coefficients and GOF measures from MIDAS regression object",
      "topics": [
        "extract.midas_r"
      ]
    },
    {
      "page": "fitted.midas_nlpr",
      "title": "Fitted values for non-linear parametric MIDAS regression model",
      "topics": [
        "fitted.midas_nlpr"
      ]
    },
    {
      "page": "fitted.midas_sp",
      "title": "Fitted values for semi-parametric MIDAS regression model",
      "topics": [
        "fitted.midas_sp"
      ]
    },
    {
      "page": "fmls",
      "title": "Full MIDAS lag structure",
      "topics": [
        "fmls"
      ]
    },
    {
      "page": "forecast.midas_r",
      "title": "Forecast MIDAS regression",
      "topics": [
        "forecast",
        "forecast.midas_r"
      ]
    },
    {
      "page": "genexp",
      "title": "Generalized exponential MIDAS coefficients",
      "topics": [
        "genexp"
      ]
    },
    {
      "page": "genexp_gradient",
      "title": "Gradient of generalized exponential MIDAS coefficient generating function",
      "topics": [
        "genexp_gradient"
      ]
    },
    {
      "page": "get_estimation_sample",
      "title": "Get the data which was used to etimate MIDAS regression",
      "topics": [
        "get_estimation_sample"
      ]
    },
    {
      "page": "gompertzp",
      "title": "Normalized Gompertz probability density function MIDAS weights specification",
      "topics": [
        "gompertzp"
      ]
    },
    {
      "page": "gompertzp_gradient",
      "title": "Gradient function for normalized Gompertz probability density function MIDAS weights specification",
      "topics": [
        "gompertzp_gradient"
      ]
    },
    {
      "page": "hAh_test",
      "title": "Test restrictions on coefficients of MIDAS regression",
      "topics": [
        "hAh_test"
      ]
    },
    {
      "page": "hAhr_test",
      "title": "Test restrictions on coefficients of MIDAS regression using robust version of the test",
      "topics": [
        "hAhr_test"
      ]
    },
    {
      "page": "harstep",
      "title": "HAR(3)-RV model MIDAS weights specification",
      "topics": [
        "harstep"
      ]
    },
    {
      "page": "harstep_gradient",
      "title": "Gradient function for HAR(3)-RV model MIDAS weights specification",
      "topics": [
        "harstep_gradient"
      ]
    },
    {
      "page": "hf_lags_table",
      "title": "Create a high frequency lag selection table for MIDAS regression model",
      "topics": [
        "hf_lags_table"
      ]
    },
    {
      "page": "imidas_r",
      "title": "Restricted MIDAS regression with I(1) regressors",
      "topics": [
        "imidas_r"
      ]
    },
    {
      "page": "lcauchyp",
      "title": "Normalized log-Cauchy probability density function MIDAS weights specification",
      "topics": [
        "lcauchyp"
      ]
    },
    {
      "page": "lcauchyp_gradient",
      "title": "Gradient function for normalized log-Cauchy probability density function MIDAS weights specification",
      "topics": [
        "lcauchyp_gradient"
      ]
    },
    {
      "page": "lf_lags_table",
      "title": "Create a low frequency lag selection table for MIDAS regression model",
      "topics": [
        "lf_lags_table"
      ]
    },
    {
      "page": "lstr",
      "title": "Compute LSTR term for high frequency variable",
      "topics": [
        "lstr"
      ]
    },
    {
      "page": "midas_auto_sim",
      "title": "Simulate simple autoregressive MIDAS model",
      "topics": [
        "midas_auto_sim"
      ]
    },
    {
      "page": "midas_lstr_plain",
      "title": "LSTR (Logistic Smooth TRansition) MIDAS regression",
      "topics": [
        "midas_lstr_plain"
      ]
    },
    {
      "page": "midas_lstr_sim",
      "title": "Simulate LSTR MIDAS regression model",
      "topics": [
        "midas_lstr_sim"
      ]
    },
    {
      "page": "midas_mmm_plain",
      "title": "MMM (Mean-Min-Max) MIDAS regression",
      "topics": [
        "midas_mmm_plain"
      ]
    },
    {
      "page": "midas_mmm_sim",
      "title": "Simulate MMM MIDAS regression model",
      "topics": [
        "midas_mmm_sim"
      ]
    },
    {
      "page": "midas_nlpr",
      "title": "Non-linear parametric MIDAS regression",
      "topics": [
        "midas_nlpr"
      ]
    },
    {
      "page": "midas_nlpr.fit",
      "title": "Fit restricted MIDAS regression",
      "topics": [
        "midas_nlpr.fit"
      ]
    },
    {
      "page": "midas_pl_plain",
      "title": "MIDAS Partialy linear non-parametric regression",
      "topics": [
        "midas_pl_plain"
      ]
    },
    {
      "page": "midas_pl_sim",
      "title": "Simulate PL MIDAS regression model",
      "topics": [
        "midas_pl_sim"
      ]
    },
    {
      "page": "midas_qr",
      "title": "Restricted MIDAS quantile regression",
      "topics": [
        "midas_qr"
      ]
    },
    {
      "page": "midas_r",
      "title": "Restricted MIDAS regression",
      "topics": [
        "midas_r"
      ]
    },
    {
      "page": "midas_r_ic_table",
      "title": "Create a weight and lag selection table for MIDAS regression model",
      "topics": [
        "midas_r_ic_table"
      ]
    },
    {
      "page": "midas_r_np",
      "title": "Estimate non-parametric MIDAS regression",
      "topics": [
        "midas_r_np"
      ]
    },
    {
      "page": "midas_r_plain",
      "title": "Restricted MIDAS regression",
      "topics": [
        "midas_r_plain"
      ]
    },
    {
      "page": "midas_r.fit",
      "title": "Fit restricted MIDAS regression",
      "topics": [
        "midas_r.fit"
      ]
    },
    {
      "page": "midas_si_plain",
      "title": "MIDAS Single index regression",
      "topics": [
        "midas_si_plain"
      ]
    },
    {
      "page": "midas_si_sim",
      "title": "Simulate SI MIDAS regression model",
      "topics": [
        "midas_si_sim"
      ]
    },
    {
      "page": "midas_sim",
      "title": "Simulate simple MIDAS regression response variable",
      "topics": [
        "midas_sim"
      ]
    },
    {
      "page": "midas_sp",
      "title": "Semi-parametric MIDAS regression",
      "topics": [
        "midas_sp"
      ]
    },
    {
      "page": "midas_u",
      "title": "Estimate unrestricted MIDAS regression",
      "topics": [
        "midas_u"
      ]
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      "title": "MIDAS lag structure",
      "topics": [
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    },
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      "page": "mlsd",
      "title": "MIDAS lag structure with dates",
      "topics": [
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    },
    {
      "page": "mmm",
      "title": "Compute MMM term for high frequency variable",
      "topics": [
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    },
    {
      "page": "modsel",
      "title": "Select the model based on given information criteria",
      "topics": [
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    {
      "page": "nakagamip",
      "title": "Normalized Nakagami probability density function MIDAS weights specification",
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      "page": "plot_lstr",
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