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  "Package": "tseffects",
  "Title": "Dynamic Inferences from Time Series (with Interactions)",
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  "Authors@R": "c(person(\"Soren\", \"Jordan\", comment = c(ORCID = \"0000-0003-4201-1085\"), email = \"sorenjordanpols@gmail.com\", role = c(\"aut\", \"cre\", \"cph\")), person(\"Garrett N. Vande Kamp\", email = \"garrettvandekamp@uga.edu\", role = c(\"aut\")), person(\"Reshi Rajan\", email = \"rrajan@tamu.edu\", role = c(\"aut\")))",
  "Maintainer": "Soren Jordan <sorenjordanpols@gmail.com>",
  "Description": "Autoregressive distributed lag (A[R]DL) models (and their\nreparameterized equivalent, the Generalized Error-Correction\nModel [GECM]) are the workhorse models in uncovering dynamic\ninferences. ADL models are simple to estimate; this is what\nmakes them attractive. Once these models are estimated, what is\nless clear is how to uncover a rich set of dynamic inferences\nfrom these models. We provide tools for recovering those\ninferences. These tools apply to traditional time-series\nquantities of interest and are built from the Impulse Response\nFunction and Step Response Function (sometimes described as a\npulse effect or a cumulative effect). They also allow for a\nvariety of shock histories to be applied to the independent\nvariable (beyond just a one-time, one-unit increase) as well as\nthe recovery of inferences in levels for shocks applied to\n(in)dependent variables in differences (what we call the\nGeneralized Dynamic Response Function). These effects are also\navailable for the general conditional dynamic model advocated\nby Warner, Vande Kamp, and Jordan (2026\n<doi:10.1017/psrm.2026.10087>). We also provide the formulae\nfor these effects.",
  "URL": "https://sorenjordan.github.io/tseffects/,\nhttps://github.com/sorenjordan/tseffects",
  "BugReports": "https://github.com/sorenjordan/tseffects/issues",
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  "Repository": "https://sorenjordan.r-universe.dev",
  "Date/Publication": "2026-07-15 21:25:14 UTC",
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    "User": "root"
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  "Author": "Soren Jordan [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0003-4201-1085>),\nGarrett N. Vande Kamp [aut],\nReshi Rajan [aut]",
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    "gecm.plot",
    "gecm.to.adl",
    "general.calculator",
    "interact.adl.plot",
    "pulse.calculator",
    "yhat.calculator"
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      "object": "approval",
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        "APPROVE_ECONOMY",
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        "PARTY_OUT",
        "PRESIDENT",
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        "HONEYMOON",
        "UMCSENT",
        "UNRATE",
        "APPROVE_L1"
      ],
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      "title": "Simulated interactive time series data",
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      ],
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        "x",
        "l_1_x",
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        "y",
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        "l_3_y",
        "l_4_y",
        "l_5_y",
        "x_z",
        "x_l_1_z",
        "z_l_1_x",
        "l_1_x_l_1_z",
        "d_x",
        "l_1_d_x",
        "d_y",
        "l_1_d_y",
        "l_2_d_x",
        "l_2_d_y",
        "d_2_y",
        "l_1_d_2_y",
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      ],
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      "table": true,
      "tojson": true
    }
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      "page": "adl.plot",
      "title": "Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for an autoregressive distributed lag (ADL) model, assuming the underlying model is in levels ('d.x' = 'd.y' = 0) and the user wants a marginal effect (the untransformed GDRF). (This is just a wrapper for 'GDRF.adl.plot' with simplifying assumptions)",
      "topics": [
        "adl.plot"
      ]
    },
    {
      "page": "approval",
      "title": "Data on US Presidential Approval",
      "topics": [
        "approval"
      ]
    },
    {
      "page": "GDRF.adl.plot",
      "title": "Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for an autoregressive distributed lag (ADL) model",
      "topics": [
        "GDRF.adl.plot"
      ]
    },
    {
      "page": "GDRF.gecm.plot",
      "title": "Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for a Generalized Error Correction Model (GECM)",
      "topics": [
        "GDRF.gecm.plot"
      ]
    },
    {
      "page": "gecm.plot",
      "title": "Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for a GECM(1,1) model, assuming the underlying model is in first differences ('x.vrbl.d.x' = 'y.vrbl.d.y' = 0 and 'x.d.vrbl.d.x' = 'y.d.vrbl.d.y' = 1) and the user wants a marginal effect (the untransformed GDRF) and inferences about y in levels to a treatment applied to x in levels. (This is just a wrapper for 'GDRF.gecm.plot' with simplifying assumptions)",
      "topics": [
        "gecm.plot"
      ]
    },
    {
      "page": "gecm.to.adl",
      "title": "Translate the coefficients from the General Error Correction Model (GECM) to the autoregressive distributed lag (ADL) model",
      "topics": [
        "gecm.to.adl"
      ]
    },
    {
      "page": "general.calculator",
      "title": "Generate the generalized effect formulae for an autoregressive distributed lag (ADL) model, given pulse effects and shock history",
      "topics": [
        "general.calculator"
      ]
    },
    {
      "page": "interact.adl.plot",
      "title": "Plot the interaction in a single-equation time series model estimated via 'lm'.",
      "topics": [
        "interact.adl.plot"
      ]
    },
    {
      "page": "pulse.calculator",
      "title": "Generate pulse effect formulae for a given autoregressive distributed lag (ADL) model",
      "topics": [
        "pulse.calculator"
      ]
    },
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      "page": "toy.ts.interaction.data",
      "title": "Simulated interactive time series data",
      "topics": [
        "toy.ts.interaction.data"
      ]
    },
    {
      "page": "yhat.calculator",
      "title": "Transform the GDRF formulae to fitted value formulae",
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        "Dynamic conditional effects: interact.adl.plot",
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