{
"_id": "6a155412acfb0bcc41d5c8c4",
"Package": "MARSS",
"Type": "Package",
"Title": "Multivariate Autoregressive State-Space Modeling",
"Version": "3.11.9",
"Date": "2024-02-19",
"Authors@R": "c(person(\"Elizabeth Eli\", \"Holmes\", , \"eli.holmes@noaa.gov\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9128-8393\")),\nperson(\"Eric J.\", \"Ward\", , \"eric.ward@noaa.gov\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-4359-0296\")),\nperson(\"Mark D.\", \"Scheuerell\", , \"scheuerl@uw.edu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-8284-1254\")),\nperson(\"Kellie\", \"Wills\", , \"wills.kellie@gmail.com\", role = c(\"aut\"))\n)",
"Description": "The MARSS package provides maximum-likelihood parameter\nestimation for constrained and unconstrained linear\nmultivariate autoregressive state-space (MARSS) models,\nincluding partially deterministic models. MARSS models are a\nclass of dynamic linear model (DLM) and vector autoregressive\nmodel (VAR) model. Fitting available via\nExpectation-Maximization (EM), BFGS (using optim), and 'TMB'\n(using the 'marssTMB' companion package). Functions are\nprovided for parametric and innovations bootstrapping, Kalman\nfiltering and smoothing, model selection criteria including\nbootstrap AICb, confidences intervals via the Hessian\napproximation or bootstrapping, and all conditional residual\ntypes. See the user guide for examples of dynamic factor\nanalysis, dynamic linear models, outlier and shock detection,\nand multivariate AR-p models. Online workshops (lectures,\neBook, and computer labs) at .",
"License": "GPL-2",
"LazyData": "yes",
"BuildVignettes": "yes",
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"URL": "https://atsa-es.github.io/MARSS/",
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"Repository": "https://atsa-es.r-universe.dev",
"Date/Publication": "2025-07-30 22:56:42 UTC",
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"User": "root"
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"Author": "Elizabeth Eli Holmes [aut, cre] (ORCID:\n),\nEric J. Ward [aut] (ORCID: ),\nMark D. Scheuerell [aut] (ORCID:\n),\nKellie Wills [aut]",
"Maintainer": "Elizabeth Eli Holmes ",
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"MARSSoptim",
"MARSSparamCIs",
"MARSSresiduals",
"MARSSsimulate",
"MARSSvectorizeparam",
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"_help": [
{
"page": "MARSS-package",
"title": "Multivariate Autoregressive State-Space Model Estimation",
"topics": [
"MARSS-package"
]
},
{
"page": "accuracy_marssMLE",
"title": "Return accuracy metrics",
"topics": [
"accuracy",
"accuracy.marssMLE",
"accuracy.marssPredict"
]
},
{
"page": "coef_marssMLE",
"title": "Coefficient function for MARSS MLE objects",
"topics": [
"coef.marssMLE"
]
},
{
"page": "CSEGriskfigure",
"title": "Plot Extinction Risk Metrics",
"topics": [
"CSEGriskfigure"
]
},
{
"page": "CSEGtmufigure",
"title": "Plot Forecast Uncertainty",
"topics": [
"CSEGtmufigure"
]
},
{
"page": "datasets",
"title": "Example Data Sets",
"topics": [
"datasets"
]
},
{
"page": "fitted_marssMLE",
"title": "Return fitted values for X(t) and Y(t) in a MARSS model",
"topics": [
"fitted.marssMLE"
]
},
{
"page": "forecast_marssMLE",
"title": "forecast function for marssMLE objects",
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]
},
{
"page": "glance_marssMLE",
"title": "Return brief summary information on a MARSS fit",
"topics": [
"glance",
"glance.marssMLE"
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},
{
"page": "harborSeal",
"title": "Harbor Seal Population Count Data (Log counts)",
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{
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"title": "Isle Royale Wolf and Moose Data",
"topics": [
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},
{
"page": "ldiag",
"title": "Return a diagonal list matrix",
"topics": [
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]
},
{
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"title": "Loggerhead Turtle Tracking Data",
"topics": [
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"loggerheadNoisy"
]
},
{
"page": "logLik_marssMLE",
"title": "logLik method for MARSS MLE objects",
"topics": [
"logLik",
"logLik.marssMLE"
]
},
{
"page": "MARSS",
"title": "Fit a MARSS Model via Maximum-Likelihood Estimation",
"topics": [
"MARSS"
]
},
{
"page": "MARSS_dfa",
"title": "Multivariate Dynamic Factor Analysis",
"topics": [
"MARSS.dfa"
]
},
{
"page": "MARSS_marss",
"title": "Multivariate AR-1 State-space Model",
"topics": [
"MARSS.marss"
]
},
{
"page": "MARSS_marxss",
"title": "Multivariate AR-1 State-space Model with Inputs",
"topics": [
"MARSS.marxss"
]
},
{
"page": "MARSS_vectorized",
"title": "Vectorized Multivariate AR-1 State-space Model",
"topics": [
"MARSS.vectorized"
]
},
{
"page": "MARSSaic",
"title": "AIC for MARSS Models",
"topics": [
"MARSSaic"
]
},
{
"page": "MARSSboot",
"title": "Bootstrap MARSS Parameter Estimates",
"topics": [
"MARSSboot"
]
},
{
"page": "MARSScv",
"title": "MARSScv is a wrapper for MARSS that re-fits the model with cross validated data.",
"topics": [
"MARSScv"
]
},
{
"page": "MARSSFisherI",
"title": "Observed Fisher Information Matrix at the MLE",
"topics": [
"MARSSFisherI"
]
},
{
"page": "MARSSfit",
"title": "Generic for fitting MARSS models",
"topics": [
"MARSSfit"
]
},
{
"page": "MARSSharveyobsFI",
"title": "Hessian Matrix via the Harvey (1989) Recursion",
"topics": [
"MARSSharveyobsFI"
]
},
{
"page": "MARSShatyt",
"title": "Compute Expected Value of Y, YY, and YX",
"topics": [
"MARSShatyt"
]
},
{
"page": "MARSShessian",
"title": "Parameter Variance-Covariance Matrix from the Hessian Matrix",
"topics": [
"MARSShessian"
]
},
{
"page": "MARSShessian_numerical",
"title": "Hessian Matrix via Numerical Approximation",
"topics": [
"MARSShessian.numerical"
]
},
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