gaussianrollingaverageiterative: Rolling Average Iterative Gaussian Bayesian Model
In balexanderstats/bayesurvey: Bayesian Conjugate Prior Analysis of Survey Data

Rolling Average Iterative Gaussian Bayesian Model

gaussianrollingaverageiterative(
  poll_data,
  stateloc,
  proploc,
  candidateloc,
  npolls = 10,
  varloc = NULL,
  nloc,
  invgamma = F,
  v0 = NULL,
  a0 = NULL,
  b0 = NULL,
  election_data,
  cutoffs = c(-0.2, -0.1, -0.025, 0.025, 0.1, 0.2),
  groupnames = c("Strong Red", "Red", "Lean Red", "Competitive", "Lean Blue", "Blue",
    "Strong Blue")
)

`poll_data`
`stateloc`
`proploc`
`candidateloc`
`npolls`
`varloc`
`nloc`
`invgamma`
`v0`
`a0`
`b0`
`election_data`
`cutoffs`
`groupnames`

require(politicaldata)
elect2008  = subset(pres_results , year == 2008)
elect2008$margin = elect2008$dem - elect2008$rep
elect2012 = subset(pres_results , year == 2012)
elect2012$margin = elect2012$dem - elect2012$rep
electdata = data.frame("state" = elect2008$state, "2008" =  elect2008$margin, "2012" = elect2012$margin)
data(polls2016)
polls = polls2016[ complete.cases(polls2016[, c(2, 3, 12)]), ]
head(polls)
nloc = which(colnames(polls2016) == "observations")
stateloc  = which(colnames(polls) == "State")
gaussianrollingaverage(polls, stateloc = stateloc, proploc = c(2,3), 3, nloc = nloc, election_data = electdata)