This package provides convenient functions for fitting Poisson distributions, regressions, and processes to count data of some phenomena or events. It also contains data-sets for examples of approximately Poisson distributed phenomena. These include data-sets named in R as "stormReport", "ENSO", and "atlanticStormsENSO" and "shootings". See the internal R documentation for more info and sources.

You can install the latest version from GitHub with:

devtools::install_github("shill1729/poissonFits")

We can load hurricane data from NHC NOAA, since they conveniently provide an XML file of all storm reports since 1958. See documentation on "stormReport" for more details. The data-set is provided with the package, however, a scraper is also available to get the latest file of storm-reports (which I imagine does not change frequently until after new storm seasons are over). NOTE: Early data contains named storms without designating them either a hurricane, a tropical storm, or a subtropical storm. The parameter for the Poisson distribution is chosen via MLE. A Chi-square test is then performed on the goodness of fit of the distribution to the empirical data.

library(poissonFits)
# Significance level for Chi-square test
alpha <- 0.05

# Default loads no tropical storms;
countData <- loadStormData()

# Pick either the atlantic, pacific, or world
countsYear <- countData$atlantic
# Get just the counts
countsData <- countsYear$freq

# Pass just the count data to poissonFit
poissonFit(countsData)