Data that are collected through online sources such as
    Mechanical Turk may require excluding rows because of IP address
    duplication, geolocation, or completion duration. This package
    facilitates exclusion of these data for Qualtrics datasets.

Instead of counting observations before and after a subset() 
    call, the ExclusionTable() function reports the number before and after 
    each subset() call together with the number of observations that have been 
    excluded. This is especially useful in observational studies for keeping 
    track how many observations have been excluded for each in-/ or 
    exclusion criteria. You just need to provide ExclusionTable() with a 
    dataset and a list of logical filter statements.

Interactive tools to explore topographic-like data
    sets.  Such data sets take the form of a matrix in which the rows and
    columns provide location/frequency information, and the matrix elements
    contain altitude/response information.  Such data is found in cartography,
    2D spectroscopy and chemometrics.  The functions in this package create
    interactive web pages showing the contoured data, possibly with
    slices from the original matrix parallel to each dimension. The interactive
    behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.

Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.

Performs frequentist inference for the extremal index of a 
    stationary time series.  Two types of methodology are used.  One type is
    based on a model that relates the distribution of block maxima to the 
    marginal distribution of series and leads to the semiparametric maxima 
    estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and 
    Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>.  Sliding block maxima
    are used to increase precision of estimation. A graphical block size 
    diagnostic is provided.  The other type of methodology uses a model for the 
    distribution of threshold inter-exceedance times (Ferro and Segers (2003) 
    <doi:10.1111/1467-9868.00401>). Three versions of this type of approach are 
    provided: the iterated weight least squares approach of Suveges (2007) 
    <doi:10.1007/s10687-007-0034-2>, the K-gaps model of 
    Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and a similar approach
    of Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3> 
    that we refer to as D-gaps. For the K-gaps and D-gaps models this package 
    allows missing values in the data, can accommodate independent subsets of 
    data, such as monthly or seasonal time series from different years, and can 
    incorporate information from right-censored inter-exceedance times.  
    Graphical diagnostics for the threshold level and the respective tuning
    parameters K and D are provided.