Implements TRACDS (Temporal Relationships 
    between Clusters for Data Streams), a generalization of 
    Extensible Markov Model (EMM). TRACDS adds a temporal or order model
    to data stream clustering by superimposing a dynamically adapting
    Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN 
    data stream clustering). Development of this 
    package was supported in part by NSF IIS-0948893 and R21HG005912 from 
    the National Human Genome Research Institute. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.

An HTTP API client for 'Lemmy'
    (<https://github.com/LemmyNet/lemmy>) in R. Code and documentation are
    generated from the official 'JavaScript' client source
    (<https://github.com/LemmyNet/lemmy-js-client>).

Empirical orthogonal teleconnections in R.
    'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'.
    It implements a collection of functions to facilitate empirical
    orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections
    (EOTs) denote a regression based approach to decompose spatio-temporal
    fields into a set of independent orthogonal patterns. They are quite
    similar to Empirical Orthogonal Functions (EOFs) with EOTs producing
    less abstract results. In contrast to EOFs, which are orthogonal in both
    space and time, EOT analysis produces patterns that are orthogonal in
    either space or time.

These tools were created to test map-scale hypotheses about trends in large
  remotely sensed data sets but any data with spatial and temporal variation
  can be analyzed. Tests are conducted using the PARTS method for analyzing spatially
  autocorrelated time series
  (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>).
  The method's unique approach can handle extremely large data sets that other
  spatiotemporal models cannot, while still appropriately accounting for
  spatial and temporal autocorrelation. This is done by partitioning the data
  into smaller chunks, analyzing chunks separately and then combining the
  separate analyses into a single, correlated test of the map-scale hypotheses.