Data analysis, normalisation and differential expression for Translatome studies by microarrays (T Sbarrato et al. RNA. 2017 Aug 25; <DOI:10.1261/rna.060525.116>).

The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match  similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>. For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) <doi:10.18637/jss.v099.i09>.

An efficient and incremental approach for calculating 
    the differences in orbit counts when performing single edge modifications 
    in a network. Calculating the differences in orbit counts is much more efficient than
    recalculating all orbit counts from scratch for each time point.

Provides functions and classes to compute, handle and visualise
  incidence from dated events for a defined time interval. Dates can be provided
  in various standard formats. The class 'incidence' is used to store computed
  incidence and can be easily manipulated, subsetted, and plotted. In addition,
  log-linear models can be fitted to 'incidence' objects using 'fit'. This
  package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit
  for outbreak analysis.