Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. These functions are useful for census-weights adjustments, survey calibration, or for performing linear regression with integer parameters <https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/New_Integer_Calibration_%20Procedure_2016.pdf>. This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy.

Offers a pipe-friendly alternative to the 'dplyr' functions
    case_when() and if_else(), as well as a number of user-friendly
    simplifications for common use cases.  These functions accept a vector
    as an optional first argument, allowing conditional statements to be
    built using the 'magrittr' dot operator.  The functions also coerce
    all outputs to the same type, meaning you no longer have to worry
    about using specific typed variants of NA or explicitly declaring
    integer outputs, and evaluate outputs somewhat lazily, so you don't
    waste time on long operations that won't be used.

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>.