R (>= 2.15.0), rJava, coda, ggplot2, InterVA5

Zehang Richard Li, Tyler McCormick, Sam Clark

R (>= 3.5.0), rJava, coda, ggplot2, InterVA5

Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.

Incremental Calculation of Dynamic Time Warping
Implements incremental calculation of the DTW ( Dynamic Time Warping) distance of two vectors, which is specifically useful for life data streams. Further the calculation of the global cost matrix is implemented in C++ to be faster. The Sakoe Chiba band is also implemented. The calculation of DTW is less functional then the one of dtw(), however much faster. 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>.

Implements incremental calculation of the DTW (Dynamic Time Warping) distance of two vectors, which is specifically useful for life data streams. Further the calculation of the global cost matrix is implemented in C++ to be faster. The Sakoe Chiba band is also implemented. The calculation of DTW is less functional then the one of dtw(), however much faster. 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>. Any feedback is highly appreciated!
knitr, dtw, microbenchmark, rmarkdown, gridExtra

knitr, dtw, rbenchmark, rmarkdown, gridExtra

The Dynamic Time Warping (DTW) distance for time series allows non-linear alignments of time series to match  similar patterns in time series of different lengths and or different speeds. Beside the traditional implementation of the DTW algorithm, the specialities of this package are, (1) the incremental calculation, which is specifically useful for life data streams due to computationally efficiency, (2) the vector based implementation of the traditional DTW algorithm which is faster because no matrices are allocated and is especially useful for computing distance matrices of pairwise DTW distances for many time series  and (3) the combination of incremental and vector-based calculation. 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>.
Rcpp (>= 0.12.8), RcppParallel, ggplot2, scales, parallel,
stats, data.table
knitr, dtw, rmarkdown, gridExtra, testthat, dtwclust,
parallelDist, microbenchmark, rucrdtw, proxy