'Condition.R' 'Design.R' 'NoDefault.R' 'Param.R' 'ParamDbl.R'
'ParamFct.R' 'ParamInt.R' 'ParamLgl.R' 'ParamSet.R'
'ParamSetCollection.R' 'ParamUty.R' 'Sampler.R' 'Sampler1D.R'
'SamplerHierarchical.R' 'SamplerJointIndep.R' 'SamplerUnif.R'
'asserts.R' 'default_values.R' 'helper.R' 'domain.R'
'generate_design_grid.R' 'generate_design_lhs.R'
'generate_design_random.R' 'generate_design_sobol.R' 'ps.R'
'reexports.R' 'to_tune.R' 'zzz.R'

'Condition.R' 'Design.R' 'NoDefault.R' 'Param.R' 'ParamDbl.R'
'ParamFct.R' 'ParamInt.R' 'ParamLgl.R' 'ParamSet.R'
'ParamSetCollection.R' 'ParamUty.R' 'Sampler.R' 'Sampler1D.R'
'SamplerHierarchical.R' 'SamplerJointIndep.R' 'SamplerUnif.R'
'asserts.R' 'generate_design_grid.R' 'generate_design_lhs.R'
'generate_design_random.R' 'reexports.R' 'zzz.R'

'Condition.R' 'Design.R' 'NoDefault.R' 'Param.R' 'ParamDbl.R'
'ParamFct.R' 'ParamInt.R' 'ParamLgl.R' 'ParamSet.R'
'ParamSetCollection.R' 'ParamUty.R' 'Sampler.R' 'Sampler1D.R'
'SamplerHierarchical.R' 'SamplerJointIndep.R' 'SamplerUnif.R'
'asserts.R' 'generate_design_grid.R' 'generate_design_lhs.R'
'generate_design_random.R' 'helper.R' 'reexports.R' 'zzz.R'

backports, checkmate, data.table, mlr3misc (>= 0.4.0), R6

backports, checkmate, data.table, mlr3misc (>= 0.5.0), R6

backports, checkmate, data.table, methods, mlr3misc (>=
0.5.0), R6
'Condition.R' 'Design.R' 'NoDefault.R' 'Param.R' 'ParamDbl.R'
'ParamFct.R' 'ParamInt.R' 'ParamLgl.R' 'ParamSet.R'
'ParamSetCollection.R' 'ParamUty.R' 'Sampler.R' 'Sampler1D.R'
'SamplerHierarchical.R' 'SamplerJointIndep.R' 'SamplerUnif.R'
'asserts.R' 'helper.R' 'domain.R' 'generate_design_grid.R'
'generate_design_lhs.R' 'generate_design_random.R' 'ps.R'
'reexports.R' 'to_tune.R' 'zzz.R'

backports, checkmate, data.table, methods, mlr3misc (>=
0.7.0), R6

Parallel Distance Matrix Computation using Multiple Threads
c(
person("Alexander", "Eckert", role = c("aut", "cre"), email = "info@alexandereckert.com")
)
A fast parallelized alternative to R's native 'dist' function to
calculate distance matrices for continuous, binary, and multi-dimensional
input matrices with support for a broad variety of distance functions from
the 'stats', 'proxy' and 'dtw' R packages. For ease of use, the 'parDist'
function extends the signature of the 'dist' function and uses the same
parameter naming conventions as distance methods of existing R packages.
The package is mainly implemented in C++ and leverages the 'RcppParallel'
package to parallelize the distance computations with the help of the
'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library
is used for optimized matrix operations during distance calculations. The
curiously recurring template pattern (CRTP) technique is applied to avoid
virtual functions, which improves the Dynamic Time Warping calculations
while keeping the implementation flexible enough to support different step
patterns and normalization methods.
dtw, ggplot2, proxy, highlight, testthat