**Statistical Tools for Topological Data Analysis** (**TDA**)

Tools for the statistical analysis of persistent homology and for density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries ‘GUDHI’ <http://…/>, ‘Dionysus’ <http://…/>, and ‘PHAT’ <https://…/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128> for analyzing the statistical significance of persistent homology features.

**Local Gaussian Process Model for Large-Scale Dynamic Computer Experiments** (**DynamicGP**)

Fits localized GP model for dynamic computer experiments via singular value decomposition of the response matrix Y for large N (the number of observations) using the algorithm proposed by Zhang et al. (2018) <arXiv:1611.09488>. The current version only supports 64-bit architecture.

**Multiple Imputation with Sequential Penalized Regression** (**mispr**)

Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

**Weighted Mixed-Effects Models, using Multilevel Pseudo Maximum Likelihood Estimation** (**WeMix**)

Run mixed-effects models that include weights at every level. The ‘WeMix’ package fits a Weighted Mixed model, also known as a multilevel, mixed, or hierarchical linear models. The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in ‘R’, ‘WeMix’ is unique in implementing methods for mixed models using weights at multiple levels. The model is fit using adaptive quadrature following the methodology of Rabe-Hesketh, S., and Skrondal, A. (2006) <doi:10.1111/j.1467-985X.2006.00426.x>.

**Makefile Generator for R Analytical Projects** (**rmake**)

Creates and maintains a build process for complex analytic tasks in R. Package allows to easily generate Makefile for the (GNU) ‘make’ tool, which drives the build process by (parallelly) executing build commands in order to update results accordingly to given dependencies on changed data or updated source files.

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