* Fit Hundreds of Theoretical Distributions to Empirical Data* (

**fitteR**)

Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a ‘shiny’ app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.

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**Memory-Map Character Files****mmapcharr**)

Uses memory-mapping to enable the random access of elements of a text file of characters separated by characters as if it was a simple R(cpp) matrix.

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**Manipulating External Pointer****xptr**)

There is limited native support for external pointers in the R interface. This package provides some basic tools to verify, create and modify ‘externalptr’ objects.

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**Support Points****support**)

Provides the function sp() for generating the support points proposed in Mak and Joseph (2017) <arXiv:1609.01811>. Support points are representative points of a possibly non-uniform distribution, and can be used as optimal sampling or integration points for a distribution of choice. The provided function sp() can be used to generate support points for standard distributions or for reducing big data (e.g., from Markov-chain Monte Carlo methods). A detailed description of the algorithm is found in Mak and Joseph (2017).

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**Resistance Relationship Networks using Graphical LASSO****Rnets**)

Novel methods are needed to analyze the large amounts of antimicrobial resistance (AMR) data generated by AMR surveillance programs. This package is used to estimate resistance relationship networks, or ‘Rnets’, from empirical antimicrobial susceptibility data. These networks can be used to study relationships between antimicrobial resistances (typically measured using MICs) and genes in populations. The ‘GitHub’ for this package is available at <https://…/Rnets>. Bug reports and features requests should be directed to the same ‘GitHub’ site. The methods used in ‘Rnets’ are available in the following publications: An overview of the method in WJ Love, et al., ‘Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012’ (2016) <doi:10.1371/journal.pcbi.1005160>; The graphical LASSO for sparsity in J Friedman, T Hastie, R Tibshirani ‘Sparse inverse covariance estimation with the graphical lasso’ (2007) <doi:10.1093/biostatistics/kxm045>; L1 penalty selection in H Liu, K Roeder, L Wasserman ‘Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models’ (2010) <arXiv:1006.3316>; Modularity for graphs with negative edge weights in S Gomez, P Jensen, A Arenas. ‘Analysis of community structure in networks of correlated data’ (2009) <doi:10.1103/PhysRevE.80.016114>.

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**Testing for Change in C-Statistic****CsChange**)

Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as ‘Somers’ Dxy rank correlation’/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.