Format Output of Various Routines in a Suitable Way for Reports and Publication (Publish)
A bunch of convenience functions that transform the results of some basic statistical analyses into table format nearly ready for publication. This includes descriptive tables, tables of logistic regression and Cox regression results as well as forest plots.

linl’ is not ‘Letter’ (linl)
A ‘LaTeX’ Letter class for ‘rmarkdown’, using the ‘pandoc-letter’ template adapted for use with ‘markdown’.

Fit a Modified Connor-Mosimann Distribution to Elicited Quantiles in Multinomial Problems (modcmfitr)
Fits a modified version of the Connor-Mosimann distribution (Connor & Mosimann (1969)<doi:10.2307/2283728>), a Connor-Mosimann distribution or Dirichlet distribution (e.g. Gelman, Carlin, Stern & Rubin Chapter 3.5 (2004, <ISBN:1-58488-388-X>) to elicited quantiles of a multinomial distribution. Code is also provided to sample from the distributions, generating inputs suitable for a probabilistic sensitivity analysis / Monte Carlo simulation in a decision model.

Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring (mvMonitoring)
Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data generated from a continuous-time multivariate industrial or natural process. Employ PCA-based dimension reduction to extract linear combinations of relevant features, reducing computational burdens. For a description of ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al. The multi-state application of ADPCA is from a manuscript under current revision entitled ‘Multi-State Multivariate Statistical Process Control’ by Odom, Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018.

Automate Package and Project Setup (usethis)
Automate package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, ‘GitHub’, licenses, ‘Rcpp’, ‘RStudio’ projects, and more.

DEoptim’ and ‘DEoptimR’ Plugin for the ‘R’ Optimization Interface (ROI.plugin.deoptim)
Enhances the R Optimization Infrastructure (‘ROI’) package with the ‘DEoptim’ and ‘DEoptimR’ package. ‘DEoptim’ is used for unconstrained optimization and ‘DEoptimR’ for constrained optimization.