Non Metric Space (Approximate) Library (nmslibR)
A Non-Metric Space Library (‘NMSLIB’ <https://…/nmslib> ) wrapper, which according to the authors ‘is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the ‘NMSLIB’ <https://…/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods’. The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the ‘NMSLIB’ <https://…/nmslib> ‘Python’ Library.

Simultaneous Inference for Multiple Linear Contrasts in GEE Models (mmmgee)
Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the ‘geeM’ package.

Various Methods for Measuring Agreement (agRee)
Bland-Altman plot and scatter plot with identity line for visualization and point and interval estimates for different metrics related to reproducibility/repeatability/agreement including the concordance correlation coefficient, intraclass correlation coefficient, within-subject coefficient of variation, smallest detectable difference, and mean normalized smallest detectable difference.

Provides Progress Bars in ‘knitr’ (knitrProgressBar)
Provides a progress bar similar to ‘dplyr’ that can write progress out to a variety of locations, including stdout(), stderr(), or from file(). Useful when using ‘knitr’ or ‘rmarkdown’, and you still want to see progress of calculations in the terminal.

Nonconvex Penalized Estimation for Generalized Linear Models (ncpen)
An efficient unified algorithm for estimating the nonconvex penalized linear, logistic and Poisson regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For a data set with many variables (high-dimensional data), the algorithm selects relevant variables producing a parsimonious regression model. Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>, Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>. (This project is funded by Julian Virtue Professorship from Center for Applied Research at Graziadio School of Business and Management at Pepperdine University.)

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