Create ‘Table 1’ to Describe Baseline Characteristics (tableone)
Creates ‘Table 1’, i.e., description of baseline patient characteristics, which is essential in every medical research. Supports both continuous and categorical variables, as well as p-values and standardized mean differences. Weighted data are supported via the ‘survey’ package. See ‘github’ for a screen cast. ‘tableone’ was inspired by descriptive statistics functions in ‘Deducer’ , a Java-based GUI package by Ian Fellows. This package does not require GUI or Java, and intended for command-line users.

Packed Bar Charts with ‘plotly’ (rPackedBar)
Packed bar charts are a variation of treemaps for visualizing skewed data. The concept was introduced by Xan Gregg at ‘JMP’.

Shape-Constrained Kernel Density Estimation (scdensity)
Implements methods for obtaining kernel density estimates subject to a variety of shape constraints (unimodality, bimodality, symmetry, tail monotonicity, bounds, and constraints on the number of inflection points). Enforcing constraints can eliminate unwanted waves or kinks in the estimate, which improves its subjective appearance and can also improve statistical performance. The main function scdensity() is very similar to the density() function in ‘stats’, allowing shape-restricted estimates to be obtained with little effort. The methods implemented in this package are described in Wolters and Braun (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012) <doi:10.18637/jss.v047.i06>, and Hall and Huang (2002) <http://…/j12n41.htm>. See the scdensity() help for for full citations.

Selecting the Best Set of Relevant Environmental Variables along with the Optimal Regularization Multiplier for Maxent Niche Modeling (MaxentVariableSelection)
Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al., 2014 <DOI:10.1111/ddi.12160>). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent’s regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 <DOI:10.1017/S0376892997000088>). Users of this package should be familiar with Maxent niche modelling.

Fast Algorithms for Large Scale Generalized Distance Weighted Discrimination (DWDLargeR)
Solving large scale distance weighted discrimination. The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <arXiv:1604.05473> for more details.