Counting Motifs in Bipartite Networks (bmotif)
Counts occurrences of motifs in bipartite networks, as well as the number of times each node appears in each unique position within motifs. Intended for use in ecology, but its methods are general and can be applied to any bipartite network.

Bayesian Neural Network for High-Dimensional Nonlinear Variable Selection (BNN)
Perform Bayesian variable selection for high-dimensional nonlinear systems and also can be used to test nonlinearity for a general regression problem. The computation can be accelerated using multiple CPUs. You can refer to Liang, F., Li, Q. and Zhou, L. (2017) at <https://…/SAMSI_DPDA-Liang.pdf> for detail. The publication ‘Bayesian Neural Networks for Selection of drug sensitive Genes’ will be appear on Journals of American Statistical Association soon.

Index Number Calculation (IndexNumR)
Computes bilateral and multilateral index numbers. It has support for several standard bilateral indices as well as the GEKS multilateral index number methods (see Ivancic, Diewert and Fox (2011) <doi:10.1016/j.jeconom.2010.09.003>) . It also supports updating of GEKS indexes using several splicing methods.

Relational Query Generator for Data Manipulation (rquery)
A query generator based on Edgar F. Codd’s relational algebra and operator names (plus experience using ‘SQL’ at big data scale). The design represents an attempt to make ‘SQL’ more teachable by denoting composition a sequential pipeline notation instead of nested queries or functions. Package features include: data processing trees or pipelines as observable objects (able to report both columns produced and columns used), optimized ‘SQL’ generation as an explicit user visible modeling step, and convenience methods for applying query trees to in-memory data.frames.

Short Asynchronous Time-Series Analysis (santaR)
A graphical and automated pipeline for the analysis of short time-series in R (‘santaR’). This approach is designed to accommodate asynchronous time sampling (i.e. different time points for different individuals), inter-individual variability, noisy measurements and large numbers of variables. Based on a smoothing splines functional model, ‘santaR’ is able to detect variables highlighting significantly different temporal trajectories between study groups. Designed initially for metabolic phenotyping, ‘santaR’ is also suited for other Systems Biology disciplines. Command line and graphical analysis (via a ‘shiny’ application) enable fast and parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting options for non-specialist users.

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