Imports: igraph, methods, utils, stats, graphics, ggplot2, reshape2,

Suggests: sybilSBML, parallel, knitr, rmarkdown

communities (Bauer and Zimmermann (2017) <doi:10.1371/journal.pcbi.1005544>).
Each organism is represented individually and genome scale
Imports: methods, utils, stats, graphics, ggplot2, reshape2, glpkAPI,

Description: This is the companion package of a PhD thesis entitled "Bases Giving Distances. A new paradigm for investigating functional data with applications for spectroscopy" by Timmermans (2012). See references for details and related publications. The core of the BAGIDIS methodology is a functional wavelet based semi-distance that has been introduced by Timmermans and von Sachs (2010, 2015) and Timmermans, Delsol and von Sachs (2013). This semi-distance allows for comparing curves with sharp local patterns that might not be well aligned from one curve to another. It is data-driven and highly adaptive to the curves being studied.  Its main originality is its ability to consider simultaneously horizontal and vertical variations of patterns, which proofs highly useful when used together with clustering algorithms or visualization method. BAGIDIS is an acronym for BAsis GIving DIStances. The extension of BAGIDIS to image data relies on the same principles and has been described in Timmermans and Fryzlewicz (2012), Fryzlewicz and Timmermans (2015).
Packaged: 2015-06-25 10:47:43 UTC; Catherine

Title: Balance Check for Multiple Covariates in Matched Observational
Description: Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.

Title: Balanced and spatially balanced sampling
Description: This package includes methods that can select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method and spatially correlated Poisson sampling (for spatially balanced sampling) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included.