Random Walk Covariance Models (rwc)
Code to facilitate simulation and inference when connectivity is defined by underlying random walks. Methods for spatially-correlated pairwise distance data are especially considered. This provides core code to conduct analyses similar to that in Hanks and Hooten (2013) <doi:10.1080/01621459.2012.724647>.

Time Aware Tibbles (tibbletime)
Built on top of the ‘tibble’ package, ‘tibbletime’ is an extension that allows for the creation of time aware tibbles. Some immediate advantages of this include: the ability to perform time based subsetting on tibbles, quickly summarising and aggregating results by time periods, and calling functions similar in spirit to the map family from ‘purrr’ on time based tibbles.

Evaluation of Credit Risk with Structural and Reduced Form Models (CreditRisk)
Evaluation of default probability of sovereign and corporate entities based on structural or intensity based models and calibration on market Credit Default Swap quotes. Damiano Brigo, Massimo Morini, Andrea Pallavicini (2013): ‘Counterparty Credit Risk, Collateral and Funding. With Pricing Cases for All Asset Classes’.

Core Utilities for Developing and Running Spatially Explicit Discrete Event Simulation Models (SpaDES.core)
Provide the core discrete event simulation (DES) framework for implementing spatially explicit simulation models. The core DES components facilitate modularity, and easily enable the user to include additional functionality by running user-built simulation modules.

From Biological Sequences and Simulations to Correlation Analysis (Bios2cor)
The package is dedicated to computation and analysis of correlation/co-variation in multiple sequence alignments and in side chain motions during molecular dynamics simulations. Features include the ability to compute correlation/co-variation using a variety of scoring functions between either sequence positions in alignments or side chain dihedral angles in molecular dynamics simulations and to analyze the correlation/co-variation matrix through a variety of tools including network representation and principal components analysis. In addition, several utility functions are based on the R graphical environment to provide friendly tools for help in data interpretation. Examples of sequence co-variation analysis and utility tools are provided in: Pele J, Moreau M, Abdi H, Rodien P, Castel H, Chabbert M. (2014) <doi:10.1002/prot.24570>. This work was supported by the Franch National Research Agency (Grant number: ANR-11-BSV2-026).