Solving Differential Equations (ODEs, SDEs, DDEs, DAEs) (diffeqr)
An interface to ‘DifferentialEquations.jl’ from the R programming language. It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. ‘diffeqr’ attaches an R interface onto the package, allowing seamless use of this tooling by R users.

Efficient Computations of Standard Clustering Comparison Measures (aricode)
Implements an efficient O(n) algorithm based on bucket-sorting for fast computation of standard clustering comparison measures. Available measures include adjusted Rand index (ARI), normalized information distance (NID), normalized mutual information (NMI), normalized variation information (NVI) and entropy, as described in Vinh et al (2009) <doi:10.1145/1553374.1553511>.

Bayesian Latent Space Model (BLSM)
Provides a Bayesian latent space model for complex networks, either weighted or unweighted. Given an observed input graph, the estimates for the latent coordinates of the nodes are obtained through a Bayesian MCMC algorithm. The overall likelihood of the graph depends on a fundamental probability equation, which is defined so that ties are more likely to exist between nodes whose latent space coordinates are close. The package is mainly based on the model by Hoff, Raftery and Handcock (2002) <doi:10.1198/016214502388618906> and contains some extra features (e.g., removal of the Procrustean step, weights implemented as coefficients of the latent distances, 3D plots). The original code related to the above model was retrieved from <https://…/>. Users can inspect the MCMC simulation, create and customize insightful graphical representations or apply clustering techniques.

Medical Devices Surveillance (mds)
A set of core functions for handling medical device event data in the context of post-market surveillance, pharmacovigilance, signal detection and trending, and regulatory reporting. Primary inputs are data on events by device and data on exposures by device. Outputs include: standardized device-event and exposure datasets, defined analyses, and time series.

Interface to ‘MLeap’ (mleap)
A ‘sparklyr’ <> extension that provides an interface to ‘MLeap’ <https://…/mleap>, an open source library that enables exporting and serving of ‘Apache Spark’ pipelines.