Implements popular methods for matching in time-varying
    observational studies. Matching is difficult in this scenario because
    participants can be treated at different times which may have an
    influence on the outcomes. The core methods include: "Balanced Risk
    Set Matching" from Li, Propert, and Rosenbaum (2011)
    <doi:10.1198/016214501753208573> and "Propensity Score Matching with
    Time-Dependent Covariates" from Lu (2005)
    <doi:10.1111/j.1541-0420.2005.00356.x>. Some functions use the
    'Gurobi' optimization back-end to improve the optimization problem
    speed; the 'gurobi' R package and associated software can be
    downloaded from <https://www.gurobi.com> after obtaining a license.

Calculate the matrices in
    Shiller (1991, <doi:10.1016/S1051-1377(05)80028-2>) that serve as the
    foundation for many repeat-sales price indexes.

Provides methods for model building and model evaluation of mixed effects models using 
    'Monolix' <https://monolix.lixoft.com>. 'Monolix' is a software tool for nonlinear mixed effects 
    modeling that must have been installed in order to use 'Rsmlx'. 
    Among other tasks, 'Rsmlx' provides a powerful tool for automatic PK model building, performs 
    statistical tests for model assessment, bootstrap simulation and likelihood profiling for 
    computing confidence intervals. 'Rsmlx' also proposes several automatic covariate search methods for 
    mixed effects models.

The Snell scoring procedure, implemented in R. This procedure was first described by E.J Snell (1964) <doi:10.2307/2528498> and was later used by Tong et al (1977) <doi:10.4141/cjas77-001> in dairy.

The Stuttgart Neural Network Simulator (SNNS) is a library
    containing many standard implementations of neural networks. This
    package wraps the SNNS functionality to make it available from
    within R. Using the 'RSNNS' low-level interface, all of the
    algorithmic functionality and flexibility of SNNS can be accessed.
    Furthermore, the package contains a convenient high-level
    interface, so that the most common neural network topologies and
    learning algorithms integrate seamlessly into R.