The dynpred package contains functions for dynamic prediction in survival analysis.

A domain-specific language for specifying translating recursions
    into dynamic-programming algorithms. See 
    <https://en.wikipedia.org/wiki/Dynamic_programming> for a description
    of dynamic programming.

Intensive longitudinal data have become increasingly prevalent in
    various scientific disciplines. Many such data sets are noisy, multivariate,
    and multi-subject in nature. The change functions may also be continuous,
    or continuous but interspersed with periods of discontinuities (i.e.,
    showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an
    R package that implements a set of computationally efficient algorithms for
    handling a broad class of linear and nonlinear discrete- and continuous-time
    models with regime-switching properties under the constraint of linear
    Gaussian measurement functions. The discrete-time models can generally
    take on the form of a state-space or difference equation model. The
    continuous-time models are generally expressed as a set of ordinary or
    stochastic differential equations. All estimation and computations are
    performed in C, but users are provided with the option to specify the
    model of interest via a set of simple and easy-to-learn model specification
    functions in R. Model fitting can be performed using single-subject time
    series data or multiple-subject longitudinal data. Ou, Hunter, & Chow
    (2019) <doi:10.32614%2FRJ-2019-012> provided a detailed introduction to the
    interface and more information on the algorithms.

Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.

Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in  Matias and Miele (2016) <doi:10.1111/rssb.12200>.