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 simulations and graphical depictions of autoregressive
    relationships.

Time-varying coefficient models for interval censored and
    right censored survival data including
    1) Bayesian Cox model with time-independent, time-varying or
    dynamic coefficients for right censored and interval censored data studied by
    Sinha et al. (1999) <doi:10.1111/j.0006-341X.1999.00585.x> and
    Wang et al. (2013) <doi:10.1007/s10985-013-9246-8>,
    2) Spline based time-varying coefficient Cox model for right censored data
    proposed by Perperoglou et al. (2006) <doi:10.1016/j.cmpb.2005.11.006>, and
    3) Transformation model with time-varying coefficients for right censored data
    using estimating equations proposed by
    Peng and Huang (2007) <doi:10.1093/biomet/asm058>.