Dynamic linear models and time series regression.

Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) <doi:10.1016/j.apm.2013.05.038>; Nagy et al. (2011) <doi:10.1002/acs.1239>.

Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, and mgcv::gam model objects.

Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.

Provides tools for describing parameters of algorithms in an abstract way.
  Description can include an id, a description, a domain (range or list of values),
  and a default value. 'dynparam' can also convert parameter sets to a 'ParamHelpers' format, 
  in order to be able to use 'dynparam' in conjunction with 'mlrMBO'.

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.