Provides tools to fit joint models of multivariate longitudinal data and time-to-event data for dynamic prediction. It allows the joint prediction of both future time-to-event outcomes and future longitudinal outcomes conditional on survival. The models accommodate irregularly measured longitudinal data and competing risks outcomes. The use of the backward joint model enables fast and efficient computation, especially for applications with large sample sizes and many longitudinal variables.

A compact, colour-first toolkit for the analysis of plant
    breeding and genetics field experiments. It provides analysis of variance
    for the randomised block design (RBD) and factorial RBD, a check-anchored
    intra-block analysis for augmented alpha-lattice designs, and the core
    biometrical-genetics workflow used in crop improvement: estimation of
    genetic variability (genotypic and phenotypic coefficients of variation,
    broad-sense heritability, expected genetic advance), genotypic and
    phenotypic correlation, path-coefficient analysis, line x tester and
    Griffing diallel combining-ability analysis (general combining ability
    and specific combining ability), Mahalanobis D-square genetic-divergence
    analysis with Tocher and hierarchical clustering, and
    genotype-by-environment stability analysis (Eberhart-Russell regression
    and the additive main effects and multiplicative interaction (AMMI)
    model). Methods follow Griffing (1956) <doi:10.1071/BI9560463> and
    Eberhart and Russell (1966)
    <doi:10.2135/cropsci1966.0011183X000600010011x>. Every analysis returns a
    tidy result object and a publication-ready 'ggplot2' figure using a
    bespoke high-contrast colour system.

Implementation of a statistical approach 
  for estimating the joint health effects of multiple 
  concurrent exposures, as described in Bobb et al (2015) 
  <doi:10.1093/biostatistics/kxu058>.

Bayesian kernel machine regression (from the 'bkmr' package)
    is a Bayesian semi-parametric generalized linear model approach under
    identity and probit links. There are a number of functions in this
    package that extend Bayesian kernel machine regression fits to allow
    multiple-chain inference and diagnostics, which leverage functions
    from the 'future', 'rstan', and 'coda' packages.  Reference: Bobb, J.
    F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical
    software for analyzing the health effects of multiple concurrent
    exposures via Bayesian kernel machine regression. ;
    <doi:10.1186/s12940-018-0413-y>.