Functionalities for analyzing high-dimensional and longitudinal biomarker data to facilitate precision medicine, using a joint model of Bayesian sparse factor analysis and dependent Gaussian processes. This paper illustrates the method in detail: J Cai, RJB Goudie, C Starr, BDM Tom (2023) <doi:10.48550/arXiv.2307.02781>.

Interface to the 'python' package 'dgpsi' for Gaussian process, deep Gaussian process, 
             and linked deep Gaussian process emulations of computer models and networks using stochastic imputation (SI). 
             The implementations follow Ming & Guillas (2021) <doi:10.1137/20M1323771> and 
             Ming, Williamson, & Guillas (2023) <doi:10.1080/00401706.2022.2124311> and 
             Ming & Williamson (2023) <doi:10.48550/arXiv.2306.01212>. To get started with the package, 
             see <https://mingdeyu.github.io/dgpsi-R/>.

"Uses a simulation-based approach to create readily
  interpretable scaled (quantile) residuals for fitted generalized linear (mixed)
  models. Currently supported are linear and generalized linear (mixed) models from
  'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive', 'spaMM', and 'brms'
  (simple models); phylogenetic linear models from 'phylolm' (classes 'phylolm' and 'phyloglm');
  generalized additive models ('gam' from 'mgcv'); 'glm' (including 'negbin' from 'MASS', but excluding
  quasi-distributions) and 'lm' model classes. Moreover, externally created simulations,
  e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN',
  or 'BUGS' can be processed as well. The resulting residuals are standardized to
  values between 0 and 1 and can be interpreted as intuitively as residuals from a
  linear regression. The package also provides a number of plot and test functions
  for typical model misspecification problems, such as over/underdispersion, zero-inflation,
  and residual spatial, temporal and phylogenetic autocorrelation."

Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.