c(
person(given = "Emma",
family = "Martin",
email = "emma.martin@le.ac.uk",
role = c("aut")),
person(given = "Alessandro",
family = "Gasparini",
email = "alessandro.gasparini@ki.se",
role = c("aut")),
person(given = "Michael",
family = "Crowther",
email = "michael.crowther@le.ac.uk",
role = c("aut", "cre"))
)
MASS, randtoolbox, statmod, stats, survival, tools

N methods are used to measure each of n items.
This data is used to estimate the accuracy and precision of
the methods. Maximum likelihood estimation is used for the
precision estimates.

N>=3 methods are used to measure each of n items.  The
data are used to estimate simultaneously systematic error
(bias) and random error (imprecision). Observed measurements
for each method or device are assumed to be linear functions of
the unknown true values and the errors are assumed Normally
distributed. Maximum likelihood estimation is used for the
imprecision standard deviation estimates.  Pairwise calibration
curves and plots can be easily generated.

N>=3 methods are used to measure each of n items.
The data are used to estimate simultaneously systematic error (bias)
and random error (imprecision). Observed measurements for each method
or device are assumed to be linear functions of the unknown true values
and the errors are assumed normally distributed. Maximum likelihood
estimation is used for the imprecision standard deviation estimates.
Pairwise calibration curves and plots can be easily generated.

N methods are used to measure each of n items. This data
is used to estimate the accuracy and precision of the methods.
Maximum likelihood estimation is used for the precision
estimates.

Bayesian Regression with Meshed Gaussian Processes
Fits Bayesian spatial or spatiotemporal multivariate regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889> and Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>.
Rcpp (>= 1.0.5), stats, dplyr, glue, rlang, magrittr