The package is only available from this GitHub
repository, for now. On Windows machines, you need to install a few dependencies, including Rtools first, e.g. by running
pkgs <- c("MASS", "Rtools", "remotes")
repos <- c("https://cran.rstudio.com", "https://inla.r-inla-download.org/R/stable")
install.packages(pkgs, repos=repos, dependencies = "Depends")
before installing the package using remotes
:
remotes::install_github("giabaio/bmhe_utils")
Under Linux or MacOS, it is sufficient to install the package via remotes
:
install.packages("remotes")
remotes::install_github("giabaio/bmhe_utils")
Alternatively, it is possible to install survHEinla
from source with the following command.
install.packages(
'bmhe',
repos = c('https://giabaio.r-universe.dev', 'https://cloud.r-project.org')
)
(NB: You can replace the CRAN mirror to any other, e.g. https://www.stats.bris.ac.uk/R/
--- see here).
Load the package into the R
workspace as usual
library(bmhe)
and use all the available functions. Roughly speaking, these can be divided into "plotting", "printing" and "utility".
betaplot
Trial-and-error Beta plot (using[manipulate](https://cran.r-project.org/web/packages/manipulate/index.html)
)coefplot
"Coefplot" for the parameters in the model (using[tidyverse](https://www.tidyverse.org/)
)diagplot
Specialised diagnostic plots to check convergence and autocorrelation of the MCMC rungammaplot
Trial-and-error Gamma plot (using[manipulate](https://cran.r-project.org/web/packages/manipulate/index.html)
)posteriorplot
Various plots for the posteriors in a 'bugs' or 'jags' objecttraceplot
Makes a traceplot (eg to visualise MCMC simulations from multiple chains, using[tidyverse](https://www.tidyverse.org/)
)acfplot
Autocorrelation plot
print.bugs
Modifies the built-in print method for theR2OpenBUGS
package to provide a few more options and standardisationprint.rjags
Modifies the built-in print method for theR2jags
package to provide a few more options and standardisationstats
Computes and prints summary statistics for a vector or matrix of simulated values
betaPar
Computes the parameters of a Beta distribution so that the mean and standard dev are the input (m,s)betaPar2
Compute the parameters of a Beta distribution, given a prior guess for key parameters. Based on "Bayesian ideas and data analysis", page 100. Optimisation method to identify the values of a,b that give required conditions on the Beta distributionilogit
Computes the inverse logit of a number between -infinity and +infinitylogit
Computes the logit of a numberlognPar
Computes mean and variance of a logNormal distribution so that the parameters on the natural scale are mu and sigmaodds2probs
Maps from odds to probabilitiesOR
Computes the odds ratio between two probabilitiesdlogitnorm
,plogitnorm
,qlogitnorm
,rlogitnorm
Computes the density, probability distribution, quantiles and random numbers from the logit-Normal distribution. The code is lifted from thegraybox
package (with attribution)change_of_variable
Computes the density for a variable y=f(x) using the rule of the change of variable and given as inputs the functions f, g=f^{-1} and the distribution p = p_X(x)
Please submit contributions through Pull Requests
. To report issues and/or seek support, please file a new ticket in the issue tracker.