Calculates the Minimum Sample Size Required for Developing a
Multivariable Prediction Model
c(person("Joie", "Ensor", role = c("aut", "cre"), email = "j.ensor@keele.ac.uk"),
person("Emma C.", "Martin", role = "aut", email = "emma.martin@leicester.ac.uk"),
person("Richard D.", "Riley", role = "aut", email = "r.riley@keele.ac.uk"))
Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.
Joie Ensor [aut, cre],
Emma C. Martin [aut],
Richard D. Riley [aut]

Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.

c(
person("Gavin", "Cooper", email = "gavin@gavincooper.net", role = c("aut", "cre", "trl"), comment = "Package creator and maintainer"),
person("Reilly", "Innes", email = "Reilly.Innes@uon.edu.au", role = c("aut")),
person("Caroline", "Kuhne", email = "caroline.kuhne@newcastle.edu.au", role = c("aut")),
person("Jon-Paul", "Cavallaro", email = "jon-paul.cavallaro@uon.edu.au", role = c("aut")),
person("David", "Gunawan", email = "dgunawan@uow.edu.au", role = c("aut"), comment = "Author of original MATLAB code"),
person("Guy", "Hawkins", email = "guy.hawkins@newcastle.edu.au", role = c("aut")),
person("Scott", "Brown", email = "scott.brown@newcastle.edu.au", role = c("aut", "trl"), comment = "Original translation from MATLAB to R"))
Provides an R implementation of the Particle Metropolis within
Gibbs sampler for model parameter, covariance matrix and random effect
estimation. A more general implementation of the sampler based on the paper
by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D.
(2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing
the package is available at <https://newcastlecl.github.io/samplerDoc/> and
includes several detailed examples, some background and troubleshooting
steps.
coda, condMVNorm, MASS, MCMCpack, mvtnorm, stats
Gavin Cooper [aut, cre, trl] (Package creator and maintainer),
Reilly Innes [aut],
Caroline Kuhne [aut],
Jon-Paul Cavallaro [aut],
David Gunawan [aut] (Author of original MATLAB code),
Guy Hawkins [aut],
Scott Brown [aut, trl] (Original translation from MATLAB to R)