Provides analytic and simulation tools to estimate the minimum
    sample size required for achieving a target prediction mean-squared error
    (PMSE) or a specified proportional PMSE reduction (pPMSEr) in linear
    regression models.  Functions implement the criteria of Ma (2023) 
    <https://digital.wpi.edu/downloads/0g354j58c>, support covariance-matrix 
    handling, and include helpers for root-finding and diagnostic plotting.

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>.

Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.

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://university-of-newcastle-research.github.io/samplerDoc/> and
    includes several detailed examples, some background and troubleshooting
    steps.

Tools for the practical management of financial
  portfolios: backtesting investment and trading strategies,
  computing profit/loss and returns, analysing trades,
  handling lists of transactions, reporting, and more.  The
  package provides a small set of reliable, efficient and
  convenient tools for processing and analysing
  trade/portfolio data.  The manual provides all the details;
  it is available from
  <https://enricoschumann.net/R/packages/PMwR/manual/PMwR.html>.
  Examples and descriptions of new features are provided at
  <https://enricoschumann.net/notes/PMwR/>.