Routines for state estimate in a linear
    Gaussian state space model and a simple stochastic volatility model using
    particle filtering. Parameter inference is also carried out in these models
    using the particle Metropolis-Hastings algorithm that includes the particle
    filter to provided an unbiased estimator of the likelihood. This package is
    a collection of minimal working examples of these algorithms and is only
    meant for educational use and as a start for learning to them on your own.

Conduct internal validation of a clinical prediction model for a binary outcome.
  Produce bias corrected performance metrics (c-statistic, Brier score, calibration intercept/slope)
  via bootstrap (simple bootstrap, bootstrap optimism, .632 optimism) and cross-validation (CV optimism,
  CV average). Also includes functions to assess model stability via bootstrap resampling.
  See Steyerberg et al. (2001) <doi:10.1016/s0895-4356(01)00341-9>;
  Harrell (2015) <doi:10.1007/978-3-319-19425-7>;
  Riley and Collins (2023) <doi:10.1002/bimj.202200302>.

Check available classification and regression data sets from the PMLB repository and download them.
    The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms.
    These data sets cover a range of applications, and include binary/multi-class classification problems and 
    regression problems, as well as combinations of categorical, ordinal, and continuous features.
    There are currently over 150 datasets included in the PMLB repository.

Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.