Risk Attribution of a portfolio with Volatility Risk Analysis.

Designed to aid both academic researchers and asset managers in conducting factor based portfolio sorts.  
    Provides functionality to sort assets into portfolios for up to three factors via a conditional or unconditional sorting procedure.

Following Sommer (2022) <https://mediatum.ub.tum.de/1658240>
    portfolio level risk estimates (e.g. Value at Risk, Expected
    Shortfall) are estimated by modeling each asset univariately by an
    ARMA-GARCH model and then their cross dependence via a Vine Copula
    model in a rolling window fashion. One can even condition on
    variables/time series at certain quantile levels to stress test the
    risk measure estimates.

Provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.

In linear LS regression, calculate for a given design matrix
  the multiplier K of coefficient standard errors such that the
  confidence intervals [b - K*SE(b), b + K*SE(b)] have a
  guaranteed coverage probability for all coefficient estimates
  b in any submodels after performing arbitrary model selection.

Determine individual pharmacokinetic (and
    pharmacokinetic-pharmacodynamic) profiles and use them to personalise drug
    regimens. You provide the data and a population pharmacokinetic model,
    'posologyr' provides the individual a posteriori estimate and allows you to
    determine the optimal dosing. The empirical Bayes estimates are computed as
    described in Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.