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.

Conduct post-selection inference for regression coefficients in linear models after they have been selected by adjusted R squared. The p-values and confidence intervals are valid after model selection with the same data. This allows the user to use all data for both model selection and inference without losing control over the type I error rate. The provided tests are more powerful than data splitting, which bases inference on less data since it discards all information used for selection.

Post-selection inference in linear regression models,
    constructing simultaneous confidence intervals across a user-specified universe of models.
    Implements the methodology described in Kuchibhotla, Kolassa, and Kuffner (2022) "Post-Selection Inference"
    <doi:10.1146/annurev-statistics-100421-044639> to ensure valid inference after model
    selection, with applications in high-dimensional settings like Lasso selection.

Collection of tutorials for working with 'Positron'. Covers scripts, 
    'Quarto' documents, 'Git', 'GitHub', and 'Quarto' websites. Makes extensive use of 
    the tools in the 'tutorial.helpers' package.

Personalize drug regimens using individual pharmacokinetic (PK) and 
    pharmacokinetic-pharmacodynamic (PK-PD) profiles. By combining therapeutic 
    drug monitoring (TDM) data with a population model, 'posologyr' offers 
    accurate posterior estimates and helps compute optimal individualized dosing
    regimens. The empirical Bayes estimates are computed following the method 
    described by Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.

Computes the exact probability density function of X/Y conditioned on positive quadrant for series of  bivariate distributions,for more details see Nadarajah,Song and Si (2019) <DOI:10.1080/03610926.2019.1576893>.