A small, dependency-light toolkit for talking to token-authenticated
    REST APIs. It manages authentication tokens in process environment variables
    (never written to disk), builds requests with configurable authentication and
    pagination strategies, and retrieves paginated data either one page at a time
    or in chunks combined into a single tibble. The design is API-agnostic: a
    single 'apifetch_api' profile describes an endpoint together with how it
    authenticates and paginates, so the same verbs work across different services.

Parentage assignment package.
    Parentage assignment is performed based on observed average Mendelian transmission probability distributions or Exclusion.
    The main functions of this package are the function APIS_2n(), APIS_3n() and launch_APIShiny(), which perform parentage assignment.

API for using 'episensr', Basic sensitivity analysis of the
    observed relative risks adjusting for unmeasured confounding and
    misclassification of the exposure/outcome, or both. See
    <https://cran.r-project.org/package=episensr>.

Set of tools for fitting the additive partial linear models with symmetric autoregressive errors of order p, or APLMS-AR(p). This setup enables the modeling of a time series response variable using linear and nonlinear structures of a set of explanatory variables, with nonparametric components approximated by natural cubic splines or P-splines. It also accounts for autoregressive error terms with distributions that have lighter or heavier tails than the normal distribution. The package includes various error distributions, such as normal, generalized normal, Student's t, generalized Student's t, power-exponential, and Cauchy distributions. Chou-Chen, S.W., Oliveira, R.A., Raicher, I., Gilberto A. Paula (2024) <doi:10.1007/s00362-024-01590-w>.

An unofficial companion to "Applied
    Logistic Regression" by D.W. Hosmer, S. Lemeshow and
    R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book.