Implements a parsimonious evolutionary model to analyze and
  predict gene-functional annotations in phylogenetic trees as described in Vega
  Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. Focusing on
  computational efficiency, 'aphylo' makes it possible to estimate pooled
  phylogenetic models, including thousands (hundreds) of annotations (trees) in
  the same run. The package also provides the tools for visualization of
  annotated phylogenies, calculation of posterior probabilities (prediction)
  and goodness-of-fit assessment featured in Vega Yon et al. (2021).

Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (<https://jfrench.github.io/LinearRegression/>).

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>.