Local recombination rates are graphically estimated across a genome using Marey maps.

Implements likelihood inference based on higher order approximations for linear nonnormal regression models.

The target of 'margaret' is help to extract data from Minciencias to analyze scientific production in Colombia.

Implements a non-stationary extreme value analysis framework
    by coupling a covariate-driven Non-Homogeneous Poisson Process (NHPP)
    with Elastic-Net regularization and exact analytical gradients. Provides
    methodologies for estimating conditional return levels and unconditional
    (marginalized) return levels via parametric stochastic integration over
    Vector Autoregressive VAR(p) covariate trajectories, or non-parametric
    block bootstrapping. Methodologies are based on Villa (2026)
    <https://sabi.ufrgs.br/> "A Novel Regularized Point Process and Stochastic
    Marginalization Framework for Return Level Inference under Covariate-Driven
    Extremes" (Master's dissertation, Universidade Federal do Rio Grande do Sul).

Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.

Estimates risk as a function of a marker by integrating over other covariates in a conditional risk model.

Test the marginal correlation between a scalar response variable with a vector of explanatory variables 
    using the max-type test with bootstrap.
    The test is based on the max-type statistic and its asymptotic distribution
    under the null hypothesis of no marginal correlation.
    The bootstrap procedure is used to approximate the null distribution of the test statistic.
    The package provides a function for performing the test. For more technical details, refer to Zhang and Laber (2014) <doi:10.1080/01621459.2015.1106403>.