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.

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>.

Provides the ability to perform "Marginal Mediation"--mediation
   wherein the indirect and direct effects are in terms of the average marginal effects 
   (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). 
   The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package.
   This framework allows the use of categorical mediators and outcomes with little change in interpretation
   from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details 
   on the method.

Functionality to estimate relative risks, risk differences, and 
  partial effects from mixed model. Marginalisation over random effect terms is accomplished using Markov Chain Monte Carlo.