Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for high-dimensional data subject to measurement error in predictors. It can be used to address generalized linear models (GLM) in Chen (2023) <doi: 10.1007/s11222-023-10209-3> and the accelerated failure time (AFT) model in Chen and Qiu (2023) <doi: 10.1111/biom.13898>. Some relevant references include Chen and Yi (2021) <doi:10.1111/biom.13331> and Hastie, Tibshirani, and Friedman (2008, ISBN:978-0387848570).

The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated.  Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation.  Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values.  The code is built around the plink FAM table format for pedigrees.  Described in Yao and Ochoa (2022) <doi:10.1101/2022.03.25.485885>.

Through simfinapi, you can intuitively access the 'SimFin'
    Web-API (<https://www.simfin.com/>) to make 'SimFin' data easily
    available in R. To obtain an 'SimFin' API key (and thus to use this
    package), you need to register at <https://app.simfin.com/login>.

Simulates data from model objects (e.g., from lm(), glm()),
    and plots this along with the original data to compare how well the
    simulated data matches the original data to determine model fit.

A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort.  The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results.  It follows a clear object-oriented design and supports parallel computing to increase computational performance.