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High-Dimensional Inference
Implementation of multiple approaches to perform inference in high-dimensional models.
Biodemography Functions
The Biodem package provides a number of functions for Biodemographic analysis.
Data Sets for Copula Modeling
Data sets used for copula modeling in addition to those in the R package 'copula'. These include a random subsample from the US National Education Longitudinal Study (NELS) of 1988 and nursing home data from Wisconsin.
The Ranking Project: Visualizations for Comparing Populations
Functions to generate plots and tables for comparing independently-
sampled populations. Companion package to "A Primer on Visualizations
for Comparing Populations, Including the Issue of Overlapping Confidence
Intervals" by Wright, Klein, and Wieczorek (2019)
Estimation in Optimal Adaptive Two-Stage Designs
Methods to evaluate the performance characteristics of
various point and interval estimators for optimal adaptive two-stage designs as described
in Meis et al. (2024)
Big Data Statistical Analysis for High-Dimensional Models
Big data statistical analysis for high-dimensional models is made possible by modifying lasso.proj() in 'hdi' package by replacing its nodewise-regression with sparse precision matrix computation using 'BigQUIC'.
A Toolbox for Writing Pretty Papers and Reports
A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based reports and to prettify the output of various estimated models.
Estimation of Order of Mixture Distributions
Methods for estimating the order of a mixture model. The approaches considered are
based on the following papers (extensive list of references is available in the vignette):
1. Dacunha-Castelle, Didier, and Elisabeth Gassiat. The estimation of the order of a mixture model. Bernoulli 3, no. 3 (1997): 279-299. < https://projecteuclid.org/download/pdf_1/euclid.bj/1177334456>.
2. Woo, Mi-Ja, and T. N. Sriram. Robust estimation of mixture complexity. Journal of the American Statistical Association 101, no. 476 (2006): 1475-1486.
Generalized Additive Latent and Mixed Models
Estimates generalized additive latent and
mixed models using maximum marginal likelihood,
as defined in Sorensen et al. (2023)
Fast Kernel Density Estimation with Hexagonal Grid
Kernel density estimation with hexagonal grid for bivariate data.
Hexagonal grid has many beneficial properties like equidistant neighbours
and less edge bias, making it better for spatial analyses than the more
commonly used rectangular grid.
Carr, D. B. et al. (1987)