Useful functions for various DDI (Data Documentation Initiative)
    related inputs and outputs. Converts data files to and from DDI, SPSS,
    Stata, SAS, R and Excel, including user declared missing values.

Statistical inference for the regression coefficients in high-dimensional linear models with hidden confounders. The Doubly Debiased Lasso method was proposed in <arXiv:2004.03758>.

A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired.

Estimate common causal parameters using double/debiased machine 
    learning as proposed by Chernozhukov et al. (2018) <doi:10.1111/ectj.12097>. 
    'ddml' simplifies estimation based on (short-)stacking as discussed in 
    Ahrens et al. (2024) <doi:10.1177/1536867X241233641>, which leverages multiple base 
    learners to increase robustness to the underlying data generating process.

Provides functions for computing the density, distribution, and random generation of the Decision Diffusion model (DDM), a widely used cognitive model for analysing choice and response time data. The package allows model specification, including the ability to fix, constrain, or vary parameters across experimental conditions. While it does not include a built-in optimiser, it supports likelihood evaluation and can be integrated with external tools for parameter estimation. Functions for simulating synthetic datasets are also provided. This package is intended for researchers modelling speeded decision-making in behavioural and cognitive experiments. For more information, see Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Voss and Voss (2007) <doi:10.3758/BF03192967>, and Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>.