Dynamic Estimation of Group-Level Opinion (dgo)
Fit dynamic group-level IRT and MRP models from individual or aggregated item response data. This package handles common preprocessing tasks and extends functions for inspecting results, poststratification, and quick iteration over alternative models.

Path Diagrams for Lavaan Models via DiagrammeR (lavaanPlot)
Plots path diagrams from models in lavaan using the plotting functionality from the DiagrammeR package. DiagrammeR provides nice path diagrams via Graphviz, and these functions make it easy to generate these diagrams from a lavaan path model without having to write the DOT language graph specification.

A ‘dplyr’ Back End for Databases (dbplyr)
A ‘dplyr’ back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a ‘DBI’ back end; more advanced features require ‘SQL’ translation to be provided by the package author.

Parallel Distance Matrix Computation using Multiple Threads (parallelDist)
A fast parallelized alternative to R’s native ‘dist’ function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices with support for a broad variety of distance functions from the ‘stats’, ‘proxy’ and ‘dtw’ R packages. For ease of use, the ‘parDist’ function extends the signature of the ‘dist’ function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the ‘RcppParallel’ package to parallelize the distance computations with the help of the ‘TinyThread’ library. Furthermore, the ‘Armadillo’ linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while keeping the implementation flexible enough to support different step patterns and normalization methods.

Transparent and Reproducible Artificial Data Generation (bdlp)
The main function generateDataset() processes a user-supplied .R file that contains metadata parameters in order to generate actual data. The metadata parameters have to be structured in the form of metadata objects, the format of which is outlined in the package vignette. This approach allows to generate artificial data in a transparent and reproducible manner.