Software

collapse

Advanced and Fast Statistical Computing and Data Transformation in R

collapse is a large C/C++-based infrastructure package facilitating complex statistical computing, data transformation, and exploration tasks in R - at outstanding levels of performance and memory efficiency. It also implements a class-agnostic approach to R programming, supporting vector, matrix and data frame-like objects and their popular variants (e.g., factor, ts, xts, tibble, data.table, sf), ensuring seamless compatibility with large parts of the R ecosystem. collapse is among the world's fastest data frame libraries and has been benchmarked (Windows | Linux) for standard tasks. It excels at complex statistical tasks, such as weighted quantiles, statistical modes, time series and panel data operations. It was downloaded ~2 million times.

fastverse

An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R

The fastverse is a suite of complementary high-performance (C/C++-based) packages for statistical computing and data manipulation in R, which have stable APIs, few dependencies, and are scaleable to large and complex tasks. The fastverse R package enables easy installation, loading and management of these packages. An active list of suggested extension packages endorses further high-quality packages. In short, fastverse packages power robust, performant, and lightweight solutions to complex problems using R.

Website | GitHub

dfms

Dynamic Factor Models for R

dfms enables numerically robust and computationally efficient estimation of the (linear Gaussian) Dynamic Factor Model via the EM algorithm. Convenient methods for forecasting and extracting factors facilitate its application to time series dimensionality reduction, forecasting, and nowcasting tasks. The implementation is based on efficient C++ code, making dfms orders of magnitude faster than other R packages.

Website | GitHub | Short Paper

OptimalTransportNetworks.jl

Optimal Transport Networks in Spatial Equilibrium

Julia implementation of the quantitative spatial model and algorithms presented in: Fajgelbaum, P. D., & Schaal, E. (2020). Optimal transport networks in spatial equilibrium. Econometrica, 88(4), 1411-1452. The software uses duality principles to optimize over the space of networks, nesting an optimal flows problem and a neoclasical general-equilibrium trade model into a global network design problem to derive the optimal (welfare maximizing) transport network (extension) from any primitive set of economic fundamantals.

Website | GitHub

osmclass

Classify Open Street Map Features

Efficeintly classify Open Street Map (OSM) features into meaningful functional or analytical categories. Designed for OSM PBF files, e.g. from geofabrik.de, imported as spatial data frames. A classification consists of a list of categories that map to certain OSM tags and values. The package includes a classification of OSM features by economic function/significance following Krantz (2024).

Website | GitHub

dggridR

Discrete Global Grids for R: Spatial Analysis Done Right

dggridR is an R port to the DGGRID C++ library which builds discrete global grids that partition the surface of the Earth into hexagonal, triangular, or diamond cells, all of which have the same size. I am a contributor to and the current maintainer of it.

GitHub

API Packages

I have created several open databases (see Data Products) for which I have written API packages:

The Africa Monitor Database (ARFMDB) accessible via R package africamonitor and Python package africamonitor.
The South Africa Macroeconomic Database (SAMADB) accessible via R package samadb, Python package samadb, and Julia package SAMaDB.jl.
The Uganda Time Series Database (UGATSDB) accessible via R package ugatsdb.

Contributions

I have contributed to R packages fixest (Fast Fixed-Effects Estimations), plm (Linear Models for Panel Data), decompr (Global Value Chain Decomposition), and kit (Data Manipulation Functions Implemented in C).