This is a list of software packages that have been developed by SMARTbiomed collaborators in the past. They are organized by research theme and programming language. As more packages get developed by the Centre, they will be added here, so check back frequently.
Easy-to-use, efficient, flexible and scalable statistical tools. Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping. It provides for instance matrix operations, Principal Component Analysis, sparse linear supervised models, utility functions and more doi:10.1093/bioinformatics/bty185.
Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. Privé et al. (2018) doi:10.1093/bioinformatics/bty185.
When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) doi:10.1016/B978-1-55860-332-5.50011-0 and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) doi:10.1080/10618600.2022.2071905.
Various tools for inferring causal models from observational data. The package includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler and Ekstrøm (2021) doi:10.1093/aje/kwab087. It also includes general tools for evaluating differences in adjacency matrices, which can be used for evaluating performance of causal discovery procedures.
Package development guide for SMARTbiomed coming soon.
A key research output from SMARTbiomed will be software.
SMARTbiomed researchers will develop methods and software for analysing and interpreting health data, including causal inference (led by Erin Gabriel, University of Copenhagen), risk prediction (led by Bjarni Vilhjálmsson, Aarhus University), and machine learning (led by Chris Holmes, University of Oxford). SMARTbiomed will advance medical research, focusing on common complex diseases and disorders, especially cardiometabolic (diabetes, and cardiovascular diseases), brain (psychiatric and neurological), and reproductive (endometriosis, involuntary infertility) conditions.