Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. Privé et al. (2018) doi:10.1093/bioinformatics/bty185.

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.

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.

gact provides a flexible infrastructure for managing and analyzing large-scale genomic association data. It supports building association databases, processing biological resources, and handling GWAS summary statistics. gact links genetic markers to genes, proteins, metabolites, and pathways, and integrates seam-lessly with qgg to enable reproducible, extensible workflows for integrative genomics.

• (S)BayesS – for estimating key genetic architecture parameters such as SNP-based heritability, polygenicity, and the relationship between effect sizes and minor allele frequencies.

• GWFM – for genome-wide fine-mapping of causal variants.

GCTB provides a comprehensive framework for understanding genetic architecture and improving prediction accuracy in complex trait studies.

qgg provides advanced tools for statistical modeling and analysis of large-scale genomic data. It implements Bayesian Linear Regression (BLR) models for fine-mapping, polygenic scoring, and gene set enrichment analysis, combining efficient algorithms with high-performance computing for scalable quantitative genomics.