- perms (R-package): a simulation technique based on counting permutations, which can estimate both posterior distributions and marginal likelihoods for any model from which a random sample can be generated.
- Christensen and Moen (2023): perms: Marginal likelihood estimation for binary Bayesian nonparametric models in Python and R, arXiv, preprint arXiv:2309.01536.
- Python version
- smms: The
smmspackage allows you to fit Semi-Markovian multi-state models to panel datasets. The package constructs and optimises the likelihood of arbitrary multi-state models where the possible state transitions can be described by an acyclic graph with one or more initial states and one or more absorbing states.- Aastveit, Cunen, Hjort (2023) : A new framework for semi-Markovian parametric multi-state models with interval censoring. Statistical methods in Medical Research, 2023
- dagsim: DagSim is a Python-based framework and specification language for simulating data based on a Directed Acyclic Graph ( DAG) structure, without any constraints on variable types or functional relations. A succinct YAML format for defining the structure of the simulation model promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure the modularization of the simulation code.
- Hajj, Pensar, Sandve (2023): DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation, Plos one, April 2023
- immuneML: an open-source ecosystem for machine learning analysis of adaptive immune receptor repertoires
- Paper: Pavlovic et al (2021) The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nature Machine Intelligence, 3 (11), 936-944
- pycox: a python package for survival analysis and time-to-event prediction with PyTorch, built on the torchtuples package for training PyTorch models. An R version of this package is available at survivalmodels.
- Kvamme and Borgan (2021): Continuous and discrete-time survival prediction with neural networks. Lifetime data analysis, 27, 710-736
- Kvamme, Borgan, Scheel (2019): Time-to-Event Prediction with Neural Networks and Cox Regression. J. Machine Learning Research, 20, 1-30.
- univariateML: an
R-package for user-friendly maximum likelihood estimation of a selection of parametric univariate densities. In addition to basic estimation capabilities, this package support visualization throughplotandqqmlplot, model selection byAICandBIC, confidence sets through the parametric bootstrap withbootstrapml, and convenience functions such as the density, distribution function, quantile function, and random sampling at the estimated distribution parameters.- Moss (2019): univariateML: An R package for maximum likelihood estimation of univariate densities. J. Open Source Software, Dec. 2019
- EuroForMix: euroformix contains procedures for maximization (frequentistic) and integration (Bayesian) of the likelihood function of a gamma peak height model for single (or replicated) STR/SNP/MPS DNA data for a general specifications of hypotheses. Sensitivity analysis of unknown parameters can be carried out using Markov Chain Monte Carlo method. It also contains procedures for deconvolution and database search and may take care of stutters, allele drop-out and allele drop-in.
- Bleka, Storvik, Gill (2016): EuroForMix: An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. Forensic Science International: Genetics, 21, 35-44, 2016.
- The Genomic HyperBrowser: tools to handle acquisition, processing and analysis of collections of genomic tracks, represented in a simple tabular format, GSuite. Please proceed in either basic or advanced mode.
- Sandve et al (2013): The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleid acids research, 41 (1), 133-141.
Software packages produced by the group
The following list contains packages where some from our group have contributed.
Published Oct. 12, 2023 8:13 AM
- Last modified Oct. 31, 2023 10:51 AM