Showing 6 of total 6 results (show query)
bioc
metagenomeSeq:Statistical analysis for sparse high-throughput sequencing
metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
Maintained by Joseph N. Paulson. Last updated 4 months ago.
immunooncologyclassificationclusteringgeneticvariabilitydifferentialexpressionmicrobiomemetagenomicsnormalizationvisualizationmultiplecomparisonsequencingsoftware
69 stars 11.90 score 494 scripts 7 dependents
florianhartig
BayesianTools:General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics
General-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.
Maintained by Florian Hartig. Last updated 1 years ago.
bayesecological-modelsmcmcoptimizationsmcsystems-biologycpp
124 stars 10.18 score 580 scripts 5 dependents
phil8192
obAnalytics:Limit Order Book Analytics
Data processing, visualisation and analysis of Limit Order Book event data.
Maintained by Philip Stubbings. Last updated 6 years ago.
bitcoinlimit-order-booktradingvisualisation
152 stars 6.36 score 30 scripts
darkeyes
VLTimeCausality:Variable-Lag Time Series Causality Inference Framework
A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.
Maintained by Chainarong Amornbunchornvej. Last updated 10 months ago.
causal-inferencegranger-causalitytime-seriestime-series-analysistransfer-entropy
54 stars 5.77 score 11 scripts
grafxzahl
genBaRcode:Analysis and Visualization Tools for Genetic Barcode Data
Provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).
Maintained by Lars Thielecke. Last updated 23 days ago.
2.30 score 6 scripts
marinasams
KarsTS:An Interface for Microclimate Time Series Analysis
An R code with a GUI for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) <doi:10.1016/j.physrep.2006.11.001>) and filling methods (Moffat et al. (2007) <doi:10.1016/j.agrformet.2007.08.011>), as well as tools to manipulate easily time series and gap sets.
Maintained by Marina Saez. Last updated 4 years ago.
1.38 score 24 scripts