Search Results for "error"

...Generalized linear models with support for all of the one-parameter exponential family distributions. Markov switching models (MSAR), also known as Hidden Markov Models (HMM). Vector autoregressive models, VAR and structural VAR. Vector error correction model, VECM. Robust linear models with support for several M-estimators. statsmodels supports specifying models using R-style formulas and pandas DataFrames.

...It combines spreadsheet-style data handling with statistical tools, publication-ready graphs, confidence intervals, project files, encrypted storage, audit trails, workflow automation, and extensible plugins. The application includes CSV, TSV, XLSX, and SQLite import and export, customizable chart styles, confidence bands, error bars, report generation, generated analysis scripts, parameter sweeps, and a documented plugin API with a starter template. TAVIA 1.0.0 Beta 1 focuses on reliability, data integrity, installer safety, secure plugin loading, reproducible results, and cross-platform desktop use. It is intended for researchers, analysts, students, and technical users who want a local, transparent alternative to fragmented analysis tools.

* GSA-SNP2 is a successor of GSA-SNP (Nam et al. 2010, NAR web server issue). GSA-SNP2 accepts human GWAS summary data (rs numbers, p-values) or gene-wise p-values and outputs pathway genesets ‘enriched’ with genes associated with the given phenotype. It also provides both local and global protein interaction networks in the associated pathways. * Article: SYoon, HCTNguyen, YJYoo, JKim, BBaik, SKim, JKim, SKim, DNam, "Efficient pathway enrichment and network analysis of GWAS summary data...

In general, Pearson correlation coefficients can be quite error-prone to outliers, thus Jackknifing may be helpful. I could not find any useful implementation for calculating more than leave-1-out Jackknife Pearson correlation coefficients. 'leave-kojack' offers an implementation for Jackknife Pearson correlation with dynamic values of k, i.e. leave-k-out Pearson correlation.

...It performs approximate Bayesian computation for stochastic models having latent dynamics defined by stochastic differential equations (SDEs) and not limited to the "state-space" modelling framework. Both one- and multi-dimensional SDE systems are supported and partially observed systems are easily accommodated. Variance components for the "measurement error" affecting the data/observations can be estimated. A 50-pages Reference Manual is provided with two case-studies implemented and discussed. The methodology is based on the research article available at http://arxiv.org/abs/1204.5459 Author's research page is http://www.maths.lth.se/matstat/staff/umberto/