Related papers: RWebData: A High-Level Interface to the Programmab…
Reliable parameter extraction from experimental data is central to quantitative analysis in spectroscopy, diffraction, photoluminescence, chromatography, microscopy, and time-resolved measurements. We present FitED, a Python-based desktop…
Over the last few years, with the growth of time-series collecting and storing, there has been a great demand for tools and software for temporal data engineering and modeling. This paper presents a generic workflow for time series data…
Meta-analysis methods are used to combine evidence from multiple studies. Meta-regression as well as model-based meta-analysis are extensions of standard pairwise meta-analysis in which information about study-level covariates and…
This study introduces a novel methodology for mapping scientific communities at scale, addressing challenges associated with network analysis in large bibliometric datasets. By leveraging enriched publication metadata from the French…
Summary: R and Matlab are two high-level scientific programming languages which are frequently applied in computational biology. To extend the wide variety of available and approved implementations, we present the Rcall interface which runs…
Reproducible document standards, like R Markdown, facilitate the programmatic creation of documents whose content is itself programmatically generated. While these documents are generally not complete in the sense that they will not include…
Mainstream knowledge management researchers generally agree that knowledge extracted from unstructured data and semi-structured data have become imperative for organizational strategic decision making. In this research, we develop a…
Pioneering data profiling systems such as Metanome and OpenClean brought public attention to science-intensive data profiling. This type of profiling aims to extract complex patterns (primitives) such as functional dependencies, data…
We look at common problems found in data that is used for predictive modeling tasks, and describe how to address them with the vtreat R package. vtreat prepares real-world data for predictive modeling in a reproducible and statistically…
Motivation: Peptides have attracted the attention in this century due to their remarkable therapeutic properties. Computational tools are being developed to take advantage of existing information, encapsulating knowledge and making it…
Data visualisation assists domain experts in understanding their data and helps them make critical decisions. Enhancing their cognitive insight essentially relies on the capability of combining domain-specific semantic information with…
In this work we propose R-GPM, a parallel computing framework for graph pattern mining (GPM) through a user-defined subgraph relation. More specifically, we enable the computation of statistics of patterns through their subgraph classes,…
A graphical model is a multivariate (potentially very high dimensional) probabilistic model, which is formed by combining lower dimensional components. Inference (computation of conditional probabilities) is based on message passing…
Recent technological advances and long-term data studies provide interaction data that can be modelled through dynamic networks, i.e a sequence of different snapshots of an evolving ecological network. Most often time is the parameter along…
The matrixdist R package provides a comprehensive suite of tools for the statistical analysis of matrix distributions, including phase-type, inhomogeneous phase-type, discrete phase-type, and related multivariate distributions. This paper…
Random graph models are frequently used as a controllable and versatile data source for experimental campaigns in various research fields. Generating such data-sets at scale is a non-trivial task as it requires design decisions typically…
With much of our lives taking place online, researchers are increasingly turning to information from the World Wide Web to gain insights into geographic patterns and processes. Web scraping as an online data acquisition technique allows us…
The Topological Data Analysis Ball Mapper (TDABM) algorithm of Dlotko (2019) provides a model free means to visualize multi-dimensional data. The visualizations are abstract two-dimensional representations of covers of the dataset. To…
Existing decision-theoretic reasoning frameworks such as decision networks use simple data structures and processes. However, decisions are often made based on complex data structures, such as social networks and protein sequences, and rich…
The R package BNSP provides a unified framework for semiparametric location-scale regression and stochastic search variable selection. The statistical methodology that the package is built upon utilizes basis function expansions to…