Related papers: Rbox: an integrated R package for ATOM Editor
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most existing datasets only evaluate limited application domains. To…
R is a language and computing environment that has been developed for data manipulation, statistical computing, and scientific graphing. In the paper, we demonstrate its use analyzing data collected in a few experiments taken from an…
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural…
One of the most powerful features of R is its infrastructure for contributed code. The built-in package manager and complementary repositories provide a great system for development and exchange of code, and have played an important role in…
Nowadays, the analysis of dynamics in networks represents a great deal in the Social Network Analysis research area. To support students, teachers, developers, and researchers in this work we introduce a novel R package, namely DynComm. It…
Quantum computing is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially overcome the limits of classical computation. Over the last years, all major companies have provided…
The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing…
The advancement of scientific knowledge increasingly depends on ensuring that data-driven research is reproducible: that two people with the same data obtain the same results. However, while the necessity of reproducibility is clear, there…
Large language models are increasingly used for qualitative data analysis, but many workflows obscure how analytic conclusions are produced. We present QualAnalyzer, an open-source Chrome extension for Google Workspace that supports…
The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is…
AI-Driven Development Environments (AIDEs) Integrate the power of modern AI into IDEs like Visual Studio Code and JetBrains IntelliJ. By leveraging massive language models and the plethora of openly available source code, AIDEs promise to…
MLPACK is a state-of-the-art, scalable, multi-platform C++ machine learning library released in late 2011 offering both a simple, consistent API accessible to novice users and high performance and flexibility to expert users by leveraging…
Internet Message Access Protocol (IMAP) clients are a common feature in several programming languages. Despite having some packages for electronic messages retrieval, the R language, until recently, lacked a broader solution, capable of…
Block-based environments are visual programming environments, which are becoming more and more popular because of their ease of use. The ease of use comes thanks to their intuitive graphical representation and structural metaphors…
Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has been imputed by an artificial intelligence or machine…
Open-ended assignments - such as lab reports and semester-long projects - provide data science and statistics students with opportunities for developing communication, critical thinking, and creativity skills. However, providing grades and…
This article describes SimEngine, an open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments. Several R packages exist for structuring…
As quantum computing evolves from theoretical promise to practical deployment, the demand for robust, portable, and scalable tools for quantum software experimentation is growing. This paper introduces Quantum Executor, a backend-agnostic…
The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need methods for exploration, debugging, and validation. New methods…
Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics Challenges to the first humanoid-robot kickboxing tournament-yet commercial autonomy still lags behind progress in machine learning. A major bottleneck is…