Search Results for "time"

...This version is designed for students following the 2024 lecture series, offering the most current set of examples, exercises, and teaching material aligned with the Statistical Rethinking framework. Online, flipped instruction. I will pre-record the lectures each week. We'll meet online once a week for an hour to discuss the material. The discussion time (3-4pm Berlin Time) should allow people in the Americas to join in their morning.

Shiny is an R package from RStudio that enables users to build interactive web applications using R without requiring knowledge of JavaScript, HTML, or CSS. It allows statisticians and data scientists to turn their analyses into fully functional web dashboards with reactive elements, data inputs, visualizations, and controls, making data communication more effective and dynamic.

renv is an R dependency management toolkit that enables project-level library isolation and reproducibility. It tracks package versions in a lockfile and can restore exact library states across machines or over time, making R projects portable and consistent.

...The project includes scripts and notebooks for cleaning and filtering the raw data, memory-efficient processing for large CSV/Parquet files, and aggregation workflows (e.g. trips per hour, heatmaps of pickups/dropoffs). It also contains example analyses—spatial and temporal visualizations like maps, time-series plots, and hotspot detection—highlighting insights such as patterns of demand, peak times, and geospatial distributions. The repository is often used as a benchmark dataset and example for teaching, benchmarking, and demonstration purposes in the data science and urban analytics communities.

...It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information.

...The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.

...The key insight illustrated is that over sufficiently long holding periods (e.g. 40 years), the stock market stabilizes and nearly always yields positive returns, even accounting for extreme market crashes and recessions. The visualizations show “return curves” for different starting years and durations, and also illustrate the probability of losses over various time horizons. The project is centered on transparency in finance and encourages users to examine the data themselves; the code is shared in R and uses ggplot2 for plotting.