Search Results for "egm2008-1"
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Showing 18 open source projects for "egm2008-1"
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Gen AI apps are built with MongoDB AtlasMongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
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Junie, the AI coding agent by JetBrainsJunie is an AI-powered coding agent developed by JetBrains designed to enhance developer productivity by integrating directly into popular IDEs such as IntelliJ IDEA, PyCharm, and Android Studio. It supports developers by assisting with code completion, testing, and inspections, ensuring code quality and reducing debugging time.
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RStudio Cheatsheets
Curated collection of official cheat sheets for data science tools
The cheatsheets repository from RStudio is a curated collection of official cheat sheets for R, RStudio, the tidyverse, Shiny, and related data science tools. Each cheat sheet is a single (or double) page PDF that condenses important syntax, functions, workflows, and best practices into a visually organized format ideal for quick reference. The repository contains source files (R Markdown or LaTeX) that generate the cheat sheets, version history, and metadata (title, author, description) for... -
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ggstatsplot
Enhancing {ggplot2} plots with statistical analysis
...In a typical exploratory data analysis workflow, data visualization and statistical modeling are two different phases: visualization informs modeling, and modeling in its turn can suggest a different visualization method, and so on and so forth. Bayesian hypothesis-testing. The central idea of {ggstatsplot} is simple: combine these two phases into one in the form of graphics with statistical details, which makes data exploration simpler and faster. Summary of statistical tests and effect sizes. -
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Huxtable
An R package to create styled tables in multiple output formats
Huxtable is an R package to create LaTeX and HTML tables, with a friendly, modern interface. Features include control over text styling, number format, background color, borders, padding, and alignment. Cells can span multiple rows and/or columns. Tables can be manipulated with standard R subsetting or dplyr functions. -
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CausalImpact
An R package for causal inference in time series
...Its goal is to estimate the effect of an intervention (e.g. a marketing campaign, policy change) on a time series outcome by predicting what would have happened in a counterfactual “no intervention” world. The package requires as input a response time series plus one or more control (covariate) time series that are assumed unaffected by the intervention, and it divides the time horizon into “pre-intervention” and “post-intervention” periods. It uses Bayesian modeling to fit a structural time series to the pre-period and extrapolate a counterfactual prediction for the post period, then compares observed vs predicted to infer the causal effect. ... -
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plotly
An interactive graphing library for R
...Moreover, both approaches are an implementation of the Grammar of Graphics and both are powered by the JavaScript graphing library plotly.js, so many of the same concepts and tools that you learn for one interface can be reused in the other. Any graph made with the plotly R package is powered by the JavaScript library plotly.js. The plot_ly() function provides a ‘direct’ interface to plotly.js with some additional abstractions to help reduce typing. -
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brms
brms R package for Bayesian generalized multivariate models using Stan
brms is an R package by Paul Bürkner which provides a high-level interface for fitting Bayesian multilevel (i.e. mixed effects) models, generalized linear / non-linear / multivariate models using Stan as the backend. It allows R users to specify complex Bayesian models using formula syntax similar to lme4 but with far more flexibility (distributions, link functions, hierarchical structure, nonlinear terms, etc.). It supports model diagnostics, posterior predictive checking, model comparison,... -
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meditator
Miscellaneous tools for meditation
Miscellaneous tools for meditation and mental health. Optionally a very basic R package for meditation and mental health. Please close your eyes and take one deep breath. Feel how long the in-breath is. Feel how long the out-breath is. -
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Statistics for Data Scientists
"Statistics for Data Scientists: 50 Essential Concepts"
...It aims to demystify the bridge between textbook statistics and empirical modeling by walking through assumption checking, visualization, interpreting outputs, and pitfalls of misuse. Throughout, the content emphasizes clarity and accessibility, showing not just how to run statistical tests or build models, but what they mean and when one method is preferred over another. -
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QuantifyPoly(A)
Quantification of poly(A) sites from 3' end sequencing data
QuantifyPoly(A) - a tool for quantification of poly(A) sites from 3' end sequencing data. [1] QuantifyPoly(A) user manual Please visit the Wiki page of this website. [2] QuantifyPoly(A) Q&A For Q&A, please visit the Blog page of this website. [3] QuantifyPoly(A) bug report You can report a bug as a Ticket request, or start a topic session in the Discussion webpage of this website. -
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R4DS (R for Data Science)
R for data science: a book
“R for Data Science” (r4ds) is the source material (book + examples) by Hadley Wickham et al., intended to teach data science using R and the tidyverse. It covers the workflow from importing data, tidying, transforming, visualizing, modelling, communicating results, and programming in R. The repository contains the source files (Quarto / RMarkdown), example datasets, visualizations, exercises, and all content needed to build the book. Includes many example datasets, diagrams, code samples,... -
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Reproducible-research
A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.
...It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. 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. -
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posterdown
Use RMarkdown to generate PDF Conference Posters via HTML
...This is my attempt to provide a semi-smooth workflow for those who wish to take their RMarkdown skills to the conference world. Many creature comforts from RMarkdown are available in this package such as Markdown section notation, figure captioning, and even citations like this one (Allaire, Xie, McPherson, et al. 2018). The rest of this example poster will show how you can insert typical conference poster features into your own document. Posterdown was created as a proof-of-concept (to myself) that it is possible to make a beautiful poster using open-source reproducible code. -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. ... -
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Open Intro Statistics
An open-source textbook written at the college level
OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. Each chapter's content is in one of the eight chapter folders that start with "ch_". Within each folder, there is a "figures" folder and a "TeX" folder. The TeX folder contains the text files that are used to typeset the chapters in the textbook. In many cases, R code is supplied with figures to regenerate the figure. It will often be necessary to install the "openintro" R package that is available from GitHub (https://github.com/OpenIntroOrg) if you would like to regenerate a figure. ... -
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bookdownplus
Use R package bookdown for writing varied types of books and documents
...It would be a pity if users stopped their steps at the door and gave up the courage to enter the wonderful world of bookdown. bookdownplus is the easiest shortcut to the world of bookdown. With just one single command users can get a demo book (or multiple demo books) in .pdf or .doc, or even more formats. bookdownplus extends the features of bookdown, and simplifies the procedure. -
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Wes Anderson Palettes
A Wes Anderson color palette for R
Tired of generic mass produced palettes for your plots? Short of adding an owl and dressing up your plot in a bowler hat, here’s the most indie thing you can do to one. The first round of palettes derived from the amazing Tumblr blog Wes Anderson Palettes. -
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ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
...It builds on a worked example that caches the mean of a numeric vector, demonstrating how the operator preserves state across function calls. You then implement analogous logic for matrices via two functions, one to construct the cache-aware object and another to compute or retrieve the cached inverse. The instructions emphasize using solve for inversion and assuming that the supplied matrix is always invertible. The repository outlines the workflow for forking, editing the provided R stub, committing your solution, and submitting your repository URL as the final deliverable. -
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ExData Plotting1
Plotting Assignment 1 for Exploratory Data Analysis
This repository explores household energy usage over time using the “Individual household electric power consumption” dataset from the UC Irvine Machine Learning Repository. The dataset covers nearly four years of minute-level measurements, including power consumption, voltage, current intensity, and detailed sub-metering values for different household areas. For analysis, focus is placed on a two-day period in February 2007, highlighting short-term consumption trends. The data requires...
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