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Showing 24 open source projects for "r-map"
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JuliaConnectoR
A functionally oriented interface for calling Julia from R
This R-package provides a functionally oriented interface between R and Julia. The goal is to call functions from Julia packages directly as R functions. Julia functions imported via the JuliaConnectoR can accept and return R variables. It is also possible to pass R functions as arguments in place of Julia functions, which allows callbacks from Julia to R. -
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RCall.jl
Call R from Julia
...However, Julia still lacks the depth and scale of the R package environment. This package, RCall.jl, facilitates communication between these two languages and allows the user to call R packages from within Julia, providing the best of both worlds. Additionally, this is a pure Julia package so it is portable and easy to use. -
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Tidier.jl
Meta-package for data analysis in Julia, modeled after the R tidyverse
...It allows users to write expressive and concise data transformation code using chaining (|>) and intuitive syntax. Built on top of DataFrames.jl, Tidier.jl aims to make data wrangling more accessible to users familiar with R or looking for cleaner data pipelines in Julia. -
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FileTrees.jl
Parallel file processing made easy
...Files and subtrees in a file tree can have any value attached to them, you can map and reduce over these values, or combine them by merging or collapsing trees or subtrees. When computing lazy trees, these values are held in distributed memory and operated on in parallel. -
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BetaML.jl
Beta Machine Learning Toolkit
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its implementation. ... -
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ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
...While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy. Functions like f which map a vector to a scalar are the best case for reverse-mode automatic differentiation, but ForwardDiff may still be a good choice if x is not too large, as it is much simpler. The best case for forward-mode differentiation is a function that maps a scalar to a vector. -
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Rocket.jl
Functional reactive programming extensions library for Julia
Rocket.jl is a Julia package for reactive programming using Observables, to make it easier to work with asynchronous data. Rocket.jl has been designed with a focus on performance and modularity. -
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Sundials.jl
Julia interface to Sundials, including a nonlinear solver
This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. -
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CounterfactualExplanations.jl
A package for Counterfactual Explanations and Algorithmic Recourse
...While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs. -
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DataFramesMeta.jl
Metaprogramming tools for DataFrames
...DataFramesMeta.jl provides the macros @select, @transform, @combine, @select!, and @transform! to mirror these functions with more convenient syntax. Inspired by dplyr in R and LINQ in C#. -
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
...Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing. Libraries from Python, R, C/Fortran, C++, and Java can also be used. -
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DataFrames.jl
In-memory tabular data in Julia
...It provides a familiar, flexible, and efficient interface for handling datasets, making it easy to load, manipulate, join, and analyze structured data. With syntax inspired by data frames in R and pandas in Python, it offers intuitive tools while taking advantage of Julia’s speed and type system. The package is actively maintained by the JuliaData community, with contributions from over 200 developers worldwide. It is widely used for data science, research, and production applications, supported by extensive documentation, tutorials, and a free Julia Academy course. ... -
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JDF.jl
Julia DataFrames serialization format
...JDF.jl is a pure-Julia solution and there are a lot of ways to do nifty things like compression and encapsulating the underlying struture of the arrays that's hard to do in R and Python. E.g. Python's numpy arrays are C objects, but all the vector types used in JDF are Julia data types. -
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CxxWrap
Package to make C++ libraries available in Julia
This package aims to provide a Boost. Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there. The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library. This dynamic library is then loaded into Julia, where the Julia part of this package uses the data provided through a... -
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LabPlot
Data Visualization and Analysis
LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone. -
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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
...Given certain structural properties, PCs enable different range of tractable exact probabilistic queries such as computing marginals, conditionals, maximum a posteriori (MAP), and more advanced probabilistic queries. -
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Rotations.jl
Julia implementations for different rotation parameterizations
...While the RotMatrix type is a dense representation of a 3×3 matrix, we also have sparse (or computed, rather) representations such as quaternions, angle-axis parameterizations, and Euler angles. All rotation types support one(R) to construct the identity rotation for the desired parameterization. -
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Transducers.jl
Efficient transducers for Julia
Transducers are transformations of "sequence" of input that can be composed very efficiently. The interface used by transducers naturally describes a wide range of processes that is expressible as a succession of steps. Furthermore, transducers can be defined without specifying the details of the input and output (collections, streams, channels, etc.) and therefore achieves a full reusability. Transducers are introduced by Rich Hickey, the creator of the Clojure language. His Strange Loop... -
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Weave
Scientific reports/literate programming for Julia
Weave is a scientific report generator/literate programming tool for the Julia programming language. It resembles Pweave, knitr, R Markdown, and Sweave. You can write your documentation and code in an input document using Markdown, Noweb or ordinal Julia script syntax, and then use the weave function to execute code and generate an output document while capturing results and figures. Supports various output document formats: HTML, PDF, GitHub markdown, Jupyter Notebook, MultiMarkdown, Asciidoc and reStructuredText. -
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Indicators.jl
Financial market technical analysis & indicators in Julia
Indicators is a Julia package offering efficient implementations of many technical analysis indicators and algorithms. This work is inspired by the TTR package in R and the Python implementation of TA-Lib, and the ultimate goal is to implement all of the functionality of these offerings (and more) in Julia. This package has been written to be able to interface with both native Julia Array types, as well as the TS time series type from the Temporal package. Contributions are of course always welcome for wrapping any of these functions in methods for other types and/or packages out there, as are suggestions for other indicators to add to the lists. -
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JuliaCall for Seamless Integration of R
Embed Julia in R
Package JuliaCall is an R interface to Julia, which is a high-level, high-performance dynamic programming language for numerical computing. Below is an image for Mandelbrot set. JuliaCall brings more than 100 times speedup of the calculation. -
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Gadfly
Crafty statistical graphics for Julia
Gadfly is a system for plotting and visualization written in Julia. It is based largely on Hadley Wickhams's ggplot2 for R and Leland Wilkinson's book The Grammar of Graphics. It was Daniel C. Jones' brainchild and is now maintained by the community. -
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Strategems
Quantitative systematic trading strategy development and backtesting
Strategems is a Julia package aimed at simplifying and streamlining the process of developing, testing, and optimizing algorithmic/systematic trading strategies. This package is inspired in large part by the quantstrat1,2 package in R, adopting a similar general structure to the building blocks that make up a strategy. Given the highly iterative nature of event-driven trading strategy development, Julia's high-performance design (particularly in the context of loops) and straightforward syntax would seem to make it a natural fit as a language for systematic strategy research and development. ... -
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BERT
Connector for Excel and the programming languages R and Julia
...It really could not be any easier. BERT also has a console that you can use to control Excel in real time, right from your R code. And (if you want), you can call R functions from VBA as well.
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