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Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

machine-learning data-science tuning cran r-package predictive-modeling classification regression statistics r survival-analysis imbalance-correction tutorial mlr learners hyperparameters-optimization feature-selection multilabel-classification clustering stackingMetaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. For more information, see Metaflow's website and documentation.

productivity data-science machine-learning r ai reproducible-research ml rstats r-package model-management ml-infrastructure mlops ml-platformAn R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js. Moreover, since ggplotly() returns a plotly object, you can apply essentially any function from the R package on that object. Some useful ones include layout() (for customizing the layout), add_traces() (and its higher-level add_*() siblings, for example add_polygons(), for adding new traces/data), subplot() (for combining multiple plotly objects), and plotly_json() (for inspecting the underlying JSON sent to plotly.js).

webgl ggplot2 r shiny plotly data-visualization rstats r-package d3jsIf you use the RStudio IDE, you should also download the latest preview release of the IDE which includes several enhancements for interacting with Spark (see the RStudio IDE section below for more details). The returned Spark connection (sc) provides a remote dplyr data source to the Spark cluster.

r rstats apache-spark machine-learning r-package dplyr sparklyr dbiRStan is the R interface to Stan. RStan's source code repository is hosted here on GitHub. Stan's source repository is defined as a submodule. See how to work with stan submodule in rstan repo.

stan mcmc bayesian-inference bayesian-data-analysis bayesian-statistics r r-packageshinyjs lets you perform common useful JavaScript operations in Shiny apps that will greatly improve your apps without having to know any JavaScript. Examples include: hiding an element, disabling an input, resetting an input back to its original value, delaying code execution by a few seconds, and many more useful functions for both the end user and the developer. shinyjs can also be used to easily call your own custom JavaScript functions from R.

r shiny rstats r-packageThe brms package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see http://mc-stan.org/). The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models (i.e. models with multiple response variables) can be fitted, as well. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks, leave-one-out cross-validation, and Bayes factors. As a simple example, we use poisson regression to model the seizure counts in epileptic patients to investigate whether the treatment (represented by variable Trt) can reduce the seizure counts and whether the effect of the treatment varies with the baseline number of seizures a person had before treatment (variable log_Base4_c). As we have multiple observations per person, a group-level intercept is incorporated to account for the resulting dependency in the data.

brms stan bayesian-inference multilevel-models statistical-models r-packageThe targets package is a Make-like pipeline toolkit for Statistics and data science in R. With targets, you can maintain a reproducible workflow without repeating yourself. targets skips costly runtime for tasks that are already up to date, runs the necessary computation with implicit parallel computing, and abstracts files as R objects. A fully up-to-date targets pipeline is tangible evidence that the output aligns with the code and data, which substantiates trust in the results. Please note that this package is released with a Contributor Code of Conduct.

workflow data-science r pipeline reproducible-research high-performance-computing make rstats r-package reproducibility targets peer-reviewed r-targetopiaThis package provides a function datatable() to display R data via the DataTables library (N.B. not to be confused with the data.table package). See the full documentation at https://rstudio.github.io/DT. Please use Github issues only if you want to file bug reports or feature requests, and you are expected to ask questions on StackOverflow with at least the tags r and dt.

r datatables r-package htmlwidgets shinyR client for accessing Twitter’s REST and stream APIs. Check out the rtweet package documentation website. All users must be authorized to interact with Twitter’s APIs. To become authorized, follow the instructions below to (1) make a Twitter app and (2) create and save your access token (using one of the two authorization methods described below).

rtweet r-rtweet r-twitter twitter twitter-api stream-api rest-api cran tweets r-package r mkearney-r-packageFor more details on the workflow and theory underpinning naniar, read the vignette Getting started with naniar. For a short primer on the data visualisation available in naniar, read the vignette Gallery of Missing Data Visualisations.

missing-data data-visualisation ggplot2 missingness tidy-data r-packagetidybayes is an R package that aims to make it easy to integrate popular Bayesian modeling methods into a tidy data + ggplot workflow. Composing data for use with the model. This often means translating data from a data.frame into a list , making sure factors are encoded as numerical data, adding variables to store the length of indices, etc. This package helps automate these operations using the compose_data function, which automatically handles data types like numeric, logical, factor, and ordinal, and allows easy extensions for converting other datatypes into a format the model understands by providing your own implementation of the generic as_data_list.

r tidy-data bayesian-data-analysis r-package visualization ggplot2 stan jagsThis package provides R with access to Boost header files. Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking.This package aims to provide the most useful subset of Boost libraries for template use among CRAN packages. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided.

r c-plus-plus boost rcpp r-packageThe digest package provides a principal function digest() for the creation of hash digests of arbitrary R objects (using the md5, sha-1, sha-256, crc32, xxhash and murmurhash algorithms) permitting easy comparison of R language objects.By using the hash sum, which is very likely to be unique, to identify an underlying object or calculation, one can easily caching strategies for which the digest package is somewhat widely used.

r cran r-package hash-digestThe R package ecosystem is one of the cornerstones of the success seen by R. As of early 2016, almost 8000 packages are on CRAN, with about one thousand more at BioConductor and probably another hundred at OmegaHat.Support for multiple repositories is built deeply into R; mostly via the (default) package utils. The update.packages function (along with several others from the utils package) can be used with ease for these three default repositories as well as many others. But it seemed that support for simple creation and use of local repositories was missing.

r cran repository repository-tools r-packageA scripting and command-line front-end for GNU R permitting use of R in command-line contexts.See the examples vignette for a full set of introductory examples. Also see the examples/ directory, as well as maybe the older tests directory both of which are installed with the package.

r r-package cran embedded examples qt mpiR has excellent tools for dates and times. The Date and POSIXct classes (as well as the 'wide' representation in POSIXlt) are versatile, and a lot of useful tooling has been built around them.However, POSIXct is implemented as a double with fractional seconds since the epoch. Given the 53 bits accuracy, it leaves just a bit less than microsecond resolution. Which is good enough for most things.

nanoseconds nanosecond-resolution datetime datetimes r-package r cranThe base R function package.skeleton() is very useful for creating new packages for R. It is also very upsetting as it has been producing the same files which upset R CMD check in the exact same way.And as something terrible happens each time R CMD check barks, this package offers a wrapper function kitten() which leaves an adorable little package behind which does not upset R CMD check.

r cran r-package skeletonThis package provides C-level date / datetime functionality taken from the R sources and made available for use by other packages.It is useful if you are writing C (or C++) code in an R package which needs to parse, format or transform date(time) objects, and want to do this at the compiled level, i.e. faster than calling back to the corresponding R level function could do it.

r-package r date date-timeThis package uses the cnpy library written by Carl Rogers to provide read and write facilities for files created with (or for) the NumPy extension for Python. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with -std=c++11 which is the default starting with release 0.2.3 following the release of R 3.1.0, and available on all platforms following the release of R 3.3.0 with the updated 'Rtools'.saves two matrices in floating-point and integer representation.

r cran r-package numpy c-plus-plus
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