Package Name | Access | Summary | Updated |
---|---|---|---|

r-tidypredict | public | It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models. | 2019-07-19 |

r-brms | public | Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. 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, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. 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 and leave-one-out cross-validation. References: B端rkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>. | 2019-07-19 |

r-dosefinding | public | The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology. | 2019-07-19 |

r-circlize | public | Circular layout is an efficient way for the visualization of huge amounts of information. Here this package provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of the package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multiple dimensional data. | 2019-07-19 |

r-cubist | public | Regression modeling using rules with added instance-based corrections. | 2019-07-19 |

r-covr | public | Track and report code coverage for your package and (optionally) upload the results to a coverage service like 'Codecov' <http://codecov.io> or 'Coveralls' <http://coveralls.io>. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code. | 2019-07-19 |

r-klar | public | Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to 'svmlight' and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing. | 2019-07-19 |

r-cvauc | public | This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively. One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods. The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package. | 2019-07-19 |

r-doby | public | Contains: 1) Facilities for working with grouped data: 'do' something to data stratified 'by' some variables. 2) LSmeans (least-squares means), general linear contrasts. 3) Miscellaneous other utilities. | 2019-07-19 |

r-bookdown | public | Output formats and utilities for authoring books and technical documents with R Markdown. | 2019-07-19 |

r-distillery | public | Some very simple method functions for confidence interval calculation, bootstrap resampling, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx. | 2019-07-19 |

r-broom.mixed | public | Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics. | 2019-07-19 |

r-ca | public | Computation and visualization of simple, multiple and joint correspondence analysis. | 2019-07-19 |

r-ebayesthresh | public | Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package. | 2019-07-19 |

r-ff | public | The ff package provides data structures that are stored on disk but behave (almost) as if they were in RAM by transparently mapping only a section (pagesize) in main memory - the effective virtual memory consumption per ff object. ff supports R''s standard atomic data types ''double'', ''logical'', ''raw'' and ''integer'' and non-standard atomic types boolean (1 bit), quad (2 bit unsigned), nibble (4 bit unsigned), byte (1 byte signed with NAs), ubyte (1 byte unsigned), short (2 byte signed with NAs), ushort (2 byte unsigned), single (4 byte float with NAs). For example ''quad'' allows efficient storage of genomic data as an ''A'',''T'',''G'',''C'' factor. The unsigned types support ''circular'' arithmetic. There is also support for close-to-atomic types ''factor'', ''ordered'', ''POSIXct'', ''Date'' and custom close-to-atomic types. ff not only has native C-support for vectors, matrices and arrays with flexible dimorder (major column-order, major row-order and generalizations for arrays). There is also a ffdf class not unlike data.frames and import/export filters for csv files. ff objects store raw data in binary flat files in native encoding, and complement this with metadata stored in R as physical and virtual attributes. ff objects have well-defined hybrid copying semantics, which gives rise to certain performance improvements through virtualization. ff objects can be stored and reopened across R sessions. ff files can be shared by multiple ff R objects (using different data en/de-coding schemes) in the same process or from multiple R processes to exploit parallelism. A wide choice of finalizer options allows to work with ''permanent'' files as well as creating/removing ''temporary'' ff files completely transparent to the user. On certain OS/Filesystem combinations, creating the ff files works without notable delay thanks to using sparse file allocation. Several access optimization techniques such as Hybrid Index Preprocessing and Virtualization are implemented to achieve good performance even with large datasets, for example virtual matrix transpose without touching a single byte on disk. Further, to reduce disk I/O, ''logicals'' and non-standard data types get stored native and compact on binary flat files i.e. logicals take up exactly 2 bits to represent TRUE, FALSE and NA. Beyond basic access functions, the ff package also provides compatibility functions that facilitate writing code for ff and ram objects and support for batch processing on ff objects (e.g. as.ram, as.ff, ffapply). ff interfaces closely with functionality from package ''bit'': chunked looping, fast bit operations and coercions between different objects that can store subscript information (''bit'', ''bitwhich'', ff ''boolean'', ri range index, hi hybrid index). This allows to work interactively with selections of large datasets and quickly modify selection criteria. Further high-performance enhancements can be made available upon request. | 2019-07-19 |

r-vim | public | New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods. | 2019-07-19 |

r-etm | public | The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 <doi:10.18637/jss.v038.i04>). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 <doi:10.1007/s00180-010-0200-x>). | 2019-07-19 |

r-filehash | public | Implements a simple key-value style database where character string keys are associated with data values that are stored on the disk. A simple interface is provided for inserting, retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be treated much like environments and lists are already used in R. These utilities are provided to encourage interactive and exploratory analysis on large datasets. Three different file formats for representing the database are currently available and new formats can easily be incorporated by third parties for use in the 'filehash' framework. | 2019-07-19 |

r-energy | public | E-statistics (energy) tests and statistics for multivariate and univariate inference, including distance correlation, one-sample, two-sample, and multi-sample tests for comparing multivariate distributions, are implemented. Measuring and testing multivariate independence based on distance correlation, partial distance correlation, multivariate goodness-of-fit tests, clustering based on energy distance, testing for multivariate normality, distance components (disco) for non-parametric analysis of structured data, and other energy statistics/methods are implemented. | 2019-07-19 |

r-geepack | public | Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses. | 2019-07-19 |

r-gamlss.dist | public | A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a ''log'' or a ''logit' transformation respectively. | 2019-07-19 |

r-english | public | Allow numbers to be presented in an English language version, one, two, three, ... Ordinals are also available, first, second, third, ... | 2019-07-19 |

r-gensa | public | Performs search for global minimum of a very complex non-linear objective function with a very large number of optima. | 2019-07-19 |

r-effects | public | Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors. | 2019-07-19 |

boto3 | public | Amazon Web Services SDK for Python | 2019-07-19 |

r-factominer | public | Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017). | 2019-07-19 |

r-fit.models | public | The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally. | 2019-07-19 |

r-fontquiver | public | Provides a set of fonts with permissive licences. This is useful when you want to avoid system fonts to make sure your outputs are reproducible. | 2019-07-19 |

r-formula.tools | public | These utilities facilitate the programmatic manipulations of formulas, expressions, calls, assignments and other R language objects. These objects all share the same structure: a left-hand side, operator and right-hand side. This packages provides methods for accessing and modifying this structures as well as extracting and replacing names and symbols from these objects. | 2019-07-19 |

r-gamlss.data | public | Data used as examples to demonstrate GAMLSS models. | 2019-07-19 |

r-itertools | public | Various tools for creating iterators, many patterned after functions in the Python itertools module, and others patterned after functions in the 'snow' package. | 2019-07-19 |

r-grpreg | public | Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. | 2019-07-19 |

r-plot3d | public | Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography. | 2019-07-19 |

r-rstanarm | public | Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors. | 2019-07-19 |

r-getpass | public | A micro-package for reading "passwords", i.e. reading user input with masking, so that the input is not displayed as it is typed. Currently we have support for 'RStudio', the command line (every OS), and any platform where 'tcltk' is present. | 2019-07-19 |

r-gss | public | A comprehensive package for structural multivariate function estimation using smoothing splines. | 2019-07-19 |

r-gsw | public | Provides an interface to the Gibbs SeaWater (TEOS-10) C library, which derives from Matlab and other code written by WG127 (Working Group 127) of SCOR/IAPSO (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans). | 2019-07-18 |

r-iso | public | Linear order and unimodal order (univariate) isotonic regression; bivariate isotonic regression with linear order on both variables. | 2019-07-18 |

r-gistr | public | Work with ''GitHub'' ''gists'' from ''R'' (e.g., <http://en.wikipedia.org/wiki/GitHub#Gist>, <https://help.github.com/articles/about-gists/>). A ''gist'' is simply one or more files with code/text/images/etc. This package allows the user to create new ''gists'', update ''gists'' with new files, rename files, delete files, get and delete ''gists'', star and ''un-star'' ''gists'', fork ''gists'', open a ''gist'' in your default browser, get embed code for a ''gist'', list ''gist'' ''commits'', and get rate limit information when ''authenticated''. Some requests require authentication and some do not. | 2019-07-18 |

r-ggstance | public | A 'ggplot2' extension that provides flipped components: horizontal versions of 'Stats' and 'Geoms', and vertical versions of 'Positions'. | 2019-07-18 |

r-ggfittext | public | Provides 'ggplot2' geoms to fit text into a box by growing, shrinking or wrapping the text. | 2019-07-18 |

r-ggthemes | public | Some extra themes, geoms, and scales for 'ggplot2'. Provides 'ggplot2' themes and scales that replicate the look of plots by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata', 'Excel', and 'The Wall Street Journal', among others. Provides 'geoms' for Tufte's box plot and range frame. | 2019-07-18 |

r-getopt | public | Package designed to be used with Rscript to write ``#!'' shebang scripts that accept short and long flags/options. Many users will prefer using instead the packages optparse or argparse which add extra features like automatically generated help option and usage, support for default values, positional argument support, etc. | 2019-07-18 |

r-gridgraphics | public | Functions to convert a page of plots drawn with the 'graphics' package into identical output drawn with the 'grid' package. The result looks like the original 'graphics'-based plot, but consists of 'grid' grobs and viewports that can then be manipulated with 'grid' functions (e.g., edit grobs and revisit viewports). | 2019-07-18 |

r-hoardr | public | Suite of tools for managing cached files, targeting use in other R packages. Uses 'rappdirs' for cross-platform paths. Provides utilities to manage cache directories, including targeting files by path or by key; cached directories can be compressed and uncompressed easily to save disk space. | 2019-07-18 |

r-proj4 | public | A simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. It allows transformation of geographic coordinates from one projection and/or datum to another. | 2019-07-18 |

pandas | public | High-performance, easy-to-use data structures and data analysis tools. | 2019-07-18 |

r-lsei | public | It contains functions that solve least squares linear regression problems under linear equality/inequality constraints. Functions for solving quadratic programming problems are also available, which transform such problems into least squares ones first. It is developed based on the 'Fortran' program of Lawson and Hanson (1974, 1995), which is public domain and available at <http | 2019-07-18 |

r-mice | public | Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations. | 2019-07-18 |

r-mba | public | Functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by Øyvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>). | 2019-07-18 |

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