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r-traminer
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Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
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2025-03-25 |
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r-tnam
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Temporal and cross-sectional network autocorrelation models. These are models for variation in attributes of nodes nested in a network (e.g., drinking behavior of adolescents nested in a school class, or democracy versus autocracy of countries nested in the network of international relations). These models can be estimated for cross-sectional data or panel data, with complex network dependencies as predictors, multiple networks and covariates, arbitrary outcome distributions, and random effects or time trends. Basic references: Doreian, Teuter and Wang (1984) <doi:10.1177/0049124184013002001>; Hays, Kachi and Franzese (2010) <doi:10.1016/j.stamet.2009.11.005>; Leenders, Roger Th. A. J. (2002) <doi:10.1016/S0378-8733(01)00049-1>.
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2025-03-25 |
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r-seriation
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public |
Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT).
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2025-03-25 |
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r-rms
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public |
Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. 'rms' works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.
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2025-03-25 |
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r-mice
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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.
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2025-03-25 |
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r-imputets
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Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'.
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2025-03-25 |
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r-greybox
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Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.
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2025-03-25 |
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r-caret
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Misc functions for training and plotting classification and regression models.
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2025-03-25 |
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r-xergm.common
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public |
Datasets and definitions of generic functions used in dependencies of the 'xergm' package.
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2025-03-25 |
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r-visdat
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Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.
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2025-03-25 |
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r-unpivotr
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public |
Tools for converting data from complex or irregular layouts to a columnar structure. For example, tables with multilevel column or row headers, or spreadsheets. Header and data cells are selected by their contents and position, as well as formatting and comments where available, and are associated with one other by their proximity in given directions. Functions for data frames and HTML tables are provided.
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2025-03-25 |
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r-tsna
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Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
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2025-03-25 |
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r-threejs
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public |
Create interactive 3D scatter plots, network plots, and globes using the 'three.js' visualization library (<https://threejs.org>).
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2025-03-25 |
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r-syuzhet
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public |
Extracts sentiment and sentiment-derived plot arcs from text using a variety of sentiment dictionaries conveniently packaged for consumption by R users. Implemented dictionaries include "syuzhet" (default) developed in the Nebraska Literary Lab "afinn" developed by Finn {\AA}rup Nielsen, "bing" developed by Minqing Hu and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the "get_sentiment" function. The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization.
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2025-03-25 |
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r-skimr
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A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the "Using skimr" vignette and the README.
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2025-03-25 |
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r-sjlabelled
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Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.
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2025-03-25 |
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r-shinydashboardplus
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Extend 'shinydashboard' with 'AdminLTE2' components. 'AdminLTE2' is a free 'Bootstrap 3' dashboard template available at <https://adminlte.io>. Customize boxes, add timelines and a lot more.
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2025-03-25 |
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r-seasonalview
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A graphical user interface to the 'seasonal' package and 'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. Unifies the code base of <http://www.seasonal.website> and the GUI in the 'seasonal' package.
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2025-03-25 |
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r-rsample
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Classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).
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2025-03-25 |
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r-rio
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public |
Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data structure from the file extension, reasonable defaults are used for data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly without explicit decompression, and fast import packages are used where appropriate. An additional convenience function, 'convert()', provides a simple method for converting between file types.
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2025-03-25 |
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r-recipes
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An extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.
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2025-03-25 |
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r-rattle
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The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. A Gnome (RGtk2) based graphical interface is included with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file (or via ODBC), transform and explore the data, build and evaluate models, and export models as PMML (predictive modelling markup language) or as scores. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself.
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2025-03-25 |
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r-pool
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Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are 'DBI' connections.
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2025-03-25 |
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r-networkdynamicdata
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A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
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2025-03-25 |
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r-mitml
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Provides tools for multiple imputation of missing data in multilevel modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo', and several functions for visualization, data management and the analysis of multiply imputed data sets.
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2025-03-25 |
