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r-varimp
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Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).
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2025-03-25 |
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r-mediation
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We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
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2025-03-25 |
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r-dotwhisker
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Quick and easy dot-and-whisker plots of regression results.
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2025-03-25 |
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r-d3heatmap
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Create interactive heat maps that are usable from the R console, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover the mouse pointer over a cell to show details, drag a rectangle to zoom, and click row/column labels to highlight.
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2025-03-25 |
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r-traminer
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public |
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-rms
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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-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-xergm.common
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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-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-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-lava.tobit
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public |
Lava plugin allowing combinations of left and right censored and binary outcomes.
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2025-03-25 |
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r-colourpicker
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A colour picker that can be used as an input in Shiny apps or Rmarkdown documents. The colour picker supports alpha opacity, custom colour palettes, and many more options. A Plot Colour Helper tool is available as an RStudio Addin, which helps you pick colours to use in your plots. A more generic Colour Picker RStudio Addin is also provided to let you select colours to use in your R code.
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2025-03-25 |
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r-sem
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Functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares.
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2025-03-25 |
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r-pdp
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public |
A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
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2025-03-25 |
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r-latentnet
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public |
Fit and simulate latent position and cluster models for statistical networks.
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2025-03-25 |
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r-ipumsr
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An easy way to import census, survey and geographic data provided by 'IPUMS' into R plus tools to help use the associated metadata to make analysis easier. 'IPUMS' data describing 1.4 billion individuals drawn from over 750 censuses and surveys is available free of charge from our website <https://ipums.org>.
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2025-03-25 |
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r-ggraph
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The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. ggraph is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer.
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2025-03-25 |
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r-ergm.userterms
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A template package to demonstrate the use of user-specified statistics for use in 'ergm' models as part of the Statnet suite of packages.
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2025-03-25 |
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r-biclust
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The main function biclust() provides several algorithms to find biclusters in two-dimensional data: Cheng and Church (2000, ISBN:1-57735-115-0), spectral (2003) <doi:10.1101/gr.648603>, plaid model (2005) <doi:10.1016/j.csda.2004.02.003>, xmotifs (2003) <doi:10.1142/9789812776303_0008> and bimax (2006) <doi:10.1093/bioinformatics/btl060>. In addition, the package provides methods for data preprocessing (normalization and discretisation), visualisation, and validation of bicluster solutions.
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2025-03-25 |
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r-wakefield
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public |
Generates random data sets including: data.frames, lists, and vectors.
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2025-03-25 |
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r-tram
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Formula-based user-interfaces to specific transformation models implemented in package 'mlt'. Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
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2025-03-25 |
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r-styler
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Pretty-prints R code without changing the user's formatting intent.
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2025-03-25 |
