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r-gamlss.dist
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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.
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2025-03-25 |
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r-gamlss.data
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Data used as examples to demonstrate GAMLSS models.
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2025-03-25 |
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r-webshot
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Takes screenshots of web pages, including Shiny applications and R Markdown documents.
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2025-03-25 |
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r-ggpubr
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The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.
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2025-03-25 |
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r-ggsignif
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Enrich your 'ggplots' with group-wise comparisons. This package provides an easy way to indicate if two groups are significantly different. Commonly this is shown by a bracket on top connecting the groups of interest which itself is annotated with the level of significance (NS, *, **, ***). The package provides a single layer (geom_signif()) that takes the groups for comparison and the test (t.test(), wilcox.text() etc.) as arguments and adds the annotation to the plot.
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2025-03-25 |
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r-ggsci
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A collection of 'ggplot2' color palettes inspired by plots in scientific journals, data visualization libraries, science fiction movies, and TV shows.
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2025-03-25 |
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r-ggrepel
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Provides text and label geoms for 'ggplot2' that help to avoid overlapping text labels. Labels repel away from each other and away from the data points.
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2025-03-25 |
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r-gridextra
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Provides a number of user-level functions to work with "grid" graphics, notably to arrange multiple grid-based plots on a page, and draw tables.
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2025-03-25 |
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r-cowplot
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Some helpful extensions and modifications to the 'ggplot2' package. In particular, this package makes it easy to combine multiple 'ggplot2' plots into one and label them with letters, e.g. A, B, C, etc., as is often required for scientific publications. The package also provides a streamlined and clean theme that is used in the Wilke lab, hence the package name, which stands for Claus O. Wilke's plot package.
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2025-03-25 |
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r-fields
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For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research. See the Fields URL for a vignette on using this package and some background on spatial statistics.
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2025-03-25 |
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r-spam
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Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64).
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2025-03-25 |
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r-dotcall64
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Provides .C64(), which is an enhanced version of .C() and .Fortran() from the foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code.
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2025-03-25 |
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r-maps
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Display of maps. Projection code and larger maps are in separate packages ('mapproj' and 'mapdata').
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2025-03-25 |
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r-rminer
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Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.2 new NMAE metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models/algorithms, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.0 - first version.
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2025-03-25 |
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r-party
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A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331> and Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
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2025-03-25 |
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r-plotrix
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Lots of plots, various labeling, axis and color scaling functions.
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2025-03-25 |
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r-pls
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Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
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2025-03-25 |
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r-cubist
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Regression modeling using rules with added instance-based corrections.
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2025-03-25 |
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r-rpart.plot
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Plot 'rpart' models. Extends plot.rpart() and text.rpart() in the 'rpart' package.
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2025-03-25 |
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r-kknn
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Weighted k-Nearest Neighbors for Classification, Regression and Clustering.
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2025-03-25 |
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r-islr
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We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
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2025-03-25 |
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r-hdtweedie
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This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.
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2025-03-25 |
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r-adabag
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It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. Since version 2.0 the function margins() is available to calculate the margins for these classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as a function of the number of iterations. In addition, the ensembles can be pruned using the option 'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies the relative importance measure to take into account the gain of the Gini index given by a variable in each tree and the weights of these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on unlabeled data. Version 4.2 includes the parallel computation option for some of the functions.
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2025-03-25 |
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r-mda
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Mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, ...
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2025-03-25 |
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r-tabplot
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A tableplot is a visualisation of a (large) dataset with a dozen of variables, both numeric and categorical. Each column represents a variable and each row bin is an aggregate of a certain number of records. Numeric variables are visualized as bar charts, and categorical variables as stacked bar charts. Missing values are taken into account. Also supports large 'ffdf' datasets from the 'ff' package.
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2025-03-25 |
