|
r-maxlik
|
public |
Functions for Maximum Likelihood (ML) estimation and non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the ML viewpoint. It also includes a number of convenience tools for testing and developing your own models.
|
2025-03-25 |
|
r-mailr
|
public |
Interface to Apache Commons Email to send emails from R.
|
2025-03-25 |
|
r-loa
|
public |
Various plots and functions that make use of the lattice/trellis plotting framework. The plots, which include loaPlot(), GoogleMap() and trianglePlot(), use panelPal(), a function that extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel and panel-to-key synchronization/management.
|
2025-03-25 |
|
r-lava
|
public |
A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2019) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
|
2025-03-25 |
|
r-irdisplay
|
public |
An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>. Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.
|
2025-03-25 |
|
r-htmltable
|
public |
Tables with state-of-the-art layout elements such as row spanners, column spanners, table spanners, zebra striping, and more. While allowing advanced layout, the underlying css-structure is simple in order to maximize compatibility with word processors such as 'MS Word' or 'LibreOffice'. The package also contains a few text formatting functions that help outputting text compatible with HTML/LaTeX.
|
2025-03-25 |
|
r-grpregoverlap
|
public |
Fit the regularization path of linear, logistic or Cox models with overlapping grouped covariates based on the latent group lasso approach. Latent group MCP/SCAD as well as bi-level selection methods, namely the group exponential lasso and the composite MCP are also available. This package serves as an extension of R package 'grpreg' (by Dr. Patrick Breheny <[email protected]>) for grouped variable selection involving overlaps between groups.
|
2025-03-25 |
|
r-gmm
|
public |
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
|
2025-03-25 |
|
r-glmnetutils
|
public |
Provides a formula interface for the 'glmnet' package for elasticnet regression, a method for cross-validating the alpha parameter, and other quality-of-life tools.
|
2025-03-25 |
|
r-ggm
|
public |
Functions and datasets for maximum likelihood fitting of some classes of graphical Markov models.
|
2025-03-25 |
|
r-furrr
|
public |
Implementations of the family of map() functions from 'purrr' that can be resolved using any 'future'-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.
|
2025-03-25 |
|
r-fitdistrplus
|
public |
Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available. See e.g. Casella & Berger (2002). Statistical inference. Pacific Grove.
|
2025-03-25 |
|
r-ecfun
|
public |
Functions to update data sets in Ecdat and to create, manipulate, plot and analyze those and similar data sets.
|
2025-03-25 |
|
r-dygraphs
|
public |
An R interface to the 'dygraphs' JavaScript charting library (a copy of which is included in the package). Provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting.
|
2025-03-25 |
|
r-drr
|
public |
An Implementation of Dimensionality Reduction via Regression using Kernel Ridge Regression.
|
2025-03-25 |
|
r-diffusionmap
|
public |
Implements diffusion map method of data parametrization, including creation and visualization of diffusion map, clustering with diffusion K-means and regression using adaptive regression model.
|
2025-03-25 |
|
r-dbitest
|
public |
A helper that tests 'DBI' back ends for conformity to the interface.
|
2025-03-25 |
|
r-clubsandwich
|
public |
Provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) <http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf> and developed further by Pustejovsky and Tipton (2017) <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple- contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple- contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), ivreg (from package 'AER'), plm() (from package 'plm'), gls() and lme() (from 'nlme'), robu() (from 'robumeta'), and rma.uni() and rma.mv() (from 'metafor').
|
2025-03-25 |
|
r-cliapp
|
public |
Create rich command line applications, with colors, headings, lists, alerts, progress bars, etc. It uses CSS for custom themes.
|
2025-03-25 |
|
r-chemometrics
|
public |
R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).
|
2025-03-25 |
|
r-cellranger
|
public |
Helper functions to work with spreadsheets and the "A1:D10" style of cell range specification.
|
2025-03-25 |
|
r-cccd
|
public |
Class Cover Catch Digraphs, neighborhood graphs, and relatives.
|
2025-03-25 |
|
r-bsda
|
public |
Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.
|
2025-03-25 |
|
r-bridgesampling
|
public |
Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
|
2025-03-25 |
|
r-bradleyterry2
|
public |
Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
|
2025-03-25 |
|
r-bookdown
|
public |
Output formats and utilities for authoring books and technical documents with R Markdown.
|
2025-03-25 |
|
r-blme
|
public |
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting. Extends 'lme4' by Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker.
|
2025-03-25 |
|
r-arm
|
public |
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
|
2025-03-25 |
|
r-agd
|
public |
Tools for the analysis of growth data: to extract an LMS table from a gamlss object, to calculate the standard deviation scores and its inverse, and to superpose two wormplots from different models. The package contains a some varieties of reference tables, especially for The Netherlands.
|
2025-03-25 |
|
r-wskm
|
public |
Entropy weighted k-means (ewkm) is a weighted subspace clustering algorithm that is well suited to very high dimensional data. Weights are calculated as the importance of a variable with regard to cluster membership. The two-level variable weighting clustering algorithm tw-k-means (twkm) introduces two types of weights, the weights on individual variables and the weights on variable groups, and they are calculated during the clustering process. The feature group weighted k-means (fgkm) extends this concept by grouping features and weighting the group in addition to weighting individual features.
|
2025-03-25 |
|
r-vegan
|
public |
Ordination methods, diversity analysis and other functions for community and vegetation ecologists.
|
2025-03-25 |
|
r-ttr
|
public |
Functions and data to construct technical trading rules with R.
|
2025-03-25 |
|
r-tmb
|
public |
With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines 'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through 'BLAS' and parallel user templates.
|
2025-03-25 |
|
r-tibble
|
public |
Provides a 'tbl_df' class (the 'tibble') that provides stricter checking and better formatting than the traditional data frame.
|
2025-03-25 |
|
r-text2vec
|
public |
Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. This package provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.
|
2025-03-25 |
|
r-spatstat
|
public |
Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
|
2025-03-25 |
|
r-spamm
|
public |
Inference based on mixed-effect models, including generalized linear mixed models with spatial correlations and models with non-Gaussian random effects (e.g., Beta). Both classical geostatistical models, and Markov random field models on irregular grids, can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a generalized linear mixed model. Various approximations of likelihood or restricted likelihood are implemented, in particular h-likelihood (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) and Laplace approximation.
|
2025-03-25 |
|
r-satellite
|
public |
Herein, we provide a broad variety of functions which are useful for handling, manipulating, and visualizing satellite-based remote sensing data. These operations range from mere data import and layer handling (eg subsetting), over Raster* typical data wrangling (eg crop, extend), to more sophisticated (pre-)processing tasks typically applied to satellite imagery (eg atmospheric and topographic correction). This functionality is complemented by a full access to the satellite layers' metadata at any stage and the documentation of performed actions in a separate log file. Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat Global Land Survey data set. Moreover, support for Terra and Aqua-MODIS as well as PROBA-V is expected to arrive shortly.
|
2025-03-25 |
|
r-rsqlite
|
public |
Embeds the 'SQLite' database engine in R and provides an interface compliant with the 'DBI' package. The source for the 'SQLite' engine is included.
|
2025-03-25 |
|
r-rspectra
|
public |
R interface to the 'Spectra' library <https://spectralib.org/> for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user.
|
2025-03-25 |
|
r-robust
|
public |
Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis.
|
2025-03-25 |
|
r-rgexf
|
public |
Create, read and write GEXF (Graph Exchange XML Format) graph files (used in Gephi and others). Using the XML package, it allows the user to easily build/read graph files including attributes, GEXF viz attributes (such as color, size, and position), network dynamics (for both edges and nodes) and edge weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through "sigmajs" (a javascript library) and interact with the igraph package.
|
2025-03-25 |
|
r-rem
|
public |
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
|
2025-03-25 |
|
r-ranger
|
public |
A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.
|
2025-03-25 |
|
r-quantreg
|
public |
Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included.
|
2025-03-25 |
|
r-profvis
|
public |
Interactive visualizations for profiling R code.
|
2025-03-25 |
|
r-performanceanalytics
|
public |
Collection of econometric functions for performance and risk analysis. This package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.
|
2025-03-25 |
|
r-penalized
|
public |
Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.
|
2025-03-25 |
|
r-pdfcluster
|
public |
Cluster analysis via nonparametric density estimation is performed. Operationally, the kernel method is used throughout to estimate the density. Diagnostics methods for evaluating the quality of the clustering are available. The package includes also a routine to estimate the probability density function obtained by the kernel method, given a set of data with arbitrary dimensions.
|
2025-03-25 |
|
r-partykit
|
public |
A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <http://jmlr.org/papers/v16/hothorn15a.html>.
|
2025-03-25 |
