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r-ordinal
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Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.
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2025-03-25 |
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r-openxlsx2
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Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets.
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2025-03-25 |
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r-openmx
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Create structural equation models that can be manipulated programmatically. Models may be specified with matrices or paths (LISREL or RAM) Example models include confirmatory factor, multiple group, mixture distribution, categorical threshold, modern test theory, differential Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares. equations, state space, and many others. Support and advanced package binaries available at <http://openmx.ssri.psu.edu>. The software is described in Neale, Hunter, Pritikin, Zahery, Brick, Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.
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2025-03-25 |
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r-openimager
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Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
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2025-03-25 |
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r-openair
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Tools to analyse, interpret and understand air pollution data. Data are typically regular time series and air quality measurement, meteorological data and dispersion model output can be analysed. The package is described in Carslaw and Ropkins (2012, <doi:10.1016/j.envsoft.2011.09.008>) and subsequent papers.
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2025-03-25 |
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r-olsrr
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Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.
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2025-03-25 |
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r-npflow
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Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.
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2025-03-25 |
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r-nsm3
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Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
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2025-03-25 |
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r-nonlineartseries
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Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
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2025-03-25 |
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r-nns
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Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).
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2025-03-25 |
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r-nngeo
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K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor search uses (1) C code from 'GeographicLib' for lon-lat point layers, (2) function knn() from package 'nabor' for projected point layers, or (3) function st_distance() from package 'sf' for line or polygon layers. The package also includes several other utility functions for spatial analysis.
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2025-03-25 |
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r-nlmixr2est
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Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
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2025-03-25 |
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r-networkdynamic
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Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.
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2025-03-25 |
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r-network
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public |
Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
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2025-03-25 |
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r-ndjson
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public |
Streaming 'JSON' ('ndjson') has one 'JSON' record per-line and many modern 'ndjson' files contain large numbers of records. These constructs may not be columnar in nature, but it is often useful to read in these files and "flatten" the structure out to enable working with the data in an R 'data.frame'-like context. Functions are provided that make it possible to read in plain 'ndjson' files or compressed ('gz') 'ndjson' files and either validate the format of the records or create "flat" 'data.table' structures from them.
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2025-03-25 |
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r-narray
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Stacking arrays according to dimension names, subset-aware splitting and mapping of functions, intersecting along arbitrary dimensions, converting to and from data.frames, and many other helper functions.
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2025-03-25 |
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r-nanotime
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Full 64-bit resolution date and time functionality with nanosecond granularity is provided, with easy transition to and from the standard 'POSIXct' type. Three additional classes offer interval, period and duration functionality for nanosecond-resolution timestamps.
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2025-03-25 |
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r-nanonext
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R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. NNG is a socket library providing high-performance scalability protocols, a cross-platform standard for messaging and communications. Serves as a concurrency framework for building distributed applications, utilising 'aio' objects which resolve automatically upon completion of asynchronous operations. Implements connections with transport layer security, and synchronisation primitives, which allow R execution to wait upon events being signalled by concurrent messaging threads.
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2025-03-25 |
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r-n2r
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Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <arXiv:1603.09320>.
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2025-03-25 |
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r-nanoarrow
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Provides an 'R' interface to the 'nanoarrow' 'C' library and the 'Apache Arrow' application binary interface. Functions to import and export 'ArrowArray', 'ArrowSchema', and 'ArrowArrayStream' 'C' structures to and from 'R' objects are provided alongside helpers to facilitate zero-copy data transfer among 'R' bindings to libraries implementing the 'Arrow' 'C' data interface.
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2025-03-25 |
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r-mvpot
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public |
Tools for high-dimensional peaks-over-threshold inference and simulation of spatial extremal processes. Key references include de Fondeville and Davison (2018) <doi:10.1093/biomet/asy026>, Thibaud and Opitz (2015) <doi:10.1093/biomet/asv045>, Wadsworth and Tawn <doi:10.1093/biomet/ast042>.
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2025-03-25 |
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r-multimode
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Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.
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2025-03-25 |
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r-multgee
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public |
GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.
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2025-03-25 |
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r-mrgsolve
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public |
Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.
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2025-03-25 |
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r-mrfdepth
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public |
Tools to compute depth measures and implementations of related tasks such as outlier detection, data exploration and classification of multivariate, regression and functional data.
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2025-03-25 |
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r-move
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public |
Contains functions to access movement data stored in 'movebank.org' as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions.
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2025-03-25 |
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r-mpath
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Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2020) <arXiv:2010.02848>.
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2025-03-25 |
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r-mousetrap
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Mouse-tracking, the analysis of mouse movements in computerized experiments, is a method that is becoming increasingly popular in the cognitive sciences. The mousetrap package offers functions for importing, preprocessing, analyzing, aggregating, and visualizing mouse-tracking data. An introduction into mouse-tracking analyses using mousetrap can be found in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2023) <doi:10.31234/osf.io/v685r> (preprint: <https://osf.io/preprints/psyarxiv/v685r>).
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2025-03-25 |
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r-movehmm
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Provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.
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2025-03-25 |
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r-mmrm
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Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane and Schnell (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.
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2025-03-25 |
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r-mombf
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Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).
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2025-03-25 |
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r-mokken
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Contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions.
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2025-03-25 |
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r-mlpack
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A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) <doi:10.21105/joss.05026>.
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2025-03-25 |
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r-mlt
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Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>.
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2025-03-25 |
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r-mmapcharr
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Uses memory-mapping to enable the random access of elements of a text file of characters separated by characters as if it were a simple R(cpp) matrix.
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2025-03-25 |
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r-mlr3mbo
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A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.
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2025-03-25 |
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r-mixtools
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public |
Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).
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2025-03-25 |
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r-mixr
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Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.
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2025-03-25 |
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r-mixl
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Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.
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2025-03-25 |
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r-mixak
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Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
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2025-03-25 |
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r-mirt
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Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture, and zero-inflated response models are supported.
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2025-03-25 |
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r-mirtcat
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Provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks.
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2025-03-25 |
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r-miceadds
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Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
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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-mhurdle
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Estimation of models with zero left-censored variables. Null values may be caused by a selection process Cragg (1971) <doi:10.2307/1909582>, insufficient resources Tobin (1958) <doi:10.2307/1907382> or infrequency of purchase Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.
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2025-03-25 |
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r-mev
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Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
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2025-03-25 |
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r-metabma
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Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, <doi:10.1177/25152459211031256>).
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2025-03-25 |
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r-metacoder
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A set of tools for parsing, manipulating, and graphing data classified by a hierarchy (e.g. a taxonomy).
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2025-03-25 |
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r-mess
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A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book.
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2025-03-25 |
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r-memisc
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An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.
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2025-03-25 |
