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r-rsghb
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Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.
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2024-01-16 |
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r-rseis
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Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms. Includes general time-series tools, plotting, filtering, interactive display.
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2024-01-16 |
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r-rsnns
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The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.
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2024-01-16 |
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r-rsiena
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The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Tom A.B. Snijders (2017), Stochastic Actor-Oriented Models for Network Dynamics, Annual Review of Statistics and Its Application, 4, 343-363 <doi: 10.1146/annurev-statistics-060116-054035>. An extensive manual, scripts, and much further information is at the Siena website <http://www.stats.ox.ac.uk/~snijders/siena/>.
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2024-01-16 |
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r-rserve
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None |
Rserve acts as a socket server (TCP/IP or local sockets) which allows binary requests to be sent to R. Every connection has a separate workspace and working directory. Client-side implementations are available for popular languages such as C/C++ and Java, allowing any application to use facilities of R without the need of linking to R code. Rserve supports remote connection, user authentication and file transfer. A simple R client is included in this package as well.
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2024-01-16 |
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r-rrcovhd
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Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).
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2024-01-16 |
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r-rsdepth
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Ray Shooting Depth functions are provided for bivariate analysis. This mainly includes functions for computing the bivariate depth as well as RS median. Drawing functions for depth bags are also provided.
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2024-01-16 |
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r-rsclient
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Client for Rserve, allowing to connect to Rserve instances and issue commands.
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2024-01-16 |
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r-rsae
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Empirical best linear unbiased prediction (EBLUP) and robust prediction of the area-level means under the basic unit-level model. The model can be fitted by maximum likelihood or a (robust) M-estimator. Mean square prediction error is computed by a parametric bootstrap.
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2024-01-16 |
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r-rqpen
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Performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC. Below URL provides a link to a work in progress vignette.
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2024-01-16 |
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r-rri
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Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors, e.g., clustered errors, or doubly-clustered errors.
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2024-01-16 |
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r-rrf
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Feature Selection with Regularized Random Forest. This package is based on the 'randomForest' package by Andy Liaw. The key difference is the RRF() function that builds a regularized random forest. Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener, Regularized random forest for classification by Houtao Deng, Regularized random forest for regression by Xin Guan. Reference: Houtao Deng (2013) <arXiv:1306.0237>.
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2024-01-16 |
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r-rres
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Functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.
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2024-01-16 |
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r-rrcov
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Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point: principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>), linear and quadratic discriminant analysis (Todorov and Pires (2007)), multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>), outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>). See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>, Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
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2024-01-16 |
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r-round
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public |
Decimal rounding is non-trivial in binary arithmetic. ISO standard round to even is more rare than typically assumed as most decimal fractions are not exactly representable in binary. Our 'roundX()' versions explore differences between current and potential future versions of round() in R. Further, provides (some partly related) C99 math lib functions not in base R.
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2024-01-16 |
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r-rpeglmen
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Implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) <arxiv:1804.07780>. An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.
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2024-01-16 |
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r-rr
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public |
Enables researchers to conduct multivariate statistical analyses of survey data with randomized response technique items from several designs, including mirrored question, forced question, and unrelated question. This includes regression with the randomized response as the outcome and logistic regression with the randomized response item as a predictor. In addition, tools for conducting power analysis for designing randomized response items are included. The package implements methods described in Blair, Imai, and Zhou (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal of the American Statistical Association <https://graemeblair.com/papers/randresp.pdf>.
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2024-01-16 |
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r-rptests
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Performs goodness of fits tests for both high and low-dimensional linear models. It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity. In addition one can test the significance of potentially large groups of variables, and also produce p-values for the significance of individual variables in high-dimensional linear regression.
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2024-01-16 |
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r-rpf
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Factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions are made available with R_RegisterCCallable(). This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
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2024-01-16 |
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r-rpms
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Functions to allow users to build and analyze design consistent tree and random forest models using survey data from a complex sample design. The tree model algorithm can fit a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and selected splits are obtained using a randomized permutation test procedure which adjusted for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to any specified least squares linear model between the dependent and independent variables used in the end nodes. The main functions return the resulting binary tree or random forest as an object of "rpms" or "rpms_forest" type. The package also provides methods modeling a "boosted" tree or forest model and a tree model for zero-inflated data as well as a number of functions and methods available for use with these object types.
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2024-01-16 |
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r-rpact
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Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
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2024-01-16 |
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r-roxygen2
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None |
Generate your Rd documentation, 'NAMESPACE' file, and collation field using specially formatted comments. Writing documentation in-line with code makes it easier to keep your documentation up-to-date as your requirements change. 'roxygen2' is inspired by the 'Doxygen' system for C++.
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2024-01-16 |
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r-rotasym
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Implementation of the tests for rotational symmetry on the hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020) <doi:10.1080/01621459.2019.1665527>. The package also implements the proposed distributions on the hypersphere, based on the tangent-normal decomposition, and allows for the replication of the data application considered in the paper.
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2024-01-16 |
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r-roughsets
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Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.
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2024-01-16 |
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r-roptim
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Perform general purpose optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying optim().
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2024-01-16 |
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r-roi.plugin.qpoases
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public |
Enhances the 'R' Optimization Infrastructure ('ROI') package with the quadratic solver 'qpOASES'. More information about 'qpOASES' can be found at <https://github.com/coin-or/qpOASES>.
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2024-01-16 |
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r-roll
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public |
Fast and efficient computation of rolling and expanding statistics for time-series data.
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2024-01-16 |
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r-robustlmm
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Implements the Robust Scoring Equations estimator to fit linear mixed effects models robustly. Robustness is achieved by modification of the scoring equations combined with the Design Adaptive Scale approach.
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2024-01-16 |
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r-ropj
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public |
Read the data from Origin(R) project files ('*.opj') <https://www.originlab.com/doc/User-Guide/Origin-File-Types>. No write support is planned.
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2024-01-16 |
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r-rootwishart
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public |
Functions for hypothesis testing in single and double Wishart settings, based on Roy's largest root. This test statistic is especially useful in multivariate analysis. The computations are based on results by Chiani (2014) <DOI:10.1016/j.jmva.2014.04.002> and Chiani (2016) <DOI:10.1016/j.jmva.2015.10.007>. They use the fact that the CDF is related to the Pfaffian of a matrix that can be computed in a finite number of iterations. This package takes advantage of the Boost and Eigen C++ libraries to perform multi-precision linear algebra.
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2024-01-16 |
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r-rootsolve
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public |
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). Includes routines that: (1) generate gradient and jacobian matrices (full and banded), (2) find roots of non-linear equations by the 'Newton-Raphson' method, (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running, (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations by numerical differencing (using the method-of-lines approach). Includes fortran code.
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2024-01-16 |
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r-rook
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public |
An HTTP web server for R with a documented API to interface between R and the server. The documentation contains the Rook specification and details for building and running Rook applications. To get started, be sure and read the 'Rook' help file first.
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2024-01-16 |
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r-robustvarcomp
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Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.
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2024-01-16 |
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r-robusthd
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public |
Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression. Specifically, the package implements robust least angle regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; <doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).
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2024-01-16 |
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r-rodeo
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Provides an R6 class and several utility methods to facilitate the implementation of models based on ordinary differential equations. The heart of the package is a code generator that creates compiled 'Fortran' (or 'R') code which can be passed to a numerical solver. There is direct support for solvers contained in packages 'deSolve' and 'rootSolve'.
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2024-01-16 |
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r-rodbc
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An ODBC database interface.
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2024-01-16 |
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r-rococo
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Provides the robust gamma rank correlation coefficient as introduced by Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026> along with a permutation-based rank correlation test. The rank correlation coefficient and the test are explicitly designed for dealing with noisy numerical data.
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2024-01-16 |
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r-robustreg
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Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
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2024-01-16 |
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r-robust
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public |
Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis.
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2024-01-16 |
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r-robstattm
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Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>. This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.
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2024-01-16 |
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r-robustgasp
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Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.
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2024-01-16 |
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r-robslopes
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Fast algorithms for the Theil-Sen estimator, Siegel's repeated median slope estimator, and Passing-Bablok regression. The implementation is based on algorithms by Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998) <doi:10.1007/PL00009190>. The implementations are detailed in Raymaekers (2023) <doi:10.32614/RJ-2023-012> and Raymaekers J., Dufey F. (2022) <arXiv:2202.08060>. All algorithms run in quasilinear time.
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2024-01-16 |
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r-robustetm
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Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.
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2024-01-16 |
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r-robustblme
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Bayesian robust fitting of linear mixed effects models through weighted likelihood equations and approximate Bayesian computation as proposed by Ruli et al. (2017) <arXiv:1706.01752>.
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2024-01-16 |
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r-robustbase
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None |
"Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
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2024-01-16 |
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r-robustaft
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public |
R functions for the computation of the truncated maximum likelihood and the robust accelerated failure time regression for gaussian and log-Weibull case.
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2024-01-16 |
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r-rniftyreg
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public |
Provides an 'R' interface to the 'NiftyReg' image registration tools <https://github.com/KCL-BMEIS/niftyreg>. Linear and nonlinear registration are supported, in two and three dimensions.
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2024-01-16 |
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r-robcompositions
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Methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.
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2024-01-16 |
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r-robfilter
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Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> and the slope comparing adaptive repeated median by Borowski and Fried (2013) <doi:10.1007/s11222-013-9391-7> choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393> for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.
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2024-01-16 |
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r-robeth
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public |
Locations problems, M-estimates of coefficients and scale in linear regression, Weights for bounded influence regression, Covariance matrix of the coefficient estimates, Asymptotic relative efficiency of regression M-estimates, Robust testing in linear models, High breakdown point regression, M-estimates of covariance matrices, M-estimates for discrete generalized linear models.
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2024-01-16 |
