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r-fastpcs
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The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.
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2024-01-16 |
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r-fastmatch
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Package providing a fast match() replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match() function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory.
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2024-01-16 |
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r-fastm
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Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.
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2024-01-16 |
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r-fastica
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Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit.
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2024-01-16 |
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r-fastjt
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This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.
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2024-01-16 |
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r-fastglm
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Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard against convergence issues.
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2024-01-16 |
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r-fasterelasticnet
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Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)(<doi:10.1214/009053604000000067 >) and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library.
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2024-01-16 |
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r-familyrank
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Grows families of features by selecting features that maximize a weighted score calculated from empirical feature scores and graphical knowledge. The final weighted score for a feature is determined by summing a feature's family-weighted scores across all families in which the feature appears.
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2024-01-16 |
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r-fastghquad
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Fast, numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A. (1994) <doi:10.2307/2337136> for a reference on these methods.
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2024-01-16 |
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r-fasterize
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Provides a drop-in replacement for rasterize() from the 'raster' package that takes 'sf'-type objects, and is much faster. There is support for the main options provided by the rasterize() function, including setting the field used and background value, and options for aggregating multi-layer rasters. Uses the scan line algorithm attributed to Wylie et al. (1967) <doi:10.1145/1465611.1465619>.
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2024-01-16 |
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r-fastdigest
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Provides an R interface to Bob Jenkin's streaming, non-cryptographic 'SpookyHash' hash algorithm for use in digest-based comparisons of R objects. 'fastdigest' plugs directly into R's internal serialization machinery, allowing digests of all R objects the serialize() function supports, including reference-style objects via custom hooks. Speed is high and scales linearly by object size; memory usage is constant and negligible.
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2024-01-16 |
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r-fastcox
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We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) DOI: <doi:10.4310/SII.2013.v6.n2.a1>.
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2024-01-16 |
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r-fastcmh
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A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.
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2024-01-16 |
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r-fastcluster
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This is a two-in-one package which provides interfaces to both R and 'Python'. It implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the 'SciPy' package 'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the 'flashClust' package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide. For information on how to install the 'Python' files, see the file INSTALL in the source distribution. Based on the present package, Christoph Dalitz also wrote a pure 'C++' interface to 'fastcluster': <https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/>.
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2024-01-16 |
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r-farver
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The encoding of colour can be handled in many different ways, using different colour spaces. As different colour spaces have different uses, efficient conversion between these representations are important. The 'farver' package provides a set of functions that gives access to very fast colour space conversion and comparisons implemented in C++, and offers speed improvements over the 'convertColor' function in the 'grDevices' package.
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2024-01-16 |
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r-fastbandchol
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Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
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2024-01-16 |
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r-farmtest
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Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>. Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.
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2024-01-16 |
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r-far
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Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ...
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2024-01-16 |
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r-fansi
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Counterparts to R string manipulation functions that account for the effects of ANSI text formatting control sequences.
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2024-01-16 |
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r-factorstochvol
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Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
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2024-01-16 |
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r-fanc
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Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used. This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.
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2024-01-16 |
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r-fable
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Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.
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2024-01-16 |
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r-falconx
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This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data. Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual. The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.
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2024-01-16 |
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r-falcon
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This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing. Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
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2024-01-16 |
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r-factoclass
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Some functions of 'ade4' and 'stats' are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. See, for example: Lebart, L. and Piron, M. and Morineau, A. (2006). Statistique Exploratoire Multidimensionnelle, Dunod, Paris. In order to permit to have different weights of the elements to be clustered, the function 'kmeansW', programmed in C++, is included. It is a modification of 'kmeans'. Some graphical functions include the option: 'gg=FALSE'. When 'gg=TRUE', they use the 'ggplot2' and 'ggrepel' packages to avoid the super-position of the labels.
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2024-01-16 |
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r-factominer
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Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
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2024-01-16 |
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r-extremes
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General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.
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2024-01-16 |
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r-extremis
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Conducts inference in statistical models for extreme values (de Carvalho et al (2012), <doi:10.1080/03610926.2012.709905>; de Carvalho and Davison (2014), <doi:10.1080/01621459.2013.872651>; Einmahl et al (2016), <doi:10.1111/rssb.12099>).
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2024-01-16 |
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r-extradistr
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Density, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson.
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2024-01-16 |
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r-expm
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Computation of the matrix exponential, logarithm, sqrt, and related quantities, using traditional and modern methods.
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2024-01-16 |
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r-expperm
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A set of functions for computing expected permutation matrices given a matrix of likelihoods for each individual assignment. It has been written to accompany the forthcoming paper 'Computing expectations and marginal likelihoods for permutations'. Publication details will be updated as soon as they are finalized.
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2024-01-16 |
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r-expint
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The exponential integrals E_1(x), E_2(x), E_n(x) and Ei(x), and the incomplete gamma function G(a, x) defined for negative values of its first argument. The package also gives easy access to the underlying C routines through an API; see the package vignette for details. A test package included in sub-directory example_API provides an implementation. C routines derived from the GNU Scientific Library <https://www.gnu.org/software/gsl/>.
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2024-01-16 |
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r-exif
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Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif) library.
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2024-01-16 |
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r-experiment
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Provides various statistical methods for designing and analyzing randomized experiments. One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data.
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2024-01-16 |
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r-exdex
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Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>. Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) <doi:10.1007/s10687-007-0034-2>, the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and a similar approach of Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3> that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided.
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2024-01-16 |
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r-excursions
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Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.
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2024-01-16 |
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r-evtree
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Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions.
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2024-01-16 |
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r-exceedprob
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Computes confidence intervals for the exceedance probability of normally distributed estimators. Currently only supports general linear models. Please see Segal (2019) <arXiv:1803.03356> for more information.
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2024-01-16 |
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r-exactranktests
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Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel.
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2024-01-16 |
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r-evgam
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Methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. For details of distributions see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>. Details of how evgam works and various examples are given in Youngman, B.D. (2022) <doi:10.18637/jss.v103.i03>.
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2024-01-16 |
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r-essentials
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Some essential packages for working with R
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2024-01-16 |
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r-ewgof
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Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.
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2024-01-16 |
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r-eulerr
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Generate area-proportional Euler diagrams using numerical optimization. An Euler diagram is a generalization of a Venn diagram, relaxing the criterion that all interactions need to be represented. Diagrams may be fit with ellipses and circles via a wide range of inputs and can be visualized in numerous ways.
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2024-01-16 |
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r-evdbayes
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Provides functions for the Bayesian analysis of extreme value models, using Markov chain Monte Carlo methods. Allows the construction of both uninformative and informed prior distributions for common statistical models applied to extreme event data, including the generalized extreme value distribution.
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2024-01-16 |
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r-etm
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The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multi-state model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 <doi:10.18637/jss.v038.i04>). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 <doi:10.1007/s00180-010-0200-x>).
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2024-01-16 |
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r-evd
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Extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models.
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2024-01-16 |
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r-erm
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Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
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2024-01-16 |
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r-esshist
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Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
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2024-01-16 |
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r-estimatr
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Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.
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2024-01-16 |
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r-ergm.multi
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A set of extensions for the 'ergm' package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. 'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Krivitsky, Coletti, and Hens (2022) <doi:10.48550/arXiv.2202.03685>.
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2024-01-16 |
