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r-sensitivitypstrat
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This package provides functions to perform principal stratification sensitivity analyses on datasets.
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2023-06-16 |
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r-segmentier
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A dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>. In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian' or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes.
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2023-06-16 |
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r-segmag
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Contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results.
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2023-06-16 |
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r-sdwd
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Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties.
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2023-06-16 |
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r-rcrm
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Fit a 2-parameter continual reassessment method (CRM) model (O'Quigley and Shen (1996), <doi: 10.2307/2532905>) regularized with L2 norm (Friedman et al. (2010), <doi: 10.18637/jss.v033.i01>) adjusted by the distance with the target dose limiting toxicity (DLT) rate.
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2023-06-16 |
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r-rcppxts
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This package provides access to some of the C level functions of the xts package. . In its current state, the package is mostly a proof-of-concept to support adding useful functions, and does not yet add any of its own.
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2023-06-16 |
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r-rcpphmm
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Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
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2023-06-16 |
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r-rcppfaddeeva
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Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see <http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package> for more information).
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2023-06-16 |
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r-raschsampler
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MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
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2023-06-16 |
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r-rare
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Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2018) <arXiv:1803.06675>. The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free.
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2023-06-16 |
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r-ramp
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Provides an efficient procedure for fitting the entire solution path for high-dimensional regularized quadratic generalized linear models with interactions effects under the strong or weak heredity constraint.
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2023-06-16 |
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r-rad
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Fit a variety of models to Rank Abundance Data
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2023-06-16 |
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r-glassofast
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A fast and improved implementation of the graphical LASSO.
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2023-06-16 |
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r-ggmselect
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Graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.
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2023-06-16 |
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r-geohashtools
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Tools for working with Gustavo Niemeyer's geohash coordinate system, ported to R from Hiroaki Kawai's 'Python' implementation and embellished to sit naturally in the R ecosystem.
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2023-06-16 |
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r-gensa
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Performs search for global minimum of a very complex non-linear objective function with a very large number of optima.
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2023-06-16 |
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r-genlasso
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Provides fast algorithms for computing the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two problems are given to improve stability and speed.
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2023-06-16 |
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r-gafit
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A group of sample points are evaluated against a user-defined expression, the sample points are lists of parameters with values that may be substituted into that expression. The genetic algorithm attempts to make the result of the expression as low as possible (usually this would be the sum of residuals squared).
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2023-06-16 |
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r-funitroots
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Provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's unit root test suite.
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2023-06-16 |
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r-ftnonpar
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The package contains R-functions to perform the methods in nonparametric regression and density estimation, described in Davies, P. L. and Kovac, A. (2001) Local Extremes, Runs, Strings and Multiresolution (with discussion) Annals of Statistics. 29. p1-65 Davies, P. L. and Kovac, A. (2004) Densities, Spectral Densities and Modality Annals of Statistics. Annals of Statistics. 32. p1093-1136 Kovac, A. (2006) Smooth functions and local extreme values. Computational Statistics and Data Analysis (to appear) D\"umbgen, L. and Kovac, A. (2006) Extensions of smoothing via taut strings Davies, P. L. (1995) Data features. Statistica Neerlandica 49,185-245.
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2023-06-16 |
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r-fpow
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Returns the noncentrality parameter of the noncentral F distribution if probability of type I and type II error, degrees of freedom of the numerator and the denominator are given. It may be useful for computing minimal detectable differences for general ANOVA models. This program is documented in the paper of A. Baharev, S. Kemeny, On the computation of the noncentral F and noncentral beta distribution; Statistics and Computing, 2008, 18 (3), 333-340.
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2023-06-16 |
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r-sommer
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Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects and unknown variance-covariance structures (i.e. heterogeneous and unstructured variance models) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). ML/REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) and dense known covariance structures for levels of random effects. Spatial models can also be fitted using i.e. the two-dimensional spline functionality available in sommer.
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2023-06-16 |
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r-smoothsurv
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Contains, as a main contribution, a function to fit a regression model with possibly right, left or interval censored observations and with the error distribution expressed as a mixture of G-splines. Core part of the computation is done in compiled C++ written using the Scythe Statistical Library Version 0.3.
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2023-06-16 |
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r-smisc
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A collection of functions for statistical computing and data manipulation in R. Includes routines for data ingestion, operating on dataframes and matrices, conversion to and from lists, converting factors, filename manipulation, programming utilities, parallelization, plotting, statistical and mathematical operations, and time series.
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2023-06-16 |
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r-smds
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Symbolic multidimensional scaling for interval-valued dissimilarities. The hypersphere model and the hyperbox model are available.
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2023-06-16 |
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r-shrinkcovmat
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Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
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2023-06-16 |
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r-seismicroll
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Fast versions of seismic analysis functions that 'roll' over a vector of values. See the 'RcppRoll' package for alternative versions of basic statistical functions such as rolling mean, median, etc.
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2023-06-16 |
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r-readstata13
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Function to read and write the 'Stata' file format.
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2023-06-16 |
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r-read.dbc
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Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
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2023-06-16 |
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r-rcppnloptexample
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An example package which shows use of 'NLopt' functionality from C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks to the exporting API added there by Jelmer Ypma. This package is a fully functioning, updated, and expanded version of the initial example by Julien Chiquet at <https://github.com/jchiquet/RcppArmadilloNLoptExample> also containing a large earlier pull request of mine.
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2023-06-16 |
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r-rcppexamples
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Examples for Seamless R and C++ integration The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. The site <http://gallery.rcpp.org> regroups a large number of examples for 'Rcpp'.
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2023-06-16 |
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r-rbf
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A robust backfitting algorithm for additive models based on (robust) local polynomial kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and it computes predictions for points outside the training set if desired. See Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> for details.
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2023-06-16 |
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r-rankaggreg
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Performs aggregation of ordered lists based on the ranks using several different algorithms: Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm (for small problems).
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2023-06-16 |
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r-qif
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Developed to perform the estimation and inference for regression coefficient parameters in longitudinal marginal models using the method of quadratic inference functions. Like generalized estimating equations, this method is also a quasi-likelihood inference method. It has been showed that the method gives consistent estimators of the regression coefficients even if the correlation structure is misspecified, and it is more efficient than GEE when the correlation structure is misspecified. Based on Qu, A., Lindsay, B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
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2023-06-16 |
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r-sprsmdl
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R functions to mine sparse models from data.
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2023-06-16 |
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r-spikes
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Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.
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2023-06-16 |
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r-spectralgp
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Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers.
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2023-06-16 |
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r-sparsepp
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Provides interface to 'sparsepp' - fast, memory efficient hash map. It is derived from Google's excellent 'sparsehash' implementation. We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance).
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2023-06-16 |
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r-spacesrgb
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Standard RGB spaces included are sRGB, 'Adobe' RGB, 'ProPhoto' RGB, BT.709, and others. User-defined RGB spaces are also possible. There is partial support for ACES Color workflows.
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2023-06-16 |
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r-smloutliers
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Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
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2023-06-16 |
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r-smcure
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An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models
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2023-06-16 |
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r-slc
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Estimates the slope and level change present in data after removing phase A trend. Represents graphically the original and the detrended data.
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2023-06-16 |
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r-simsem
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Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
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2023-06-16 |
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r-shuffle
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Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.
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2023-06-16 |
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r-sgmcmc
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Provides functions that performs popular stochastic gradient Markov chain Monte Carlo (SGMCMC) methods on user specified models. The required gradients are automatically calculated using 'TensorFlow' <https://www.tensorflow.org/>, an efficient library for numerical computation. This means only the log likelihood and log prior functions need to be specified. The methods implemented include stochastic gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian Monte Carlo (SGHMC), stochastic gradient Nose-Hoover thermostat (SGNHT) and their respective control variate versions for increased efficiency. References: M. Welling, Y. W. Teh (2011) <http://www.icml-2011.org/papers/398_icmlpaper.pdf>; T. Chen, E. B. Fox, C. E. Guestrin (2014) <arXiv:1402.4102>; N. Ding, Y. Fang, R. Babbush, C. Chen, R. D. Skeel, H. Neven (2014) <https://papers.nips.cc/paper/5592-bayesian-sampling-using-stochastic-gradient-thermostats>; J. Baker, P. Fearnhead, E. B. Fox, C. Nemeth (2017) <arXiv:1706.05439>.
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2023-06-16 |
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r-sesem
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Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.
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2023-06-16 |
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r-sendmailr
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Package contains a simple SMTP client which provides a portable solution for sending email, including attachment, from within R.
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2023-06-16 |
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r-semmodcomp
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Conduct tests of difference in fit for mean and covariance structure models as in structural equation modeling (SEM)
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2023-06-16 |
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r-sejong
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Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP
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2023-06-16 |
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r-sdcspatial
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Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.
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2023-06-16 |
