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r-sparsehessianfd
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Estimates Hessian of a scalar-valued function, and returns it in a sparse Matrix format. The sparsity pattern must be known in advance. The algorithm is especially efficient for hierarchical models with a large number of heterogeneous units. See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.
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2025-04-22 |
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r-sparcl
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Implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726.
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2025-04-22 |
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r-spam64
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Provides the Fortran code of the R package 'spam' with 64-bit integers. Loading this package together with the R package spam enables the sparse matrix class spam to handle huge sparse matrices with more than 2^31-1 non-zero elements.
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2025-04-22 |
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r-spam
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Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64).
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2025-04-22 |
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r-spacejam
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This package provides an extension of conditional independence (CIG) and directed acyclic graph (DAG) estimation to the case where conditional relationships are (non-linear) additive models.
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2025-04-22 |
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r-space
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Partial correlation estimation with joint sparse regression model
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2025-04-22 |
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r-soundexbr
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The SoundexBR package provides an algorithm for decoding names into phonetic codes, as pronounced in Portuguese. The goal is for homophones to be encoded to the same representation so that they can be matched despite minor differences in spelling. The algorithm mainly encodes consonants; a vowel will not be encoded unless it is the first letter. The soundex code resultant consists of a four digits long string composed by one letter followed by three numerical digits: the letter is the first letter of the name, and the digits encode the remaining consonants.
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2025-04-22 |
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r-soobench
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Collection of different single objective test functions useful for benchmarks and algorithm development.
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2025-04-22 |
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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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2025-04-22 |
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r-som
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Self-Organizing Map (with application in gene clustering).
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2025-04-22 |
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r-softrandomforest
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Performs random forests for soft decision trees for a classification problem. Current limitations are for a maximum depth of 5 resulting in 16 terminal nodes. Some data cleaning is required before input. Final graphic output requires currently requires exporting to 'Microsoft Excel' for visualization. Method based on Irsoy, Yildiz and Alpaydin (2012, ISBN: 978-4-9906441-1-6).
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2025-04-22 |
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r-softimpute
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Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)
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2025-04-22 |
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r-soda
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Functions, examples and other software related to the book "Software for Data Analysis: Programming with R". See package?SoDA for an overview.
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2025-04-22 |
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r-socialnetworks
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Generates social networks using either of two approaches: using either pairwise distances or territorial area intersections.
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2025-04-22 |
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r-social
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A set of functions to quantify and visualise social autocorrelation.
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2025-04-22 |
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r-soccer
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Functions for evaluating tournament predictions, simulating results from individual soccer matches and tournaments. See <http://sandsynligvis.dk/2018/08/03/world-cup-prediction-winners/> for more information.
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2025-04-22 |
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r-sobolsequence
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R implementation of S. Joe and F. Y. Kuo(2008) <DOI:10.1137/070709359>. The implementation is based on the data file new-joe-kuo-6.21201 <http://web.maths.unsw.edu.au/~fkuo/sobol/>.
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2025-04-22 |
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r-snnr
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Solve the problem of over-parameterization in neural networks for genomic selection. Daniel Gianola, Hayrettin OkutEmail, Kent A Weigel and Guilherme JM Rosa (2011) <doi:10.1186/1471-2156-12-87>.
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2025-04-22 |
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r-snipem
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Snipping methods optimally removing scattered cells for robust estimation and cluster analysis.
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2025-04-22 |
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r-snappier
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Compression and decompression with 'Snappy'.
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2025-04-22 |
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r-snakecharmr
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Run 'Python' code, make function calls, assign and retrieve variables, etc. from R. A fork from 'rPython' which uses 'jsonlite', 'Rcpp' and has several fixes and improvements.
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2025-04-22 |
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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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2025-04-22 |
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r-smoothhazard
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Estimation of two-state (survival) models and irreversible illness- death models with possibly interval-censored,left-truncated and right-censored data. Proportional intensities regression models can be specified to allow for covariates effects separately for each transition. We use either a parametric approach with Weibull baseline intensities or a semi-parametric approach with M-splines approximation of baseline intensities in order to obtain smooth estimates of the hazard functions. Parameter estimates are obtained by maximum likelihood in the parametric approach and by penalized maximum likelihood in the semi-parametric approach.
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2025-04-22 |
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r-smma
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Efficient design matrix free procedure for solving a soft maximin problem for large scale array-tensor structured models. Currently Lasso and SCAD penalized estimation is implemented.
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2025-04-22 |
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r-smitidvisu
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Visualisation tools for 'SMITIDstruct' package. Allow to visualize host timeline, transmission tree, index diversities and variant graph using HTMLwidgets. It mainly using D3JS javascript framework.
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2025-04-22 |
