Showing 200 of total 575 results (show query)
pachadotdev
cpp11armadillo:An 'Armadillo' Interface
Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. This implementation is detailed in Vargas Sepulveda and Schneider Malamud (2024) <doi:10.48550/arXiv.2408.11074>.
Maintained by Mauricio Vargas Sepulveda. Last updated 26 days ago.
armadillocppcpp11hacktoberfestlinear-algebra
24.9 match 9 stars 9.14 score 1 scripts 16 dependentsbsvars
bsvars:Bayesian Estimation of Structural Vector Autoregressive Models
Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.
Maintained by Tomasz Woźniak. Last updated 1 months ago.
bayesian-inferenceeconometricsvector-autoregressionopenblascppopenmp
28.9 match 46 stars 7.67 score 32 scripts 1 dependentscran
wavethresh:Wavelets Statistics and Transforms
Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.
Maintained by Guy Nason. Last updated 7 months ago.
35.8 match 5.89 score 41 dependentsbioc
BiocSingular:Singular Value Decomposition for Bioconductor Packages
Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.
Maintained by Aaron Lun. Last updated 5 months ago.
softwaredimensionreductionprincipalcomponentbioconductor-packagehuman-cell-atlassingular-value-decompositioncpp
16.3 match 7 stars 12.10 score 1.2k scripts 99 dependentserichson
rsvd:Randomized Singular Value Decomposition
Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided.
Maintained by N. Benjamin Erichson. Last updated 4 years ago.
dimension-reductionmatrix-approximationpcaprincipal-component-analysisprobabilistic-algorithmsrandomized-algorithmsingular-value-decompositionsvd
18.2 match 98 stars 10.80 score 408 scripts 119 dependentsbusiness-science
timetk:A Tool Kit for Working with Time Series
Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.
Maintained by Matt Dancho. Last updated 1 years ago.
coercioncoercion-functionsdata-miningdplyrforecastforecastingforecasting-modelsmachine-learningseries-decompositionseries-signaturetibbletidytidyquanttidyversetimetime-seriestimeseries
13.8 match 625 stars 14.15 score 4.0k scripts 16 dependentsmlverse
torch:Tensors and Neural Networks with 'GPU' Acceleration
Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
Maintained by Daniel Falbel. Last updated 6 days ago.
10.4 match 520 stars 16.52 score 1.4k scripts 38 dependentssnoweye
QZ:Generalized Eigenvalues and QZ Decomposition
Generalized eigenvalues and eigenvectors use QZ decomposition (generalized Schur decomposition). The decomposition needs an N-by-N non-symmetric matrix A or paired matrices (A,B) with eigenvalues reordering mechanism. The decomposition functions are mainly based Fortran subroutines in complex*16 and double precision of LAPACK library (version 3.10.0 or later).
Maintained by Wei-Chen Chen. Last updated 2 years ago.
31.8 match 2 stars 5.20 score 53 scriptsfriendly
matlib:Matrix Functions for Teaching and Learning Linear Algebra and Multivariate Statistics
A collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. Many of these functions are designed for tutorial purposes in learning matrix algebra ideas using R. In some cases, functions are provided for concepts available elsewhere in R, but where the function call or name is not obvious. In other cases, functions are provided to show or demonstrate an algorithm. In addition, a collection of functions are provided for drawing vector diagrams in 2D and 3D and for rendering matrix expressions and equations in LaTeX.
Maintained by Michael Friendly. Last updated 2 days ago.
diagramslinear-equationsmatrixmatrix-functionsmatrix-visualizervectorvignette
12.6 match 65 stars 12.89 score 900 scripts 11 dependentszhaokg
Rbeast:Bayesian Change-Point Detection and Time Series Decomposition
Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
Maintained by Kaiguang Zhao. Last updated 6 months ago.
anomoly-detectionbayesian-time-seriesbreakpoint-detectionchangepoint-detectioninterrupted-time-seriesseasonality-analysisstructural-breakpointtechnical-analysistime-seriestime-series-decompositiontrendtrend-analysis
20.3 match 302 stars 7.63 score 89 scriptssciviews
pastecs:Package for Analysis of Space-Time Ecological Series
Regularisation, decomposition and analysis of space-time series. The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 functionalities to R.
Maintained by Philippe Grosjean. Last updated 1 years ago.
14.4 match 4 stars 10.34 score 2.1k scripts 13 dependentshelske
Rlibeemd:Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)
An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version from GitHub: <https://github.com/helske/Rlibeemd/>.
Maintained by Jouni Helske. Last updated 2 years ago.
cdecompositioneemdemdtime-seriesgslcppopenmp
24.0 match 39 stars 6.14 score 17 scripts 14 dependentskylecaudle
rTensor2:MultiLinear Algebra
A set of tools for basic tensor operators. A tensor in the context of data analysis in a multidimensional array. The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)). Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition. Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld et al. (2015) <https://www.sciencedirect.com/science/article/pii/S0024379515004358>.
Maintained by Kyle Caudle. Last updated 12 months ago.
58.2 match 2.48 score 2 scripts 1 dependentsbusiness-science
anomalize:Tidy Anomaly Detection
The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods.
Maintained by Matt Dancho. Last updated 1 years ago.
anomalyanomaly-detectiondecompositiondetect-anomaliesiqrtime-series
15.0 match 339 stars 9.56 score 332 scriptsbwlewis
irlba:Fast Truncated Singular Value Decomposition and Principal Components Analysis for Large Dense and Sparse Matrices
Fast and memory efficient methods for truncated singular value decomposition and principal components analysis of large sparse and dense matrices.
Maintained by B. W. Lewis. Last updated 2 years ago.
pcaprincipal-component-analysissingular-value-decompositionsparse-principal-componentssvdopenblas
9.9 match 128 stars 13.85 score 1.5k scripts 293 dependentsrobjhyndman
forecast:Forecasting Functions for Time Series and Linear Models
Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
Maintained by Rob Hyndman. Last updated 7 months ago.
forecastforecastingopenblascpp
7.3 match 1.1k stars 18.63 score 16k scripts 239 dependentsbquast
decompr:Global Value Chain Decomposition
Three global value chain (GVC) decompositions are implemented. The Leontief decomposition derives the value added origin of exports by country and industry as in Hummels, Ishii and Yi (2001). The Koopman, Wang and Wei (2014) decomposition splits country-level exports into 9 value added components, and the Wang, Wei and Zhu (2013) decomposition splits bilateral exports into 16 value added components. Various GVC indicators based on these decompositions are computed in the complimentary 'gvc' package. --- References: --- Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96. Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94. Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research.
Maintained by Bastiaan Quast. Last updated 1 years ago.
19.5 match 17 stars 6.78 score 118 scripts 1 dependentscrlsierra
SoilR:Models of Soil Organic Matter Decomposition
Functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014) <doi:10.5194/gmd-7-1919-2014>.
Maintained by Carlos A. Sierra. Last updated 1 years ago.
44.8 match 5 stars 2.88 score 153 scriptsrobjhyndman
stR:Seasonal Trend Decomposition Using Regression
Methods for decomposing seasonal data: STR (a Seasonal-Trend time series decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can also be used for forecasting.
Maintained by Rob Hyndman. Last updated 2 months ago.
17.1 match 7 stars 7.02 score 1.3k scripts 2 dependentskylecaudle
TensorTools:Multilinear Algebra
A set of tools for basic tensor operators. A tensor in the context of data analysis in a multidimensional array. The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)). Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition. Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld, E., Kilmer, M., and Aeron, S. (2015) <doi:10.1016/j.laa.2015.07.021>.
Maintained by Kyle Caudle. Last updated 5 months ago.
57.8 match 2.00 scoremlampros
VMDecomp:Variational Mode Decomposition
'RcppArmadillo' implementation for the Matlab code of the 'Variational Mode Decomposition' and 'Two-Dimensional Variational Mode Decomposition'. For more information, see (i) 'Variational Mode Decomposition' by K. Dragomiretskiy and D. Zosso in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, <doi:10.1109/TSP.2013.2288675>; (ii) 'Two-Dimensional Variational Mode Decomposition' by Dragomiretskiy, K., Zosso, D. (2015), In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, <doi:10.1007/978-3-319-14612-6_15>.
Maintained by Lampros Mouselimis. Last updated 2 years ago.
rcpparmadillovariational-mode-decompositionopenblascppopenmp
19.9 match 8 stars 5.78 score 1 scripts 5 dependentssvkucheryavski
mdatools:Multivariate Data Analysis for Chemometrics
Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.
Maintained by Sergey Kucheryavskiy. Last updated 8 months ago.
14.5 match 35 stars 7.37 score 220 scripts 1 dependentsrikenbit
rTensor:Tools for Tensor Analysis and Decomposition
A set of tools for creation, manipulation, and modeling of tensors with arbitrary number of modes. A tensor in the context of data analysis is a multidimensional array. rTensor does this by providing a S4 class 'Tensor' that wraps around the base 'array' class. rTensor provides common tensor operations as methods, including matrix unfolding, summing/averaging across modes, calculating the Frobenius norm, and taking the inner product between two tensors. Familiar array operations are overloaded, such as index subsetting via '[' and element-wise operations. rTensor also implements various tensor decomposition, including CP, GLRAM, MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some auxiliary functions include the Khatri-Rao product, Kronecker product, and the Hadamard product for a list of matrices.
Maintained by Koki Tsuyuzaki. Last updated 2 years ago.
13.6 match 6 stars 7.65 score 278 scripts 30 dependentsbioc
DelayedTensor:R package for sparse and out-of-core arithmetic and decomposition of Tensor
DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum.
Maintained by Koki Tsuyuzaki. Last updated 5 months ago.
softwareinfrastructuredatarepresentationdimensionreduction
21.2 match 4 stars 4.68 score 3 scriptsrstudio
keras3:R Interface to 'Keras'
Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.
Maintained by Tomasz Kalinowski. Last updated 4 days ago.
7.1 match 845 stars 13.57 score 264 scripts 2 dependentsbioc
TDbasedUFE:Tensor Decomposition Based Unsupervised Feature Extraction
This is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. It can perform unsupervised feature extraction. It uses tensor decomposition. It is applicable to gene expression, DNA methylation, and histone modification etc. It can perform multiomics analysis. It is also potentially applicable to single cell omics data sets.
Maintained by Y-h. Taguchi. Last updated 5 months ago.
geneexpressionfeatureextractionmethylationarraysinglecellbioinformaticsdna-methylationgene-expression-profileshistone-modificationsmultiomicstensor-decomposition
17.1 match 5 stars 5.48 score 9 scripts 1 dependentscozygene
BisqueRNA:Decomposition of Bulk Expression with Single-Cell Sequencing
Provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples. For more details, see Jew and Alvarez et al (2019) <doi:10.1101/669911>.
Maintained by Brandon Jew. Last updated 4 years ago.
12.7 match 72 stars 6.95 score 124 scriptsrikenbit
dcTensor:Discrete Matrix/Tensor Decomposition
Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.
Maintained by Koki Tsuyuzaki. Last updated 10 months ago.
16.9 match 3 stars 5.08 scoredcgerard
tensr:Covariance Inference and Decompositions for Tensor Datasets
A collection of functions for Kronecker structured covariance estimation and testing under the array normal model. For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
Maintained by David Gerard. Last updated 2 years ago.
13.0 match 5 stars 6.53 score 56 scripts 4 dependentsigraph
igraph:Network Analysis and Visualization
Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.
Maintained by Kirill Müller. Last updated 9 hours ago.
complex-networksgraph-algorithmsgraph-theorymathematicsnetwork-analysisnetwork-graphfortranlibxml2glpkopenblascpp
3.9 match 582 stars 21.11 score 31k scripts 1.9k dependentsallen-1242
StructuralDecompose:Decomposes a Level Shifted Time Series
Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to 'lowess' for smoothing and 'strucchange' for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection.
Maintained by Allen Sunny. Last updated 2 years ago.
decompositiontimeseries-analysis
19.5 match 1 stars 4.18 score 4 scriptsbioc
Rdisop:Decomposition of Isotopic Patterns
In high resolution mass spectrometry (HR-MS), the measured masses can be decomposed into potential element combinations (chemical sum formulas). Where additional mass/intensity information of respective isotopic peaks is available, decomposition can take this information into account to better rank the potential candidate sum formulas. To compare measured mass/intensity information with the theoretical distribution of candidate sum formulas, the latter needs to be calculated. This package implements fast algorithms to address both tasks, the calculation of isotopic distributions for arbitrary sum formulas (assuming a HR-MS resolution of roughly 30,000), and the ranked list of sum formulas fitting an observed peak or isotopic peak set.
Maintained by Steffen Neumann. Last updated 30 days ago.
immunooncologymassspectrometrymetabolomicsmass-spectrometrycpp
8.8 match 4 stars 9.14 score 111 scripts 2 dependentsgzt
CholWishart:Cholesky Decomposition of the Wishart Distribution
Sampling from the Cholesky factorization of a Wishart random variable, sampling from the inverse Wishart distribution, sampling from the Cholesky factorization of an inverse Wishart random variable, sampling from the pseudo Wishart distribution, sampling from the generalized inverse Wishart distribution, computing densities for the Wishart and inverse Wishart distributions, and computing the multivariate gamma and digamma functions. Provides a header file so the C functions can be called directly from other programs.
Maintained by Geoffrey Thompson. Last updated 6 months ago.
cholesky-decompositioncholesky-factorizationdigamma-functionsgammamultivariatepseudo-wishartwishartwishart-distributionsopenblas
10.6 match 7 stars 7.05 score 41 scripts 13 dependentstidyverts
feasts:Feature Extraction and Statistics for Time Series
Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.
Maintained by Mitchell OHara-Wild. Last updated 4 months ago.
5.9 match 300 stars 12.38 score 1.4k scripts 7 dependentsbriencj
dae:Functions Useful in the Design and ANOVA of Experiments
The content falls into the following groupings: (i) Data, (ii) Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v) Matrix functions, (vi) Projector and canonical efficiency functions, and (vii) Miscellaneous functions. There is a vignette describing how to use the design functions for randomizing and assessing designs available as a vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of information when the 'Error' function has been used in the call to 'aov'. The package 'dae' can also be installed from <http://chris.brien.name/rpackages/>.
Maintained by Chris Brien. Last updated 4 months ago.
8.5 match 1 stars 8.62 score 356 scripts 7 dependentsegenn
rtemis:Machine Learning and Visualization
Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.
Maintained by E.D. Gennatas. Last updated 1 months ago.
data-sciencedata-visualizationmachine-learningmachine-learning-libraryvisualization
10.3 match 145 stars 7.09 score 50 scripts 2 dependentstraitecoevo
litterfitter:Fit a Collection of Curves to Single Cohort Decomposition Data
There is a long tradition of studying the flux of carbon from the biosphere to the atmosphere by following a particular cohort of litter (wood, leaves, roots, or other organic material) through time. The resulting data are mass remaining and time. A variety of functional forms may be used to fit the resulting data. Some work better empirically. Some are better connected to a process-based understanding. Some have a small number of free parameters; others have more. This package matches decomposition data to a family of these curves using likelihood--based fitting. This package is based on published research by Cornwell & Weedon (2013) <doi:10.1111/2041-210X.12138>.
Maintained by Will Cornwell. Last updated 2 years ago.
14.0 match 5 stars 5.05 score 15 scriptsbioc
limpca:An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods
This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design.
Maintained by Manon Martin. Last updated 5 months ago.
statisticalmethodprincipalcomponentregressionvisualizationexperimentaldesignmultiplecomparisongeneexpressionmetabolomics
12.2 match 2 stars 5.73 score 2 scriptsfranzmohr
bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models
Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).
Maintained by Franz X. Mohr. Last updated 1 years ago.
bayesianbayesian-inferencebayesian-varbvarbvecmgibbs-samplingmcmcvector-autoregressionvector-error-correction-modelopenblascpp
10.2 match 31 stars 6.80 score 34 scripts 1 dependentsrikenbit
nnTensor:Non-Negative Tensor Decomposition
Some functions for performing non-negative matrix factorization, non-negative CANDECOMP/PARAFAC (CP) decomposition, non-negative Tucker decomposition, and generating toy model data. See Andrzej Cichock et al (2009) and the reference section of GitHub README.md <https://github.com/rikenbit/nnTensor>, for details of the methods.
Maintained by Koki Tsuyuzaki. Last updated 10 months ago.
10.5 match 16 stars 6.58 score 9 scripts 4 dependentscran
mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.
Maintained by Simon Wood. Last updated 1 years ago.
5.3 match 32 stars 12.71 score 17k scripts 7.8k dependentscailab-tamu
scTenifoldNet:Construct and Compare scGRN from Single-Cell Transcriptomic Data
A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Maintained by Daniel Osorio. Last updated 3 months ago.
differential-regulation-analysisgene-regulatory-networksmanifold-alignmentsingle-celltensor-decomposition
11.8 match 22 stars 5.63 score 65 scripts 1 dependentsellisp
ggseas:'stats' for Seasonal Adjustment on the Fly with 'ggplot2'
Provides 'ggplot2' 'stats' that estimate seasonally adjusted series and rolling summaries such as rolling average on the fly for time series.
Maintained by Peter Ellis. Last updated 7 years ago.
9.6 match 74 stars 6.68 score 129 scriptscran
VisitorCounts:Modeling and Forecasting Visitor Counts Using Social Media
Performs modeling and forecasting of park visitor counts using social media data and (partial) on-site visitor counts. Specifically, the model is built based on an automatic decomposition of the trend and seasonal components of the social media-based park visitor counts, from which short-term forecasts of the visitor counts and percent changes in the visitor counts can be made. A reference for the underlying model that 'VisitorCounts' uses can be found at Russell Goebel, Austin Schmaltz, Beth Ann Brackett, Spencer A. Wood, Kimihiro Noguchi (2023) <doi:10.1002/for.2965> .
Maintained by Robert Bowen. Last updated 2 months ago.
19.3 match 3.30 score 8 scriptsstla
EigenR:Complex Matrix Algebra with 'Eigen'
Matrix algebra using the 'Eigen' C++ library: determinant, rank, inverse, pseudo-inverse, kernel and image, QR decomposition, Cholesky decomposition, Schur decomposition, Hessenberg decomposition, linear least-squares problems. Also provides matrix functions such as exponential, logarithm, power, sine and cosine. Complex matrices are supported.
Maintained by Stéphane Laurent. Last updated 11 months ago.
13.3 match 5 stars 4.78 score 27 scripts 1 dependentstomaskrehlik
frequencyConnectedness:Spectral Decomposition of Connectedness Measures
Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.
Maintained by Tomas Krehlik. Last updated 2 years ago.
10.3 match 100 stars 5.88 score 50 scripts 1 dependentscadam00
scoredec:S-Core Graph Decomposition
S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) <doi:10.1103/PhysRevE.88.062819>. The high speed and the low memory usage make it suitable for large graphs.
Maintained by Christos Adam. Last updated 4 months ago.
graph-theoryrcpps-core-decompositioncpp
12.5 match 1 stars 4.65 score 3 scriptsfcampelo
MOEADr:Component-Wise MOEA/D Implementation
Modular implementation of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) [Zhang and Li (2007), <DOI:10.1109/TEVC.2007.892759>] for quick assembling and testing of new algorithmic components, as well as easy replication of published MOEA/D proposals. The full framework is documented in a paper published in the Journal of Statistical Software [<doi:10.18637/jss.v092.i06>].
Maintained by Felipe Campelo. Last updated 2 years ago.
moeadmultiobjective-optimization
9.1 match 20 stars 6.30 score 40 scriptstylerjpike
sovereign:State-Dependent Empirical Analysis
A set of tools for state-dependent empirical analysis through both VAR- and local projection-based state-dependent forecasts, impulse response functions, historical decompositions, and forecast error variance decompositions.
Maintained by Tyler J. Pike. Last updated 2 years ago.
econometricsforecastingimpulse-responselocal-projectionmacroeconomicsstate-dependenttime-seriesvector-autoregression
11.9 match 11 stars 4.74 score 8 scriptsasl
Rssa:A Collection of Methods for Singular Spectrum Analysis
Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, <doi:10.1007/978-3-662-57380-8>). See 'citation("Rssa")' for details.
Maintained by Anton Korobeynikov. Last updated 6 months ago.
7.8 match 58 stars 7.10 score 182 scripts 4 dependentsreneschulenberg
dineq:Decomposition of (Income) Inequality
Decomposition of (income) inequality by population sub groups. For a decomposition on a single variable the mean log deviation can be used (see Mookherjee Shorrocks (1982) <DOI:10.2307/2232673>. For a decomposition on multiple variables a regression based technique can be used (see Fields (2003) <DOI:10.1016/s0147-9121(03)22001-x>). Recentered influence function regression for marginal effects of the (income or wealth) distribution (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>). Some extensions to inequality functions to handle weights and/or missings.
Maintained by René Schulenberg. Last updated 7 years ago.
13.8 match 3 stars 4.00 score 112 scripts 1 dependentstidyverts
fabletools:Core Tools for Packages in the 'fable' Framework
Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
Maintained by Mitchell OHara-Wild. Last updated 1 months ago.
4.5 match 91 stars 12.18 score 396 scripts 18 dependentsevanjflack
bacondecomp:Goodman-Bacon Decomposition
Decomposition for differences-in-differences with variation in treatment timing from Goodman-Bacon (2018) <doi:10.3386/w25018>.
Maintained by Evan Flack. Last updated 4 years ago.
applied-econometricseconometricseconomics
7.7 match 47 stars 6.98 score 202 scriptsadeverse
ade4:Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences
Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.
Maintained by Aurélie Siberchicot. Last updated 12 days ago.
3.5 match 39 stars 14.96 score 2.2k scripts 256 dependentsbnaras
PMA:Penalized Multivariate Analysis
Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008> and Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data <doi:10.2202/1544-6115.1470>.
Maintained by Balasubramanian Narasimhan. Last updated 1 years ago.
7.2 match 4 stars 7.24 score 254 scripts 11 dependentsnicholasjclark
mvgam:Multivariate (Dynamic) Generalized Additive Models
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.
Maintained by Nicholas J Clark. Last updated 17 hours ago.
bayesian-statisticsdynamic-factor-modelsecological-modellingforecastinggaussian-processgeneralised-additive-modelsgeneralized-additive-modelsjoint-species-distribution-modellingmultilevel-modelsmultivariate-timeseriesstantime-series-analysistimeseriesvector-autoregressionvectorautoregressioncpp
5.2 match 139 stars 9.85 score 117 scriptsnimble-dev
nimble:MCMC, Particle Filtering, and Programmable Hierarchical Modeling
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.
Maintained by Christopher Paciorek. Last updated 4 days ago.
bayesian-inferencebayesian-methodshierarchical-modelsmcmcprobabilistic-programmingopenblascpp
3.9 match 169 stars 12.97 score 2.6k scripts 19 dependentsgeobosh
mcompanion:Objects and Methods for Multi-Companion Matrices
Provides a class for multi-companion matrices with methods for arithmetic and factorization. A method for generation of multi-companion matrices with prespecified spectral properties is provided, as well as some utilities for periodically correlated and multivariate time series models. See Boshnakov (2002) <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>.
Maintained by Georgi N. Boshnakov. Last updated 1 years ago.
eigen-vector-decompositionmatricesperiodictime-series
12.1 match 4.05 score 37 scripts 2 dependentsbioc
TDbasedUFEadv:Advanced package of tensor decomposition based unsupervised feature extraction
This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.
Maintained by Y-h. Taguchi. Last updated 5 months ago.
geneexpressionfeatureextractionmethylationarraysinglecellsoftwarebioconductor-packagebioinformaticstensor-decomposition
10.8 match 4.48 score 4 scriptsbioc
lmdme:Linear Model decomposition for Designed Multivariate Experiments
linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS.
Maintained by Cristobal Fresno. Last updated 5 months ago.
microarrayonechanneltwochannelvisualizationdifferentialexpressionexperimentdatacancer
12.8 match 3.78 score 1 scriptsdevillemereuil
Reacnorm:Perform a Partition of Variance of Reaction Norms
Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. The variance partition distinguishes between the variance arising from the average shape of the reaction norms (V_Plas) and the (additive) genetic variance . The latter is itself separated into an environment-blind component (V_G/V_A) and the component arising from plasticity (V_GxE/V_AxE). The package also provides a way to further partition V_Plas into aspects (slope/curvature) of the shape of the average reaction norm (pi-decomposition) and partition V_Add (gamma-decomposition) and V_AxE (iota-decomposition) into the impact of genetic variation in the reaction norm parameters. Reference: de Villemereuil & Chevin (2025) <doi:10.32942/X2NC8B>.
Maintained by Pierre de Villemereuil. Last updated 17 days ago.
8.9 match 4 stars 5.34 scorealexkz
kernlab:Kernel-Based Machine Learning Lab
Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.
Maintained by Alexandros Karatzoglou. Last updated 7 months ago.
3.9 match 21 stars 12.26 score 7.8k scripts 487 dependentsajdamico
convey:Income Concentration Analysis with Complex Survey Samples
Variance estimation on indicators of income concentration and poverty using complex sample survey designs. Wrapper around the 'survey' package.
Maintained by Anthony Damico. Last updated 5 months ago.
7.8 match 19 stars 6.09 score 201 scriptsdkimstatlab
EMD:Empirical Mode Decomposition and Hilbert Spectral Analysis
For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
Maintained by Donghoh Kim. Last updated 3 years ago.
11.2 match 1 stars 4.17 score 55 scripts 9 dependentsfchen365
epca:Exploratory Principal Component Analysis
Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation.
Maintained by Fan Chen. Last updated 11 months ago.
community-detectionexploratory-data-analysismatrix-decompositionspcaprincipal-component-analysissparse-matrix
9.8 match 11 stars 4.74 score 8 scriptsbioc
pcaMethods:A collection of PCA methods
Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.
Maintained by Henning Redestig. Last updated 5 months ago.
3.5 match 49 stars 13.10 score 538 scripts 73 dependentskkholst
lava:Latent Variable Models
A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
Maintained by Klaus K. Holst. Last updated 2 months ago.
latent-variable-modelssimulationstatisticsstructural-equation-models
3.5 match 33 stars 12.85 score 610 scripts 476 dependentshojsgaard
gRbase:A Package for Graphical Modelling in R
The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>) and in the paper by Dethlefsen and Højsgaard, (2005, <doi:10.18637/jss.v014.i17>). Please see 'citation("gRbase")' for citation details.
Maintained by Søren Højsgaard. Last updated 4 months ago.
4.9 match 3 stars 9.24 score 241 scripts 20 dependentsrudjer
SparseM:Sparse Linear Algebra
Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.
Maintained by Roger Koenker. Last updated 8 months ago.
3.9 match 3 stars 11.47 score 306 scripts 1.5k dependentsbryanhanson
LearnPCA:Functions, Data Sets and Vignettes to Aid in Learning Principal Components Analysis (PCA)
Principal component analysis (PCA) is one of the most widely used data analysis techniques. This package provides a series of vignettes explaining PCA starting from basic concepts. The primary purpose is to serve as a self-study resource for anyone wishing to understand PCA better. A few convenience functions are provided as well.
Maintained by Bryan A. Hanson. Last updated 10 months ago.
7.0 match 10 stars 6.20 score 1 scriptsbozenne
BuyseTest:Generalized Pairwise Comparisons
Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.
Maintained by Brice Ozenne. Last updated 4 days ago.
generalized-pairwise-comparisonsnon-parametricstatisticscpp
7.3 match 5 stars 5.91 score 90 scriptsang-yu
cdgd:Causal Decomposition of Group Disparities
The framework of causal decomposition of group disparities developed by Yu and Elwert (2023) <doi:10.48550/arXiv.2306.16591>. This package implements the decomposition estimators that are based on efficient influence functions. For the nuisance functions of the estimators, both parametric and nonparametric options are provided, as well as manual options in case the default models are not satisfying.
Maintained by Ang Yu. Last updated 8 months ago.
14.3 match 1 stars 3.00 score 3 scriptsshipei-zeng
dfvad:Diewert and Fox's Method of Value Added Growth Decomposition
Decomposing value added growth into explanatory factors. A cost constrained value added function is defined to specify the production frontier. Industry estimates can also be aggregated using a weighted average approach. Details about the methodology and data can be found in Diewert and Fox (2018) <doi:10.1093/oxfordhb/9780190226718.013.19> and Zeng, Parsons, Diewert and Fox (2018) <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.
Maintained by Shipei Zeng. Last updated 3 years ago.
11.5 match 3.70 score 5 scriptsrikenbit
ccTensor:CUR/CX Tensor Decomposition
CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.
Maintained by Koki Tsuyuzaki. Last updated 2 years ago.
11.8 match 3.48 score 2 dependentsbioc
orthos:`orthos` is an R package for variance decomposition using conditional variational auto-encoders
`orthos` decomposes RNA-seq contrasts, for example obtained from a gene knock-out or compound treatment experiment, into unspecific and experiment-specific components. Original and decomposed contrasts can be efficiently queried against a large database of contrasts (derived from ARCHS4, https://maayanlab.cloud/archs4/) to identify similar experiments. `orthos` furthermore provides plotting functions to visualize the results of such a search for similar contrasts.
Maintained by Panagiotis Papasaikas. Last updated 4 days ago.
rnaseqdifferentialexpressiongeneexpression
9.8 match 4.18 score 2 scriptsoconnellmj
r.jive:Perform JIVE Decomposition for Multi-Source Data
Performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
Maintained by Michael J. OConnell. Last updated 4 years ago.
12.8 match 2 stars 3.18 score 75 scriptsbioc
decompTumor2Sig:Decomposition of individual tumors into mutational signatures by signature refitting
Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor.
Maintained by Rosario M. Piro. Last updated 5 months ago.
softwaresnpsequencingdnaseqgenomicvariationsomaticmutationbiomedicalinformaticsgeneticsbiologicalquestionstatisticalmethod
8.2 match 1 stars 4.78 score 10 scripts 1 dependentshaghish
shapley:Weighted Mean SHAP and CI for Robust Feature Selection in ML Grid
This R package introduces Weighted Mean SHapley Additive exPlanations (WMSHAP), an innovative method for calculating SHAP values for a grid of fine-tuned base-learner machine learning models as well as stacked ensembles, a method not previously available due to the common reliance on single best-performing models. By integrating the weighted mean SHAP values from individual base-learners comprising the ensemble or individual base-learners in a tuning grid search, the package weights SHAP contributions according to each model's performance, assessed by multiple either R squared (for both regression and classification models). alternatively, this software also offers weighting SHAP values based on the area under the precision-recall curve (AUCPR), the area under the curve (AUC), and F2 measures for binary classifiers. It further extends this framework to implement weighted confidence intervals for weighted mean SHAP values, offering a more comprehensive and robust feature importance evaluation over a grid of machine learning models, instead of solely computing SHAP values for the best model. This methodology is particularly beneficial for addressing the severe class imbalance (class rarity) problem by providing a transparent, generalized measure of feature importance that mitigates the risk of reporting SHAP values for an overfitted or biased model and maintains robustness under severe class imbalance, where there is no universal criteria of identifying the absolute best model. Furthermore, the package implements hypothesis testing to ascertain the statistical significance of SHAP values for individual features, as well as comparative significance testing of SHAP contributions between features. Additionally, it tackles a critical gap in feature selection literature by presenting criteria for the automatic feature selection of the most important features across a grid of models or stacked ensembles, eliminating the need for arbitrary determination of the number of top features to be extracted. This utility is invaluable for researchers analyzing feature significance, particularly within severely imbalanced outcomes where conventional methods fall short. Moreover, it is also expected to report democratic feature importance across a grid of models, resulting in a more comprehensive and generalizable feature selection. The package further implements a novel method for visualizing SHAP values both at subject level and feature level as well as a plot for feature selection based on the weighted mean SHAP ratios.
Maintained by E. F. Haghish. Last updated 3 days ago.
class-imbalanceclass-imbalance-problemfeature-extractionfeature-importancefeature-selectionmachine-learningmachine-learning-algorithmsshapshap-analysisshap-valuesshapelyshapley-additive-explanationsshapley-decompositionshapley-valueshapley-valuesshapleyvalueweighted-shapweighted-shap-confidence-intervalweighted-shapleyweighted-shapley-ci
7.5 match 14 stars 5.19 score 17 scriptsalexanderlange53
svars:Data-Driven Identification of SVAR Models
Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).
Maintained by Alexander Lange. Last updated 2 years ago.
5.3 match 46 stars 7.22 score 130 scriptsbioc
BASiCS:Bayesian Analysis of Single-Cell Sequencing data
Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.
Maintained by Catalina Vallejos. Last updated 5 months ago.
immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecelldifferentialexpressionbayesiancellbiologybioconductor-packagegene-expressionrcpprcpparmadilloscrna-seqsingle-cellopenblascppopenmp
3.6 match 83 stars 10.26 score 368 scripts 1 dependentscran
geigen:Calculate Generalized Eigenvalues, the Generalized Schur Decomposition and the Generalized Singular Value Decomposition of a Matrix Pair with Lapack
Functions to compute generalized eigenvalues and eigenvectors, the generalized Schur decomposition and the generalized Singular Value Decomposition of a matrix pair, using Lapack routines.
Maintained by Berend Hasselman. Last updated 6 years ago.
13.4 match 1 stars 2.77 score 16 dependentshafen
stlplus:Enhanced Seasonal Decomposition of Time Series by Loess
Decompose a time series into seasonal, trend, and remainder components using an implementation of Seasonal Decomposition of Time Series by Loess (STL) that provides several enhancements over the STL method in the stats package. These enhancements include handling missing values, providing higher order (quadratic) loess smoothing with automated parameter choices, frequency component smoothing beyond the seasonal and trend components, and some basic plot methods for diagnostics.
Maintained by Ryan Hafen. Last updated 8 years ago.
5.2 match 66 stars 7.02 score 63 scripts 5 dependentshypertidy
silicate:Common Forms for Complex Hierarchical and Relational Data Structures
Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and provide intermediate forms that relate spatial data structures to this mathematical construct.
Maintained by Michael D. Sumner. Last updated 1 years ago.
hierarchical-datasimplicial-complexspatial-datastructural-primitivestopologytriangulation
5.0 match 54 stars 7.28 score 111 scripts 7 dependentsyixuan
RSpectra:Solvers for Large-Scale Eigenvalue and SVD Problems
R interface to the 'Spectra' library <https://spectralib.org/> for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user.
Maintained by Yixuan Qiu. Last updated 8 months ago.
eigenvaluesspectrasvdopenblascpp
2.9 match 81 stars 12.40 score 394 scripts 433 dependentscraddm
eegUtils:Utilities for Electroencephalographic (EEG) Analysis
Electroencephalography data processing and visualization tools. Includes import functions for 'BioSemi' (.BDF), 'Neuroscan' (.CNT), 'Brain Vision Analyzer' (.VHDR), 'EEGLAB' (.set) and 'Fieldtrip' (.mat). Many preprocessing functions such as referencing, epoching, filtering, and ICA are available. There are a variety of visualizations possible, including timecourse and topographical plotting.
Maintained by Matt Craddock. Last updated 5 months ago.
eegeeg-analysiseeg-dataeeg-signalseeg-signals-processingopenblascppopenmp
5.5 match 106 stars 6.54 score 82 scriptsmwsill
s4vd:Biclustering via Sparse Singular Value Decomposition Incorporating Stability Selection
The main function s4vd() performs a biclustering via sparse singular value decomposition with a nested stability selection. The results is an biclust object and thus all methods of the biclust package can be applied.
Maintained by Martin Sill. Last updated 5 years ago.
6.7 match 4 stars 5.31 score 17 scripts 2 dependentscorybrunson
ordr:A Tidyverse Extension for Ordinations and Biplots
Ordination comprises several multivariate exploratory and explanatory techniques with theoretical foundations in geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. Greenacre (2010, ISBN:978-84-923846) shows how the most established of these, including principal components analysis, correspondence analysis, multidimensional scaling, factor analysis, and discriminant analysis, rely on eigen-decompositions or singular value decompositions of pre-processed numeric matrix data. These decompositions give rise to a set of shared coordinates along which the row and column elements can be measured. The overlay of their scatterplots on these axes, introduced by Gabriel (1971) <doi:10.1093/biomet/58.3.453>, is called a biplot. 'ordr' provides inspection, extraction, manipulation, and visualization tools for several popular ordination classes supported by a set of recovery methods. It is inspired by and designed to integrate into 'tidyverse' workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.
Maintained by Jason Cory Brunson. Last updated 13 days ago.
biplotdata-visualizationdimension-reductiongeometric-data-analysisgrammar-of-graphicslog-ratio-analysismultivariate-analysismultivariate-statisticsordinationtidymodelstidyverse
4.8 match 24 stars 7.26 score 28 scriptstomkellygenetics
matrixcalc:Collection of Functions for Matrix Calculations
A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563.
Maintained by S. Thomas Kelly. Last updated 4 years ago.
4.1 match 8.32 score 1.7k scripts 149 dependentsmaartenbijlsma
cfdecomp:Counterfactual Decomposition: MC Integration of the G-Formula
Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.
Maintained by Maarten Jacob Bijlsma. Last updated 4 years ago.
12.6 match 1 stars 2.70 score 5 scriptssklivan
ineq.2d:Two-Dimensional Decomposition of the Theil Index and the Squared Coefficient of Variation
Decomposition of income inequality by groups formed of individuals possessing similar characteristics (e.g., sex, education, age) and their income sources at the same time. Decomposition of the Theil index is based on Giammatteo, M. (2007) <https://www.lisdatacenter.org/wps/liswps/466.pdf>. Decomposition of the squared coefficient of variation is based on Garcia-Penalosa, C., & Orgiazzi, E. (2013) <doi:10.1111/roiw.12054>.
Maintained by Ivan Skliarov. Last updated 2 years ago.
7.1 match 4.70 score 3 scriptspecanproject
PEcAn.uncertainty:PEcAn Functions Used for Propagating and Partitioning Uncertainties in Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by David LeBauer. Last updated 2 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
3.8 match 216 stars 8.91 score 15 scripts 5 dependentsrjdverse
rjd3toolkit:Utility Functions around 'JDemetra+ 3.0'
R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to model time series (create outlier regressors, user-defined calendar regressors, UCARIMA models...), to test the presence of trading days or seasonal effects and also to set specifications in pre-adjustment and benchmarking when using rjd3x13 or rjd3tramoseats.
Maintained by Tanguy Barthelemy. Last updated 5 months ago.
jdemetraseasonal-adjustmenttimeseriesopenjdk
5.7 match 5 stars 5.81 score 48 scripts 15 dependentsshixiangwang
sigminer:Extract, Analyze and Visualize Mutational Signatures for Genomic Variations
Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021) <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil B., et al. (2020) <DOI:10.1038/s41586-020-1943-3> & Steele Christopher D., et al. (2022) <DOI:10.1038/s41586-022-04738-6>). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study.
Maintained by Shixiang Wang. Last updated 5 months ago.
bayesian-nmfbioinformaticscancer-researchcnvcopynumber-signaturescosmic-signaturesdbseasy-to-useindelmutational-signaturesnmfnmf-extractionsbssignature-extractionsomatic-mutationssomatic-variantsvisualizationcpp
3.5 match 150 stars 9.48 score 123 scripts 2 dependentsr-forge
Matrix:Sparse and Dense Matrix Classes and Methods
A rich hierarchy of sparse and dense matrix classes, including general, symmetric, triangular, and diagonal matrices with numeric, logical, or pattern entries. Efficient methods for operating on such matrices, often wrapping the 'BLAS', 'LAPACK', and 'SuiteSparse' libraries.
Maintained by Martin Maechler. Last updated 7 days ago.
1.9 match 1 stars 17.23 score 33k scripts 12k dependentsfriendly
genridge:Generalized Ridge Trace Plots for Ridge Regression
The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.
Maintained by Michael Friendly. Last updated 3 months ago.
bias-variancegraphicsprincipal-component-analysisregression-modelsridge-regressionsingular-value-decomposition
6.7 match 4 stars 4.84 score 69 scriptsbioc
nipalsMCIA:Multiple Co-Inertia Analysis via the NIPALS Method
Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies.
Maintained by Maximilian Mattessich. Last updated 27 days ago.
softwareclusteringclassificationmultiplecomparisonnormalizationpreprocessingsinglecell
4.8 match 6 stars 6.60 score 10 scriptsrikenbit
mwTensor:Multi-Way Component Analysis
For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.
Maintained by Koki Tsuyuzaki. Last updated 2 years ago.
11.6 match 2.70 score 2 scriptsjkrijthe
RSSL:Implementations of Semi-Supervised Learning Approaches for Classification
A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.
Maintained by Jesse Krijthe. Last updated 1 years ago.
5.1 match 58 stars 6.05 score 128 scripts 1 dependentsbioc
scTensor:Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition
The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.
Maintained by Koki Tsuyuzaki. Last updated 5 months ago.
dimensionreductionsinglecellsoftwaregeneexpression
7.3 match 4.18 score 2 scriptsstan-dev
posterior:Tools for Working with Posterior Distributions
Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.
Maintained by Paul-Christian Bürkner. Last updated 10 days ago.
1.9 match 168 stars 16.13 score 3.3k scripts 342 dependentsrstudio
tfprobability:Interface to 'TensorFlow Probability'
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
Maintained by Tomasz Kalinowski. Last updated 3 years ago.
3.5 match 54 stars 8.63 score 221 scripts 3 dependentsnalzok
tree.interpreter:Random Forest Prediction Decomposition and Feature Importance Measure
An R re-implementation of the 'treeinterpreter' package on PyPI <https://pypi.org/project/treeinterpreter/>. Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <arXiv:1906.10845>.
Maintained by Qingyao Sun. Last updated 5 years ago.
data-sciencedatascienceinterpretabilitymachine-learningrandom-forestcpp
5.1 match 12 stars 5.79 score 6 scriptslucaweihs
SEMID:Identifiability of Linear Structural Equation Models
Provides routines to check identifiability or non-identifiability of linear structural equation models as described in Drton, Foygel, and Sullivant (2011) <doi:10.1214/10-AOS859>, Foygel, Draisma, and Drton (2012) <doi:10.1214/12-AOS1012>, and other works. The routines are based on the graphical representation of structural equation models.
Maintained by Nils Sturma. Last updated 2 years ago.
7.1 match 4 stars 4.06 score 29 scriptsinsightsengineering
tern:Create Common TLGs Used in Clinical Trials
Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.
Maintained by Joe Zhu. Last updated 2 months ago.
clinical-trialsgraphslistingsnestoutputstables
2.3 match 79 stars 12.62 score 186 scripts 9 dependentsbioc
GlobalAncova:Global test for groups of variables via model comparisons
The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.
Maintained by Manuela Hummel. Last updated 5 months ago.
microarrayonechanneldifferentialexpressionpathwaysregression
5.3 match 5.32 score 9 scripts 1 dependentstomasfryda
h2o:R Interface for the 'H2O' Scalable Machine Learning Platform
R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Maintained by Tomas Fryda. Last updated 1 years ago.
3.4 match 3 stars 8.20 score 7.8k scripts 11 dependentshusson
FactoMineR:Multivariate Exploratory Data Analysis and Data Mining
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).
Maintained by Francois Husson. Last updated 3 months ago.
1.9 match 47 stars 14.71 score 5.6k scripts 112 dependentsrohelab
LRMF3:Low Rank Matrix Factorization S3 Objects
Provides S3 classes to represent low rank matrix decompositions.
Maintained by Alex Hayes. Last updated 3 years ago.
matrix-factorizationsingular-value-decomposition
7.2 match 2 stars 3.78 score 6 scripts 2 dependentsjavlacalle
tsdecomp:Decomposition of Time Series Data
ARIMA-model-based decomposition of quarterly and monthly time series data. The methodology is developed and described, among others, in Burman (1980) <DOI:10.2307/2982132> and Hillmer and Tiao (1982) <DOI:10.2307/2287770>.
Maintained by Javier López-de-Lacalle. Last updated 8 years ago.
13.6 match 2.00 scoreaambarek
GiniDecompLY:Gini Decomposition by Income Sources
Estimation of the effect of each income source on income inequalities based on the decomposition of Lerman and Yitzhaki (1985) <doi:10.2307/1928447>.
Maintained by Abdessamad Ait Mbarek. Last updated 4 months ago.
6.8 match 1 stars 4.00 score 3 scriptsfawda123
SWMPr:Retrieving, Organizing, and Analyzing Estuary Monitoring Data
Tools for retrieving, organizing, and analyzing environmental data from the System Wide Monitoring Program of the National Estuarine Research Reserve System <https://cdmo.baruch.sc.edu/>. These tools address common challenges associated with continuous time series data for environmental decision making.
Maintained by Marcus W. Beck. Last updated 1 months ago.
3.8 match 13 stars 7.05 score 143 scripts 1 dependentsropensci
karel:Learning programming with Karel the robot
This is the R implementation of Karel the robot, a programming language created by Dr. R. E. Pattis at Stanford University in 1981. Karel is an useful tool to teach introductory concepts about general programming, such as algorithmic decomposition, conditional statements, loops, etc., in an interactive and fun way, by writing programs to make Karel the robot achieve certain tasks in the world she lives in. Originally based on Pascal, Karel was implemented in many languages through these decades, including 'Java', 'C++', 'Ruby' and 'Python'. This is the first package implementing Karel in R.
Maintained by Marcos Prunello. Last updated 8 months ago.
3.8 match 10 stars 6.87 score 31 scriptsbioc
scran:Methods for Single-Cell RNA-Seq Data Analysis
Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows.
Maintained by Aaron Lun. Last updated 5 months ago.
immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecellclusteringbioconductor-packagehuman-cell-atlassingle-cell-rna-seqopenblascpp
2.0 match 41 stars 13.14 score 7.6k scripts 36 dependentskcha193
infoDecompuTE:Information Decomposition of Two-Phase Experiments
The main purpose of this package is to generate the structure of the analysis of variance (ANOVA) table of the two-phase experiments. The user only need to input the design and the relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, this package can then quickly generate the structure of the ANOVA table with the coefficients of the variance components for the expected mean squares. Thus, the balanced incomplete block design and provides the efficiency factors of the fixed effects can also be studied and compared much easily.
Maintained by Kevin Chang. Last updated 5 years ago.
8.3 match 3.15 score 28 scriptsguido-s
netmeta:Network Meta-Analysis using Frequentist Methods
A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - subgroup network meta-analysis.
Maintained by Guido Schwarzer. Last updated 2 days ago.
meta-analysisnetwork-meta-analysisrstudio
2.2 match 33 stars 11.82 score 199 scripts 10 dependentstimriffe
DemoDecomp:Decompose Demographic Functions
Three general demographic decomposition methods: Pseudo-continuous decomposition proposed by Horiuchi, Wilmoth, and Pletcher (2008) <doi:10.1353/dem.0.0033>, stepwise replacement decomposition proposed by Andreev, Shkolnikov and Begun (2002) <doi:10.4054/DemRes.2002.7.14>, and lifetable response experiments proposed by Caswell (1989) <doi:10.1016/0304-3800(89)90019-7>.
Maintained by Tim Riffe. Last updated 6 months ago.
6.5 match 4 stars 3.99 score 49 scriptscvxgrp
CVXR:Disciplined Convex Optimization
An object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided, both commercial and open source.
Maintained by Anqi Fu. Last updated 4 months ago.
2.0 match 207 stars 12.89 score 768 scripts 51 dependentsnk027
sanic:Solving Ax = b Nimbly in C++
Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported.
Maintained by Nikolas Kuschnig. Last updated 2 years ago.
bicgstabcholeskyconjugate-gradienteigenlinear-equationssolverscpp
6.1 match 9 stars 4.13 score 1 scripts 1 dependentslem-usp
evolqg:Evolutionary Quantitative Genetics
Provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.
Maintained by Diogo Melo. Last updated 11 months ago.
4.0 match 10 stars 6.26 score 114 scriptsbsvars
bsvarSIGNs:Bayesian SVARs with Sign, Zero, and Narrative Restrictions
Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.
Maintained by Xiaolei Wang. Last updated 2 months ago.
bayesian-inferenceeconometricsvector-autoregressionopenblascppopenmp
4.0 match 13 stars 6.21 score 10 scriptsr-hyperspec
hyperSpec:Work with Hyperspectral Data, i.e. Spectra + Meta Information (Spatial, Time, Concentration, ...)
Comfortable ways to work with hyperspectral data sets, i.e. spatially or time-resolved spectra, or spectra with any other kind of information associated with each of the spectra. The spectra can be data as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS, etc. More generally, any data that is recorded over a discretized variable, e.g. absorbance = f(wavelength), stored as a vector of absorbance values for discrete wavelengths is suitable.
Maintained by Claudia Beleites. Last updated 10 months ago.
data-wranglinghyperspectralimaginginfrarednmrramanspectroscopyuv-visxrf
3.0 match 16 stars 8.13 score 233 scripts 2 dependentsbayesiandemography
bage:Bayesian Estimation and Forecasting of Age-Specific Rates
Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on 'Template Model Builder'.
Maintained by John Bryant. Last updated 2 months ago.
3.3 match 3 stars 7.30 score 39 scriptssaviviro
sstvars:Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models
Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.
Maintained by Savi Virolainen. Last updated 16 days ago.
3.8 match 4 stars 6.36 score 41 scriptsbioc
YAPSA:Yet Another Package for Signature Analysis
This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata.
Maintained by Zuguang Gu. Last updated 5 months ago.
sequencingdnaseqsomaticmutationvisualizationclusteringgenomicvariationstatisticalmethodbiologicalquestion
3.8 match 6.41 score 57 scriptshubbardalex
autostsm:Automatic Structural Time Series Models
Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
Maintained by Alex Hubbard. Last updated 9 months ago.
6.7 match 3.55 score 29 scriptscran
VMDML:Variational Mode Decomposition Based Machine Learning Models
Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.
Maintained by Pankaj Das. Last updated 2 years ago.
11.8 match 2.00 score 3 scriptsderekbeaton
ExPosition:Exploratory Analysis with the Singular Value Decomposition
A variety of descriptive multivariate analyses with the singular value decomposition, such as principal components analysis, correspondence analysis, and multidimensional scaling. See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Maintained by Derek Beaton. Last updated 1 years ago.
3.8 match 4 stars 6.20 score 164 scripts 8 dependentsstan-dev
rstanarm:Bayesian Applied Regression Modeling via Stan
Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.
Maintained by Ben Goodrich. Last updated 9 months ago.
bayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticsmultilevel-modelsrstanrstanarmstanstatistical-modelingcpp
1.5 match 393 stars 15.68 score 5.0k scripts 13 dependentsbioc
mixOmics:Omics Data Integration Project
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
Maintained by Eva Hamrud. Last updated 4 days ago.
immunooncologymicroarraysequencingmetabolomicsmetagenomicsproteomicsgenepredictionmultiplecomparisonclassificationregressionbioconductorgenomicsgenomics-datagenomics-visualizationmultivariate-analysismultivariate-statisticsomicsr-pkgr-project
1.7 match 182 stars 13.71 score 1.3k scripts 22 dependentstagteam
riskRegression:Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks
Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.
Maintained by Thomas Alexander Gerds. Last updated 17 days ago.
1.8 match 46 stars 13.00 score 736 scripts 35 dependentsbioc
Informeasure:R implementation of information measures
This package consolidates a comprehensive set of information measurements, encompassing mutual information, conditional mutual information, interaction information, partial information decomposition, and part mutual information.
Maintained by Chu Pan. Last updated 5 months ago.
geneexpressionnetworkinferencenetworksoftware
5.2 match 3 stars 4.48 score 4 scriptsrebeccasalles
TSPred:Functions for Benchmarking Time Series Prediction
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Maintained by Rebecca Pontes Salles. Last updated 4 years ago.
benchmarkinglinear-modelsmachine-learningnonstationaritytime-series-forecasttime-series-prediction
4.2 match 24 stars 5.53 score 94 scripts 1 dependentsjamesramsay5
fda:Functional Data Analysis
These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from <https://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.
Maintained by James Ramsay. Last updated 4 months ago.
1.9 match 3 stars 12.29 score 2.0k scripts 143 dependentsstuart-lab
Signac:Analysis of Single-Cell Chromatin Data
A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart et al. (2021) <doi:10.1038/s41592-021-01282-5>.
Maintained by Tim Stuart. Last updated 7 months ago.
atacbioinformaticssingle-cellzlibcpp
1.9 match 349 stars 12.19 score 3.7k scripts 1 dependentsbiooss
sensitivity:Global Sensitivity Analysis of Model Outputs and Importance Measures
A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), for variable importance measures of data, as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs.
Maintained by Bertrand Iooss. Last updated 7 months ago.
3.4 match 17 stars 6.74 score 472 scripts 8 dependentsalsabtay
ATAforecasting:Automatic Time Series Analysis and Forecasting using the Ata Method
The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.
Maintained by Ali Sabri Taylan. Last updated 2 years ago.
ataataforecastingfableforecastforecastingtime-seriescpp
5.8 match 5 stars 3.88 score 4 scripts 1 dependentsrobertwbuchkowski
soilfoodwebs:Soil Food Web Analysis
Analyzing soil food webs or any food web measured at equilibrium. The package calculates carbon and nitrogen fluxes and stability properties using methods described by Hunt et al. (1987) <doi:10.1007/BF00260580>, de Ruiter et al. (1995) <doi:10.1126/science.269.5228.1257>, Holtkamp et al. (2011) <doi:10.1016/j.soilbio.2010.10.004>, and Buchkowski and Lindo (2021) <doi:10.1111/1365-2435.13706>. The package can also manipulate the structure of the food web as well as simulate food webs away from equilibrium and run decomposition experiments.
Maintained by Robert Buchkowski. Last updated 11 months ago.
5.1 match 5 stars 4.40 score 4 scriptscran
tensorA:Advanced Tensor Arithmetic with Named Indices
Provides convenience functions for advanced linear algebra with tensors and computation with data sets of tensors on a higher level abstraction. It includes Einstein and Riemann summing conventions, dragging, co- and contravariate indices, parallel computations on sequences of tensors.
Maintained by K. Gerald van den Boogaart. Last updated 1 years ago.
3.9 match 5.83 score 399 dependentsbioc
Clomial:Infers clonal composition of a tumor
Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor.
Maintained by Habil Zare. Last updated 5 months ago.
geneticsgeneticvariabilitysequencingclusteringmultiplecomparisonbayesiandnaseqexomeseqtargetedresequencingimmunooncology
5.2 match 4.30 score 3 scriptsasgr
imager:Image Processing Library Based on 'CImg'
Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.
Maintained by Aaron Robotham. Last updated 27 days ago.
1.7 match 17 stars 13.62 score 2.4k scripts 45 dependentshelske
ramcmc:Robust Adaptive Metropolis Algorithm
Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) <doi:10.1007/s11222-011-9269-5>. The package also includes fast functions for rank-one Cholesky update and downdate. These functions can be used directly from R or the corresponding C++ header files can be easily linked to other R packages.
Maintained by Jouni Helske. Last updated 3 years ago.
3.6 match 6 stars 6.21 score 8 scripts 12 dependentsconfig-i1
smooth:Forecasting Using State Space Models
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>), SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).
Maintained by Ivan Svetunkov. Last updated 2 days ago.
arimaarima-forecastingcesetsexponential-smoothingforecaststate-spacetime-seriesopenblascpp
1.9 match 90 stars 11.87 score 412 scripts 25 dependentsstephenhky
RQEntangle:Quantum Entanglement of Bipartite System
It computes the Schmidt decomposition of bipartite quantum systems, discrete or continuous, and their respective entanglement metrics. See Artur Ekert, Peter L. Knight (1995) <doi:10.1119/1.17904> for more details.
Maintained by Kwan-Yuet Ho. Last updated 6 years ago.
numerical-methodsphysicsquantum-computing
4.4 match 6 stars 5.03 score 12 scriptsbioc
mogsa:Multiple omics data integrative clustering and gene set analysis
This package provide a method for doing gene set analysis based on multiple omics data.
Maintained by Chen Meng. Last updated 5 months ago.
geneexpressionprincipalcomponentstatisticalmethodclusteringsoftware
5.1 match 4.29 score 49 scriptshelske
KFAS:Kalman Filter and Smoother for Exponential Family State Space Models
State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Maintained by Jouni Helske. Last updated 6 months ago.
dynamic-linear-modelexponential-familyfortrangaussian-modelsstate-spacetime-seriesopenblas
2.0 match 64 stars 10.97 score 242 scripts 16 dependentsnashjc
optimx:Expanded Replacement and Extension of the 'optim' Function
Provides a replacement and extension of the optim() function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that function 'optimr()' was prepared to simplify the incorporation of minimization codes going forward. Also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here. This is the version for CRAN.
Maintained by John C Nash. Last updated 2 months ago.
1.7 match 2 stars 12.87 score 1.8k scripts 89 dependentsluca-scr
mclust:Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Maintained by Luca Scrucca. Last updated 11 months ago.
1.8 match 21 stars 12.23 score 6.6k scripts 587 dependentsbnosac
udpipe:Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the 'UDPipe' 'NLP' Toolkit
This natural language processing toolkit provides language-agnostic 'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency parsing' of raw text. Next to text parsing, the package also allows you to train annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided at <https://universaldependencies.org/format.html>. The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at <doi:10.18653/v1/K17-3009>. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.
Maintained by Jan Wijffels. Last updated 2 years ago.
conlldependency-parserlemmatizationnatural-language-processingnlppos-taggingr-pkgrcpptext-miningtokenizerudpipecpp
1.8 match 215 stars 11.83 score 1.2k scripts 9 dependentsdselivanov
mlapi:Abstract Classes for Building 'scikit-learn' Like API
Provides 'R6' abstract classes for building machine learning models with 'scikit-learn' like API. <https://scikit-learn.org/> is a popular module for 'Python' programming language which design became de facto a standard in industry for machine learning tasks.
Maintained by Dmitriy Selivanov. Last updated 3 years ago.
3.9 match 5.42 score 5 scripts 26 dependentssubroy13
rsvddpd:Robust Singular Value Decomposition using Density Power Divergence
Computing singular value decomposition with robustness is a challenging task. This package provides an implementation of computing robust SVD using density power divergence (<arXiv:2109.10680>). It combines the idea of robustness and efficiency in estimation based on a tuning parameter. It also provides utility functions to simulate various scenarios to compare performances of different algorithms.
Maintained by Subhrajyoty Roy. Last updated 2 years ago.
5.0 match 3 stars 4.18 score 6 scriptselbersb
segregation:Entropy-Based Segregation Indices
Computes segregation indices, including the Index of Dissimilarity, as well as the information-theoretic indices developed by Theil (1971) <isbn:978-0471858454>, namely the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>, is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. The package also provides a method to decompose differences in segregation as described by Elbers (2021) <doi:10.1177/0049124121986204>. The package includes standard error estimation by bootstrapping, which also corrects for small sample bias. The package also contains functions for visualizing segregation patterns.
Maintained by Benjamin Elbers. Last updated 1 years ago.
entropysegregationstatisticscpp
3.3 match 36 stars 6.44 score 51 scriptsbpfaff
vars:VAR Modelling
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Maintained by Bernhard Pfaff. Last updated 12 months ago.
2.4 match 7 stars 8.68 score 2.8k scripts 44 dependentspixushi
tempted:Temporal Tensor Decomposition, a Dimensionality Reduction Tool for Longitudinal Multivariate Data
TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed.
Maintained by Pixu Shi. Last updated 10 months ago.
5.3 match 14 stars 3.92 score 12 scriptsfacebookexperimental
Robyn:Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science
Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.
Maintained by Gufeng Zhou. Last updated 19 days ago.
adstockingbudget-allocationcost-response-curveeconometricsevolutionary-algorithmgradient-based-optimisationhyperparameter-optimizationmarketing-mix-modelingmarketing-mix-modellingmarketing-sciencemmmridge-regression
2.0 match 1.2k stars 10.32 score 95 scriptsrjdverse
rjd3highfreq:Seasonal Adjustment of High Frequency Data with 'JDemetra+ 3.x'
R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It provides functions for seasonal adjustment of high-frequency data displaying multiple, non integer periodicities. Pre-adjustment with extended airline model and Arima Model Based decomposition.
Maintained by Jean Palate. Last updated 8 months ago.
4.0 match 2 stars 5.15 score 33 scripts 3 dependentsmplex
multiplex:Algebraic Tools for the Analysis of Multiple Social Networks
Algebraic procedures for analyses of multiple social networks are delivered with this package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>. 'multiplex' makes possible, among other things, to create and manipulate multiplex, multimode, and multilevel network data with different formats. Effective ways are available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup with decomposition procedures or semiring structures with the relational bundles occurring in different types of multivariate networks. 'multiplex' provides also an algebraic approach for affiliation networks through Galois derivations between families of the pairs of subsets in the two domains of the network with visualization options.
Maintained by Antonio Rivero Ostoic. Last updated 2 months ago.
algebranetwork-analysissemigroupsemiring
2.5 match 23 stars 8.12 score 69 scripts 2 dependentstsuda16k
materialmodifier:Apply Photo Editing Effects
You can apply image processing effects that modifies the perceived material properties of objects in photos, such as gloss, smoothness, and blemishes. This is an implementation of the algorithm proposed by Boyadzhiev et al. (2015) "Band-Sifting Decomposition for Image Based Material Editing". Documentation and practical tips of the package is available at <https://github.com/tsuda16k/materialmodifier>.
Maintained by Hiroyuki Tsuda. Last updated 2 years ago.
6.1 match 4 stars 3.30 score 3 scriptsnk027
BVAR:Hierarchical Bayesian Vector Autoregression
Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions to compute and identify impulse responses, calculate forecasts, forecast error variance decompositions and scenarios are available. Several methods to print, plot and summarise results facilitate analysis.
Maintained by Nikolas Kuschnig. Last updated 4 months ago.
bayesianbvarforecastsimpulse-responsesvector-autoregressions
2.8 match 51 stars 7.30 score 68 scripts 1 dependentsadeverse
adephylo:Exploratory Analyses for the Phylogenetic Comparative Method
Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa. The package contains functions to represent comparative data, compute phylogenetic proximities, perform multivariate analysis with phylogenetic constraints and test for the presence of phylogenetic autocorrelation. The package is described in Jombart et al (2010) <doi:10.1093/bioinformatics/btq292>.
Maintained by Aurélie Siberchicot. Last updated 2 days ago.
2.0 match 9 stars 10.05 score 312 scripts 4 dependentssaviviro
gmvarkit:Estimate Gaussian and Student's t Mixture Vector Autoregressive Models
Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (2022) <doi:10.48550/arXiv.2109.13648>.
Maintained by Savi Virolainen. Last updated 2 months ago.
3.7 match 3 stars 5.32 score 45 scriptsmatthieustigler
tsDyn:Nonlinear Time Series Models with Regime Switching
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Maintained by Matthieu Stigler. Last updated 5 months ago.
1.9 match 34 stars 10.56 score 684 scripts 3 dependentszilong-li
pcaone:Fast and Accurate Randomized Singular Value Decomposition Algorithms with 'PCAone'
Fast and Accurate Randomized Singular Value Decomposition (RSVD) methods proposed in the 'PCAone' paper by Li (2023) <https://genome.cshlp.org/content/33/9/1599>.
Maintained by Zilong Li. Last updated 9 hours ago.
matrix-factorizationpcarcppeigenrsvdsvdopenblascpp
4.9 match 4 stars 4.00 score 6 scriptsinsightsengineering
rbmi:Reference Based Multiple Imputation
Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular, this package supports deterministic conditional mean imputation and jackknifing as described in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.
Maintained by Isaac Gravestock. Last updated 23 days ago.
2.3 match 18 stars 8.78 score 33 scripts 1 dependentsbioc
destiny:Creates diffusion maps
Create and plot diffusion maps.
Maintained by Philipp Angerer. Last updated 4 months ago.
cellbiologycellbasedassaysclusteringsoftwarevisualizationdiffusion-mapsdimensionality-reductioncpp
1.8 match 81 stars 10.94 score 792 scriptsbbuchsbaum
multivarious:Extensible Data Structures for Multivariate Analysis
Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.
Maintained by Bradley Buchsbaum. Last updated 3 months ago.
5.5 match 3.53 score 17 scriptsnago2020
depCensoring:Statistical Methods for Survival Data with Dependent Censoring
Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023) <doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>, Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Rutten et al. (2024+) <doi:10.48550/arXiv.2403.11860> and Ding and Van Keilegom (2024).
Maintained by Negera Wakgari Deresa. Last updated 10 days ago.
7.0 match 2.78 score 5 scriptsmandymejia
fMRItools:Routines for Common fMRI Processing Tasks
Supports fMRI (functional magnetic resonance imaging) analysis tasks including reading in 'CIFTI', 'GIFTI' and 'NIFTI' data, temporal filtering, nuisance regression, and aCompCor (anatomical Components Correction) (Muschelli et al. (2014) <doi:10.1016/j.neuroimage.2014.03.028>).
Maintained by Amanda Mejia. Last updated 10 days ago.
3.8 match 2 stars 5.10 score 35 scripts 4 dependentssjuhl
spfilteR:Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models
Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.
Maintained by Sebastian Juhl. Last updated 23 days ago.
3.8 match 7 stars 5.15 score 10 scriptsmarce10
warbleR:Streamline Bioacoustic Analysis
Functions aiming to facilitate the analysis of the structure of animal acoustic signals in 'R'. 'warbleR' makes use of the basic sound analysis tools from the packages 'tuneR' and 'seewave', and offers new tools for explore and quantify acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals.
Maintained by Marcelo Araya-Salas. Last updated 2 months ago.
animal-acoustic-signalsaudio-processingbioacousticsspectrogramstreamline-analysiscpp
1.8 match 54 stars 11.01 score 270 scripts 4 dependentscran
oaxaca:Blinder-Oaxaca Decomposition
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Maintained by Marek Hlavac. Last updated 3 years ago.
7.7 match 3 stars 2.48 scoreduct317
scDHA:Single-Cell Decomposition using Hierarchical Autoencoder
Provides a fast and accurate pipeline for single-cell analyses. The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).
Maintained by Ha Nguyen. Last updated 11 months ago.
2.9 match 40 stars 6.38 score 20 scripts 2 dependentsericaponzi
RaJIVE:Robust Angle Based Joint and Individual Variation Explained
A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.
Maintained by Erica Ponzi. Last updated 4 years ago.
6.9 match 2.70 score 1 scriptstrnnick
tsutils:Time Series Exploration, Modelling and Forecasting
Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.
Maintained by Nikolaos Kourentzes. Last updated 1 years ago.
2.4 match 12 stars 7.78 score 472 scripts 18 dependentstrnnick
tsintermittent:Intermittent Time Series Forecasting
Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) <doi:10.1016/j.ijpe.2014.06.007>). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) <doi:10.1057/jors.2014.62>).
Maintained by Nikolaos Kourentzes. Last updated 3 years ago.
4.0 match 14 stars 4.53 score 48 scriptsbioc
MsCoreUtils:Core Utils for Mass Spectrometry Data
MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages.
Maintained by RforMassSpectrometry Package Maintainer. Last updated 4 days ago.
infrastructureproteomicsmassspectrometrymetabolomicsbioconductormass-spectrometryutils
1.7 match 16 stars 10.52 score 41 scripts 71 dependentskbroman
broman:Karl Broman's R Code
Miscellaneous R functions, including functions related to graphics (mostly for base graphics), permutation tests, running mean/median, and general utilities.
Maintained by Karl W Broman. Last updated 10 months ago.
2.0 match 183 stars 8.80 score 648 scripts 1 dependentsjsspaulding
rcrimeanalysis:An Implementation of Crime Analysis Methods
An implementation of functions for the analysis of crime incident or records management system data. The package implements analysis algorithms scaled for city or regional crime analysis units. The package provides functions for kernel density estimation for crime heat maps, geocoding using the 'Google Maps' API, identification of repeat crime incidents, spatio-temporal map comparison across time intervals, time series analysis (forecasting and decomposition), detection of optimal parameters for the identification of near repeat incidents, and near repeat analysis with crime network linkage.
Maintained by Jamie Spaulding. Last updated 2 years ago.
4.0 match 5 stars 4.40 score 5 scriptscran
unbalhaar:Function Estimation via Unbalanced Haar Wavelets
Top-down and bottom-up algorithms for nonparametric function estimation in Gaussian noise using Unbalanced Haar wavelets.
Maintained by Piotr Fryzlewicz. Last updated 3 years ago.
10.7 match 1.62 score 14 scripts 1 dependentsbozenne
lavaSearch2:Tools for Model Specification in the Latent Variable Framework
Tools for model specification in the latent variable framework (add-on to the 'lava' package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.
Maintained by Brice Ozenne. Last updated 8 months ago.
inferencelatent-variable-modelsstatisticsopenblascpp
3.8 match 4.55 score 155 scriptsadeverse
adegraphics:An S4 Lattice-Based Package for the Representation of Multivariate Data
Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the 'ade4' package.
Maintained by Aurélie Siberchicot. Last updated 8 months ago.
1.7 match 9 stars 10.37 score 386 scripts 6 dependentsfaosorios
fastmatrix:Fast Computation of some Matrices Useful in Statistics
Small set of functions to fast computation of some matrices and operations useful in statistics and econometrics. Currently, there are functions for efficient computation of duplication, commutation and symmetrizer matrices with minimal storage requirements. Some commonly used matrix decompositions (LU and LDL), basic matrix operations (for instance, Hadamard, Kronecker products and the Sherman-Morrison formula) and iterative solvers for linear systems are also available. In addition, the package includes a number of common statistical procedures such as the sweep operator, weighted mean and covariance matrix using an online algorithm, linear regression (using Cholesky, QR, SVD, sweep operator and conjugate gradients methods), ridge regression (with optimal selection of the ridge parameter considering several procedures), omnibus tests for univariate normality, functions to compute the multivariate skewness, kurtosis, the Mahalanobis distance (checking the positive defineteness), and the Wilson-Hilferty transformation of gamma variables. Furthermore, the package provides interfaces to C code callable by another C code from other R packages.
Maintained by Felipe Osorio. Last updated 1 years ago.
commutation-matrixjarque-bera-testldl-factorizationlu-factorizationmatrix-api-for-r-packagesmatrix-normsmodified-choleskyols-regressionpower-methodridge-regressionsherman-morrisonstatisticssweep-operatorsymmetrizer-matrixfortranopenblas
2.8 match 19 stars 6.27 score 37 scripts 10 dependentsmlcollyer
RRPP:Linear Model Evaluation with Randomized Residuals in a Permutation Procedure
Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.
Maintained by Michael Collyer. Last updated 26 days ago.
1.8 match 4 stars 9.84 score 173 scripts 7 dependentsmarcinjurek
GPvecchia:Scalable Gaussian-Process Approximations
Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.
Maintained by Marcin Jurek. Last updated 1 years ago.
4.0 match 4.26 score 61 scripts 2 dependentsr-gregmisc
gmodels:Various R Programming Tools for Model Fitting
Various R programming tools for model fitting.
Maintained by Gregory R. Warnes. Last updated 3 months ago.
1.7 match 1 stars 10.01 score 3.5k scripts 30 dependentsrqtl
qtl2:Quantitative Trait Locus Mapping in Experimental Crosses
Provides a set of tools to perform quantitative trait locus (QTL) analysis in experimental crosses. It is a reimplementation of the 'R/qtl' package to better handle high-dimensional data and complex cross designs. Broman et al. (2019) <doi:10.1534/genetics.118.301595>.
Maintained by Karl W Broman. Last updated 8 days ago.
1.8 match 34 stars 9.48 score 1.1k scripts 5 dependents