Showing 200 of total 550 results (show query)
kwstat
agridat:Agricultural Datasets
Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.
Maintained by Kevin Wright. Last updated 28 days ago.
440.9 match 125 stars 11.02 score 1.7k scripts 2 dependents
stla
uniformly:Uniform Sampling
Uniform sampling on various geometric shapes, such as spheres, ellipsoids, simplices.
Maintained by Stéphane Laurent. Last updated 2 years ago.
86.8 match 10 stars 6.21 score 109 scripts 1 dependents
egarpor
sphunif:Uniformity Tests on the Circle, Sphere, and Hypersphere
Implementation of uniformity tests on the circle and (hyper)sphere. The main function of the package is unif_test(), which conveniently collects more than 35 tests for assessing uniformity on S^{p-1} = {x in R^p : ||x|| = 1}, p >= 2. The test statistics are implemented in the unif_stat() function, which allows computing several statistics for different samples within a single call, thus facilitating Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows parallelizing them in a simple way. The asymptotic null distributions of the statistics are available through the function unif_stat_distr(). The core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package. The package also provides several novel datasets and gives the replicability for the data applications/simulations in García-Portugués et al. (2021) <doi:10.1007/978-3-030-69944-4_12>, García-Portugués et al. (2023) <doi:10.3150/21-BEJ1454>, García-Portugués et al. (2024) <doi:10.48550/arXiv.2108.09874>, and Fernández-de-Marcos and García-Portugués (2024) <doi:10.48550/arXiv.2405.13531>.
Maintained by Eduardo García-Portugués. Last updated 10 months ago.
circular-statisticsdirectional-statisticsuniformityuniformity-testsopenblascpp
57.5 match 8 stars 6.11 score 18 scripts 2 dependents
paulnorthrop
rust:Ratio-of-Uniforms Simulation with Transformation
Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.
Maintained by Paul J. Northrop. Last updated 7 months ago.
1977bayesian-inferencekindermanmonahanofposterior-samplesratioratio-of-uniformsratio-of-uniforms-methodrcppsimulationtransformationuniformsopenblascpp
39.4 match 7.13 score 36 scripts 7 dependents
elvanceyhan
pcds:Proximity Catch Digraphs and Their Applications
Contains the functions for construction and visualization of various families of the proximity catch digraphs (PCDs) (see (Ceyhan (2005) ISBN:978-3-639-19063-2), for computing the graph invariants for testing the patterns of segregation and association against complete spatial randomness (CSR) or uniformity in one, two and three dimensional cases. The package also has tools for generating points from these spatial patterns. The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011) <doi:10.1080/03610921003597211>) and arc density (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>; Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). The PCD families considered are Arc-Slice PCDs, Proportional-Edge PCDs, and Central Similarity PCDs.
Maintained by Elvan Ceyhan. Last updated 2 years ago.
36.2 match 5.80 score 21 scripts 2 dependents
rstudio
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.
14.1 match 845 stars 13.57 score 264 scripts 2 dependents
alexpghayes
distributions3:Probability Distributions as S3 Objects
Tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.
Maintained by Alex Hayes. Last updated 6 months ago.
14.0 match 102 stars 11.35 score 118 scripts 7 dependents
igraph
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 13 hours ago.
complex-networksgraph-algorithmsgraph-theorymathematicsnetwork-analysisnetwork-graphfortranlibxml2glpkopenblascpp
7.2 match 582 stars 21.11 score 31k scripts 1.9k dependents
t-kalinowski
keras: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 11 months ago.
12.0 match 10.82 score 10k scripts 54 dependents
cran
randomUniformForest:Random Uniform Forests for Classification, Regression and Unsupervised Learning
Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.
Maintained by Saip Ciss. Last updated 3 years ago.
30.8 match 3 stars 3.77 score 99 scripts
azure
azuremlsdk:Interface to the 'Azure Machine Learning' 'SDK'
Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.
Maintained by Diondra Peck. Last updated 3 years ago.
amlcomputeazureazure-machine-learningazuremldsimachine-learningrstudiosdk-r
12.3 match 106 stars 8.91 score 221 scripts
mlverse
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.
6.3 match 520 stars 16.52 score 1.4k scripts 38 dependents
coatless-rpkg
sitmo:Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' Header Files
Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence. The objective of this library to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and 'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the header files inside of 'sitmo' instead of including a copy of each engine within their individual package. Lastly, the package contains example implementations using the 'sitmo' package and three accompanying vignette that provide additional information.
Maintained by James Balamuta. Last updated 1 years ago.
parallelrandom-generationrcppcppopenmp
10.3 match 7 stars 9.75 score 15 scripts 201 dependents
spatstat
spatstat.random:Random Generation Functionality for the 'spatstat' Family
Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.
Maintained by Adrian Baddeley. Last updated 2 days ago.
point-processesrandom-generationsimulationspatial-samplingspatial-simulationcpp
8.7 match 5 stars 10.82 score 84 scripts 175 dependents
statnet
ergm:Fit, Simulate and Diagnose Exponential-Family Models for Networks
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i03> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
Maintained by Pavel N. Krivitsky. Last updated 7 days ago.
5.9 match 100 stars 15.36 score 1.4k scripts 36 dependentscran
circular:Circular Statistics
Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Maintained by Eduardo García-Portugués. Last updated 7 months ago.
11.1 match 7 stars 7.76 score 1.1k scripts 40 dependents
tkonopka
umap:Uniform Manifold Approximation and Projection
Uniform manifold approximation and projection is a technique for dimension reduction. The algorithm was described by McInnes and Healy (2018) in <arXiv:1802.03426>. This package provides an interface for two implementations. One is written from scratch, including components for nearest-neighbor search and for embedding. The second implementation is a wrapper for 'python' package 'umap-learn' (requires separate installation, see vignette for more details).
Maintained by Tomasz Konopka. Last updated 11 months ago.
dimensionality-reductionumapcpp
6.6 match 132 stars 12.74 score 3.6k scripts 43 dependentscran
noisemodel:Noise Models for Classification Datasets
Implementation of models for the controlled introduction of errors in classification datasets. This package contains the noise models described in Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, attributes and both simultaneously.
Maintained by José A. Sáez. Last updated 2 years ago.
37.9 match 2.00 scorecoatless-rpkg
cetcolor:CET Perceptually Uniform Colour Maps
Collection of perceptually uniform colour maps made by Peter Kovesi (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> at the Centre for Exploration Targeting (CET).
Maintained by James Balamuta. Last updated 1 years ago.
11.7 match 31 stars 6.08 score 78 scripts
paulnorthrop
revdbayes:Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis
Provides functions for the Bayesian analysis of extreme value models. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.
Maintained by Paul J. Northrop. Last updated 7 months ago.
analysisbayesianextremeextreme-value-statisticsextremesgeneralized-pareto-distributiongevinferencenhpppoint-processposteriorpredictivercppvalueopenblascpp
8.7 match 4 stars 7.62 score 58 scripts 4 dependents
ropensci
beautier:'BEAUti' from R
'BEAST2' (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAUti 2' (which is part of 'BEAST2') is a GUI tool that allows users to specify the many possible setups and generates the XML file 'BEAST2' needs to run. This package provides a way to create 'BEAST2' input files without active user input, but using R function calls instead.
Maintained by Richèl J.C. Bilderbeek. Last updated 23 days ago.
bayesianbeastbeast2beautiphylogenetic-inferencephylogenetics
7.4 match 13 stars 8.76 score 198 scripts 5 dependents
unuran
Runuran:R Interface to the 'UNU.RAN' Random Variate Generators
Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions.
Maintained by Josef Leydold. Last updated 5 months ago.
9.1 match 6.87 score 180 scripts 8 dependentskwstat
pals:Color Palettes, Colormaps, and Tools to Evaluate Them
A comprehensive collection of color palettes, colormaps, and tools to evaluate them. See Kovesi (2015) <doi:10.48550/arXiv.1509.03700>.
Maintained by Kevin Wright. Last updated 9 days ago.
5.5 match 83 stars 11.39 score 2.1k scripts 8 dependents
merliseclyde
BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Maintained by Merlise Clyde. Last updated 4 months ago.
bayesianbayesian-inferencegeneralized-linear-modelslinear-regressionlogistic-regressionmcmcmodel-selectionpoisson-regressionpredictive-modelingregressionvariable-selectionfortranopenblas
5.7 match 44 stars 10.81 score 420 scripts 3 dependents
bayesball
LearnBayes:Learning Bayesian Inference
Contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Maintained by Jim Albert. Last updated 7 years ago.
5.2 match 38 stars 11.34 score 690 scripts 31 dependents
florianhartig
DHARMa:Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models
The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive', and 'spaMM'; phylogenetic linear models from 'phylolm' (classes 'phylolm' and 'phyloglm'); generalized additive models ('gam' from 'mgcv'); 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation.
Maintained by Florian Hartig. Last updated 12 days ago.
glmmregressionregression-diagnosticsresidual
3.8 match 226 stars 14.74 score 2.8k scripts 10 dependents
jlmelville
uwot:The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction
An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) <doi:10.48550/arXiv.1802.03426>. It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) <doi:10.48550/arXiv.1602.00370> is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website (<https://github.com/jlmelville/uwot>) for more documentation and examples.
Maintained by James Melville. Last updated 20 days ago.
dimensionality-reductionumapcpp
3.3 match 328 stars 15.74 score 2.0k scripts 140 dependents
tobiste
tectonicr:Analyzing the Orientation of Maximum Horizontal Stress
Models the direction of the maximum horizontal stress using relative plate motion parameters. Statistical algorithms to evaluate the modeling results compared with the observed data. Provides plots to visualize the results. Methods described in Stephan et al. (2023) <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998) <doi:10.1016/S0079-1946(98)00091-3>.
Maintained by Tobias Stephan. Last updated 14 days ago.
geologystructural-geologytectonics
7.2 match 7 stars 7.26 score 33 scriptsropensci
QuadratiK:Collection of Methods Constructed using Kernel-Based Quadratic Distances
It includes test for multivariate normality, test for uniformity on the d-dimensional Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson kernel-based density and clustering algorithm for spherical data. For more information see Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024) <doi:10.48550/arXiv.2402.02290> Markatou, M. and Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, Ding, Y., Markatou, M. and Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, and Golzy, M. and Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.
Maintained by Giovanni Saraceno. Last updated 1 months ago.
8.2 match 1 stars 6.36 score 27 scripts
collinerickson
comparer:Compare Output and Run Time
Quickly run experiments to compare the run time and output of code blocks. The function mbc() can make fast comparisons of code, and will calculate statistics comparing the resulting outputs. It can be used to compare model fits to the same data or see which function runs faster. The R6 class ffexp$new() runs a function using all possible combinations of selected inputs. This is useful for comparing the effect of different parameter values. It can also run in parallel and automatically save intermediate results, which is very useful for long computations.
Maintained by Collin Erickson. Last updated 5 months ago.
9.4 match 4 stars 5.38 score 20 scripts
epinowcast
primarycensored:Primary Event Censored Distributions
Provides functions for working with primary event censored distributions and 'Stan' implementations for use in Bayesian modeling. Primary event censored distributions are useful for modeling delayed reporting scenarios in epidemiology and other fields (Charniga et al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for arbitrary delay distributions, a range of common primary distributions, and allows for truncation and secondary event censoring to be accounted for (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of common distributions also have analytical solutions implemented, allowing for faster computation. In addition, it provides multiple methods for fitting primary event censored distributions to data via optional dependencies.
Maintained by Sam Abbott. Last updated 1 months ago.
censoringdistributionsmc-stantruncation
6.5 match 8 stars 7.69 score 16 scripts 1 dependents
gertvv
hitandrun:"Hit and Run" and "Shake and Bake" for Sampling Uniformly from Convex Shapes
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) <doi:10.1016/j.ejor.2012.08.026>. van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) <doi:10.1016/j.ejor.2014.06.036>.
Maintained by Gert van Valkenhoef. Last updated 3 years ago.
7.2 match 16 stars 6.92 score 121 scripts 9 dependents
tdaverse
tdaunif:Uniform Manifold Samplers for Topological Data Analysis
Uniform random samples from simple manifolds, sometimes with noise, are commonly used to test topological data analytic (TDA) tools. This package includes samplers powered by two techniques: analytic volume-preserving parameterizations, as employed by Arvo (1995) <doi:10.1145/218380.218500>, and rejection sampling, as employed by Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.
Maintained by Jason Cory Brunson. Last updated 9 months ago.
manifoldssamplertdatopological-data-analysistopological-statistics
9.9 match 3 stars 4.95 score 8 scripts
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
5.3 match 9 stars 9.14 score 1 scripts 16 dependents
alexkowa
EnvStats:Package for Environmental Statistics, Including US EPA Guidance
Graphical and statistical analyses of environmental data, with focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. Major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. Numerous built-in data sets from regulatory guidance documents and environmental statistics literature. Includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, <doi:10.1007/978-1-4614-8456-1>).
Maintained by Alexander Kowarik. Last updated 17 days ago.
3.8 match 26 stars 12.80 score 2.4k scripts 46 dependents
gjmvanboxtel
gsignal:Signal Processing
R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.
Maintained by Geert van Boxtel. Last updated 2 months ago.
4.5 match 24 stars 10.03 score 133 scripts 34 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.
5.2 match 54 stars 8.63 score 221 scripts 3 dependents
bioc
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
9.9 match 3 stars 4.48 score 4 scriptsbioc
COTAN:COexpression Tables ANalysis
Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes.
Maintained by Galfrè Silvia Giulia. Last updated 19 days ago.
systemsbiologytranscriptomicsgeneexpressionsinglecell
5.5 match 16 stars 7.88 score 96 scripts
spsanderson
TidyDensity:Functions for Tidy Analysis and Generation of Random Data
To make it easy to generate random numbers based upon the underlying stats distribution functions. All data is returned in a tidy and structured format making working with the data simple and straight forward. Given that the data is returned in a tidy 'tibble' it lends itself to working with the rest of the 'tidyverse'.
Maintained by Steven Sanderson. Last updated 5 months ago.
bootstrapdensitydistributionsggplot2probabilityr-languagesimulationstatisticstibbletidy
5.5 match 34 stars 7.78 score 66 scripts 1 dependents
amalan-constat
fitODBOD:Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD
Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>), also Journal article to use the package(<doi:10.21105/joss.01505>).
Maintained by Amalan Mahendran. Last updated 4 months ago.
binomial-outcome-dataoverdispersion
9.4 match 1 stars 4.44 score 139 scripts
kisungyou
Riemann:Learning with Data on Riemannian Manifolds
We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.
Maintained by Kisung You. Last updated 2 years ago.
11.2 match 10 stars 3.70 score 8 scripts
greta-dev
greta:Simple and Scalable Statistical Modelling in R
Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.
Maintained by Nicholas Tierney. Last updated 6 days ago.
3.3 match 566 stars 12.53 score 396 scripts 6 dependents
jkim82133
TDA:Statistical Tools for Topological Data Analysis
Tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements methods from Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2015) <doi:10.20382/jocg.v6i2a8> for analyzing the statistical significance of persistent homology features.
Maintained by Jisu Kim. Last updated 1 months ago.
5.7 match 9 stars 7.18 score 204 scripts 5 dependentsspatstat
spatstat.data:Datasets for 'spatstat' Family
Contains all the datasets for the 'spatstat' family of packages.
Maintained by Adrian Baddeley. Last updated 19 hours ago.
kernel-densitypoint-processspatial-analysisspatial-dataspatial-data-analysisspatstatstatistical-analysisstatistical-methodsstatistical-testsstatistics
3.6 match 6 stars 11.00 score 186 scripts 228 dependentsbioc
MLInterfaces:Uniform interfaces to R machine learning procedures for data in Bioconductor containers
This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers.
Maintained by Vincent Carey. Last updated 5 months ago.
5.1 match 7.63 score 79 scripts 6 dependents
trevorld
gridpattern:'grid' Pattern Grobs
Provides 'grid' grobs that fill in a user-defined area with various patterns. Includes enhanced versions of the geometric and image-based patterns originally contained in the 'ggpattern' package as well as original 'pch', 'polygon_tiling', 'regular_polygon', 'rose', 'text', 'wave', and 'weave' patterns plus support for custom user-defined patterns.
Maintained by Trevor L. Davis. Last updated 1 months ago.
4.6 match 33 stars 8.42 score 4 scripts 4 dependentskisungyou
SHT:Statistical Hypothesis Testing Toolbox
We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.
Maintained by Kisung You. Last updated 19 days ago.
7.5 match 6 stars 5.13 score 50 scripts 1 dependents
debruine
faux:Simulation for Factorial Designs
Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://debruine.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.
Maintained by Lisa DeBruine. Last updated 2 months ago.
4.0 match 98 stars 9.35 score 716 scripts 1 dependents
spedygiorgio
mbbefd:Maxwell Boltzmann Bose Einstein Fermi Dirac Distribution and Destruction Rate Modelling
Distributions that are typically used for exposure rating in general insurance, in particular to price reinsurance contracts. The vignette shows code snippets to fit the distribution to empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208> freely available on-line.
Maintained by Christophe Dutang. Last updated 22 days ago.
actuarialdestruction-rate-modelingreinsurancecpp
5.2 match 15 stars 7.05 score 99 scripts
asael697
bayesforecast:Bayesian Time Series Modeling with Stan
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.
bayesian-inferenceforecasting-modelsmcmcstantime-series-analysiscpp
5.2 match 45 stars 6.92 score 62 scripts
claudioagostinelli
CircStats:Circular Statistics, from "Topics in Circular Statistics" (2001)
Circular Statistics, from "Topics in Circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Maintained by Claudio Agostinelli. Last updated 7 years ago.
5.3 match 2 stars 6.60 score 261 scripts 36 dependents
jenniniku
gllvm:Generalized Linear Latent Variable Models
Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).
Maintained by Jenni Niku. Last updated 12 hours ago.
3.3 match 52 stars 10.53 score 176 scripts 1 dependents
bjoelle
FossilSim:Simulation and Plots for Fossil and Taxonomy Data
Simulating and plotting taxonomy and fossil data on phylogenetic trees under mechanistic models of speciation, preservation and sampling.
Maintained by Joelle Barido-Sottani. Last updated 6 months ago.
6.6 match 1 stars 5.24 score 65 scripts 1 dependentsbioc
BioNet:Routines for the functional analysis of biological networks
This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork.
Maintained by Marcus Dittrich. Last updated 5 months ago.
microarraydataimportgraphandnetworknetworknetworkenrichmentgeneexpressiondifferentialexpression
5.6 match 6.14 score 114 scripts 2 dependents
jolars
eulerr:Area-Proportional Euler and Venn Diagrams with Ellipses
Generate area-proportional Euler diagrams using numerical optimization. An Euler diagram is a generalization of a Venn diagram, relaxing the criterion that all interactions need to be represented. Diagrams may be fit with ellipses and circles via a wide range of inputs and can be visualized in numerous ways.
Maintained by Johan Larsson. Last updated 12 months ago.
euler-diagramvenn-diagramopenblascpp
2.8 match 131 stars 12.03 score 1.2k scripts 5 dependents
mlaib
PGM2:Nested Resolvable Designs and their Associated Uniform Designs
Construction method of nested resolvable designs from a projective geometry defined on Galois field of order 2. The obtained Resolvable designs are used to build uniform design. The presented results are based on <https://eudml.org/doc/219563> and A. Boudraa et al. (See references).
Maintained by Mohamed Laib. Last updated 7 years ago.
12.4 match 2.70 score 6 scriptsrstudio
bslib:Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown'
Simplifies custom 'CSS' styling of both 'shiny' and 'rmarkdown' via 'Bootstrap' 'Sass'. Supports 'Bootstrap' 3, 4 and 5 as well as their various 'Bootswatch' themes. An interactive widget is also provided for previewing themes in real time.
Maintained by Carson Sievert. Last updated 11 days ago.
bootstraphtmltoolsrmarkdownsassshiny
1.8 match 511 stars 18.02 score 5.1k scripts 4.3k dependents
jakobbossek
ecr:Evolutionary Computation in R
Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.
Maintained by Jakob Bossek. Last updated 1 years ago.
combinatorial-optimizationevolutionary-algorithmevolutionary-algorithmsevolutionary-strategygenetic-algorithm-frameworkmetaheuristicsmulti-objective-optimizationoptimizationoptimization-frameworkcpp
4.3 match 43 stars 7.36 score 89 scripts 2 dependents
mlampros
OpenImageR:An Image Processing Toolkit
Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
Maintained by Lampros Mouselimis. Last updated 2 years ago.
filteringgabor-feature-extractiongabor-filtershog-featuresimageimage-hashingprocessingrcpparmadillorecognitionslicslicosuperpixelsopenblascppopenmp
3.1 match 60 stars 9.86 score 358 scripts 8 dependents
radiant-rstats
radiant.basics:Basics Menu for Radiant: Business Analytics using R and Shiny
The Radiant Basics menu includes interfaces for probability calculation, central limit theorem simulation, comparing means and proportions, goodness-of-fit testing, cross-tabs, and correlation. The application extends the functionality in 'radiant.data'.
Maintained by Vincent Nijs. Last updated 10 months ago.
5.5 match 8 stars 5.56 score 79 scripts 3 dependents
mitchelloharawild
distributional:Vectorised Probability Distributions
Vectorised distribution objects with tools for manipulating, visualising, and using probability distributions. Designed to allow model prediction outputs to return distributions rather than their parameters, allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.
Maintained by Mitchell OHara-Wild. Last updated 2 months ago.
probability-distributionstatisticsvctrs
2.3 match 101 stars 13.50 score 744 scripts 384 dependents
easystats
performance:Assessment of Regression Models Performance
Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.
Maintained by Daniel Lüdecke. Last updated 19 days ago.
aiceasystatshacktoberfestloomachine-learningmixed-modelsmodelsperformancer2statistics
1.9 match 1.1k stars 16.17 score 4.3k scripts 47 dependents
kwb-r
kwb.qmra:QMRA (quantitative microbial risk assessment)
QMRA for water supply systems.
Maintained by Michael Rustler. Last updated 4 years ago.
project-aquanesproject-demowareproject-smartcontrolqmraqmra-webapp-backend-engine
6.5 match 4 stars 4.53 score 21 scripts
mrc-ide
monty:Monte Carlo Models
Experimental sources for the next generation of mcstate, now called 'monty', which will support much of the old mcstate functionality but new things like better parameter interfaces, Hamiltonian Monte Carlo, and other features.
Maintained by Rich FitzJohn. Last updated 1 months ago.
3.9 match 3 stars 7.52 score 29 scripts 3 dependents
twolodzko
extraDistr:Additional Univariate and Multivariate Distributions
Density, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson.
Maintained by Tymoteusz Wolodzko. Last updated 11 days ago.
c-plus-plusc-plus-plus-11distributionmultivariate-distributionsprobabilityrandom-generationrcppstatisticscpp
2.5 match 53 stars 11.60 score 1.5k scripts 107 dependents
connordonegan
geostan:Bayesian Spatial Analysis
For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
Maintained by Connor Donegan. Last updated 3 months ago.
bayesianbayesian-inferencebayesian-statisticsepidemiologymodelingpublic-healthrspatialspatialstancpp
3.3 match 80 stars 8.80 score 46 scripts
kkholst
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
2.3 match 33 stars 12.85 score 610 scripts 476 dependents
pbiecek
ddst:Data Driven Smooth Tests
Smooth tests are data driven (alternative hypothesis is dynamically selected based on data). In this package you will find two groups of smooth of test: goodness-of-fit tests and nonparametric tests for comparing distributions. Among goodness-of-fit tests there are tests for exponent, Gaussian, Gumbel and uniform distribution. Among nonparametric tests there are tests for stochastic dominance, k-sample test, test with umbrella alternatives and test for change-point problems.
Maintained by Przemyslaw Biecek. Last updated 2 years ago.
data-drivensmooth-teststatisticstest
5.5 match 6 stars 5.26 score 6 scripts 2 dependents
jonasmoss
univariateML:Maximum Likelihood Estimation for Univariate Densities
User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.
Maintained by Jonas Moss. Last updated 14 days ago.
densityestimationmaximum-likelihood
3.5 match 8 stars 8.10 score 62 scripts 7 dependents
liyabera
EngrEcon:Engineering Economics Analysis for Engineering Projects Cost Analysis
Computing economic analysis in civil infrastructure and ecosystem restoration projects is a typical activity. This package contains Standard cost engineering and engineering economics methods that are applied to convert between present, future, and annualized costs. Newnan D. (2020) <ISBN 9780190931919> “Engineering Economic Analysis”.
Maintained by Liya Abera. Last updated 12 months ago.
16.7 match 1.70 score 1 scripts
muschellij2
freesurfer:Wrapper Functions for 'Freesurfer'
Wrapper functions that interface with 'Freesurfer' <https://surfer.nmr.mgh.harvard.edu/>, a powerful and commonly-used 'neuroimaging' software, using system commands. The goal is to be able to interface with 'Freesurfer' completely in R, where you pass R objects of class 'nifti', implemented by package 'oro.nifti', and the function executes an 'Freesurfer' command and returns an R object of class 'nifti' or necessary output.
Maintained by John Muschelli. Last updated 3 months ago.
3.7 match 10 stars 7.69 score 55 scripts 1 dependents
tomasfryda
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 dependents
chelbert
DiceDesign:Designs of Computer Experiments
Space-Filling Designs and space-filling criteria (distance-based and uniformity-based), with emphasis to computer experiments; <doi:10.18637/jss.v065.i11>.
Maintained by Celine Helbert. Last updated 1 years ago.
4.5 match 6.06 score 231 scripts 64 dependents
asgr
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.
2.0 match 17 stars 13.62 score 2.4k scripts 45 dependents
egenn
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
3.8 match 145 stars 7.09 score 50 scripts 2 dependents
dsy109
tolerance:Statistical Tolerance Intervals and Regions
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence. This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals. Visualizations are also available for most of these settings.
Maintained by Derek S. Young. Last updated 9 months ago.
4.2 match 4 stars 6.39 score 153 scripts 7 dependents
paulojus
geoR:Analysis of Geostatistical Data
Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.
Maintained by Paulo Justiniano Ribeiro Jr. Last updated 1 years ago.
3.5 match 10 stars 7.57 score 1.8k scripts 12 dependents
calvagone
campsis:Generic PK/PD Simulation Platform CAMPSIS
A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R packages 'rxode2' and 'mrgsolve'. CAMPSIS provides an abstraction layer over the underlying processes of writing a PK/PD model, assembling a custom dataset and running a simulation. CAMPSIS has a strong dependency to the R package 'campsismod', which allows to read/write a model from/to files and adapt it further on the fly in the R environment. Package 'campsis' allows the user to assemble a dataset in an intuitive manner. Once the user’s dataset is ready, the package is in charge of preparing the simulation, calling 'rxode2' or 'mrgsolve' (at the user's choice) and returning the results, for the given model, dataset and desired simulation settings.
Maintained by Nicolas Luyckx. Last updated 1 months ago.
3.5 match 8 stars 7.52 score 93 scripts
ericarcher
swfscMisc:Miscellaneous Functions for Southwest Fisheries Science Center
Collection of conversion, analytical, geodesic, mapping, and plotting functions. Used to support packages and code written by researchers at the Southwest Fisheries Science Center of the National Oceanic and Atmospheric Administration.
Maintained by Eric Archer. Last updated 11 months ago.
4.3 match 2 stars 6.18 score 101 scripts 20 dependents
biodiverse
ubms:Bayesian Models for Data from Unmarked Animals using 'Stan'
Fit Bayesian hierarchical models of animal abundance and occurrence via the 'rstan' package, the R interface to the 'Stan' C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Maintained by Ken Kellner. Last updated 18 days ago.
distance-samplinghierarchical-modelsn-mixture-modeloccupancystanopenblascpp
3.3 match 35 stars 7.88 score 73 scripts
nimble-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
2.0 match 169 stars 12.97 score 2.6k scripts 19 dependents
tidymodels
yardstick:Tidy Characterizations of Model Performance
Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).
Maintained by Emil Hvitfeldt. Last updated 4 days ago.
1.7 match 387 stars 15.47 score 2.2k scripts 60 dependents
zejiang-unsw
synthesis:Generate Synthetic Data from Statistical Models
Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.
Maintained by Ze Jiang. Last updated 9 months ago.
5.6 match 3 stars 4.56 score 12 scripts
sparklyr
sparklyr:R Interface to Apache Spark
R interface to Apache Spark, a fast and general engine for big data processing, see <https://spark.apache.org/>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
Maintained by Edgar Ruiz. Last updated 10 days ago.
apache-sparkdistributeddplyridelivymachine-learningremote-clusterssparksparklyr
1.7 match 959 stars 15.16 score 4.0k scripts 21 dependents
hrbrmstr
statebins:Create United States Uniform Cartogram Heatmaps
The cartogram heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on "The states most threatened by trade" (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). "State bins" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.
Maintained by Bob Rudis. Last updated 5 years ago.
4.7 match 5.42 score 354 scripts 5 dependentshrbrmstr
ggalt:Extra Coordinate Systems, 'Geoms', Statistical Transformations, Scales and Fonts for 'ggplot2'
A compendium of new geometries, coordinate systems, statistical transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities, univariate average shifted histograms, a new map coordinate system based on the 'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of geom_map(), formatters for "bytes", a stat_stepribbon() function, increased 'plotly' compatibility and the 'StateFace' open source font 'ProPublica'. Further new functionality includes lollipop charts, dumbbell charts, the ability to encircle points and coordinate-system-based text annotations.
Maintained by Bob Rudis. Last updated 2 years ago.
geomggplot-extensionggplot2ggplot2-geomggplot2-scales
2.0 match 674 stars 12.59 score 2.3k scripts 7 dependents
bertcarnell
lhs:Latin Hypercube Samples
Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.
Maintained by Rob Carnell. Last updated 9 months ago.
latin-hypercubelatin-hypercube-samplelatin-hypercube-samplinglhsorthogonal-arrayscpp
1.8 match 44 stars 13.95 score 1.5k scripts 108 dependents
bioc
dks:The double Kolmogorov-Smirnov package for evaluating multiple testing procedures.
The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated.
Maintained by Jeffrey T. Leek. Last updated 5 months ago.
multiplecomparisonqualitycontrol
7.5 match 3.30 score 1 scriptscran
terminaldigits:Tests of Uniformity and Independence for Terminal Digits
Implements simulated tests for the hypothesis that terminal digits are uniformly distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits are independent from preceding digits (several tests of independence for r x c contingency tables). Also, for a number of distributions, implements Monte Carlo simulations for type I errors and power for the test of independence.
Maintained by Josh McCormick. Last updated 3 years ago.
9.1 match 2.70 score 4 scripts
davidcsterratt
geometry:Mesh Generation and Surface Tessellation
Makes the 'Qhull' library <http://www.qhull.org> available in R, in a similar manner as in Octave and MATLAB. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2D, 3D, 4D, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this.
Maintained by David C. Sterratt. Last updated 1 months ago.
1.9 match 16 stars 12.98 score 776 scripts 139 dependents
r-forge
tm:Text Mining Package
A framework for text mining applications within R.
Maintained by Kurt Hornik. Last updated 26 days ago.
1.9 match 12.96 score 14k scripts 101 dependents
kgoldfeld
simstudy:Simulation of Study Data
Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).
Maintained by Keith Goldfeld. Last updated 8 months ago.
data-generationdata-simulationsimulationstatistical-modelscpp
2.2 match 82 stars 11.00 score 972 scripts 1 dependents
hanase
rlecuyer:R Interface to RNG with Multiple Streams
Provides an interface to the C implementation of the random number generator with multiple independent streams developed by L'Ecuyer et al (2002). The main purpose of this package is to enable the use of this random number generator in parallel R applications.
Maintained by Hana Sevcikova. Last updated 2 years ago.
4.3 match 2 stars 5.64 score 143 scripts 6 dependents
wwiecek
baggr:Bayesian Aggregate Treatment Effects
Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.
Maintained by Witold Wiecek. Last updated 1 years ago.
bayesian-statisticsmeta-analysisquantile-regressionstantreatment-effectscpp
3.3 match 49 stars 7.24 score 88 scripts
flyaflya
causact:Fast, Easy, and Visual Bayesian Inference
Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package.
Maintained by Adam Fleischhacker. Last updated 2 months ago.
bayesian-inferencedagsposterior-probabilityprobabilistic-graphical-modelsprobabilistic-programming
3.3 match 45 stars 7.15 score 52 scriptscran
sna:Tools for Social Network Analysis
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
Maintained by Carter T. Butts. Last updated 6 months ago.
3.5 match 8 stars 6.78 score 94 dependents
oobianom
quickcode:Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts.
Maintained by Obinna Obianom. Last updated 14 days ago.
3.0 match 5 stars 7.76 score 7 scripts 6 dependents
statmanrobin
Lock5Data:Datasets for "Statistics: UnLocking the Power of Data"
Datasets for the third edition of "Statistics: Unlocking the Power of Data" by Lock^5 Includes version of datasets from earlier editions.
Maintained by Robin Lock. Last updated 4 years ago.
8.0 match 2.90 score 322 scripts
lubomirantoni
ICSsmoothing:Data Smoothing by Interpolating Cubic Splines
We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing.
Maintained by Lubomir Antoni. Last updated 1 years ago.
8.5 match 2.70 score 6 scripts
daqana
dqrng:Fast Pseudo Random Number Generators
Several fast random number generators are provided as C++ header only libraries: The PCG family by O'Neill (2014 <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>) as well as the Xoroshiro / Xoshiro family by Blackman and Vigna (2021 <doi:10.1145/3460772>). In addition fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>). The fast sampling methods support unweighted sampling both with and without replacement. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+/++/** and Xoshiro256+/++/** as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011, <doi:10.1145/2063384.2063405>) as provided by the package 'sitmo'.
Maintained by Ralf Stubner. Last updated 6 months ago.
randomrandom-distributionsrandom-generationrandom-samplingrngcpp
1.8 match 42 stars 13.12 score 188 scripts 183 dependents
bgreenwell
ramify:Additional Matrix Functionality
Additional matrix functionality for R including: (1) wrappers for the base matrix function that allow matrices to be created from character strings and lists (the former is especially useful for creating block matrices), (2) better printing of large matrices via the generic "pretty" print function, and (3) a number of convenience functions for users more familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or 'Python'+'NumPy'.
Maintained by Brandon Greenwell. Last updated 8 years ago.
3.6 match 3 stars 6.32 score 154 scripts 3 dependents
giscience-fsu
sperrorest:Perform Spatial Error Estimation and Variable Importance Assessment
Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.
Maintained by Alexander Brenning. Last updated 2 years ago.
cross-validationmachine-learningspatial-statisticsspatio-temporal-modelingstatistical-learning
3.5 match 19 stars 6.46 score 46 scripts
jjustison
SiPhyNetwork:A Phylogenetic Simulator for Reticulate Evolution
A simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.
Maintained by Joshua Justison. Last updated 6 months ago.
4.3 match 11 stars 5.25 score 16 scripts
mdhall272
STraTUS:Enumeration and Uniform Sampling of Transmission Trees for a Known Phylogeny
For a single, known pathogen phylogeny, provides functions for enumeration of the set of compatible epidemic transmission trees, and for uniform sampling from that set. Optional arguments allow for incomplete sampling with a known number of missing individuals, multiple sampling, and known infection time limits. Always assumed are a complete transmission bottleneck and no superinfection or reinfection. See Hall and Colijn (2019) <doi:10.1093/molbev/msz058> for methodology.
Maintained by Matthew Hall. Last updated 5 months ago.
6.8 match 4 stars 3.30 score
clugen
clugenr:Multidimensional Cluster Generation Using Support Lines
An implementation of the clugen algorithm for generating multidimensional clusters with arbitrary distributions. Each cluster is supported by a line segment, the position, orientation and length of which guide where the respective points are placed. This package is described in Fachada & de Andrade (2023) <doi:10.1016/j.knosys.2023.110836>.
Maintained by Nuno Fachada. Last updated 7 months ago.
multidimensional-clustersmultidimensional-datasynthetic-clusterssynthetic-data-generatorsynthetic-dataset-generation
4.2 match 5 stars 5.39 score 14 scripts
faosorios
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
3.5 match 19 stars 6.27 score 37 scripts 10 dependents
niklhart
kldest:Sample-Based Estimation of Kullback-Leibler Divergence
Estimation algorithms for Kullback-Leibler divergence between two probability distributions, based on one or two samples, and including uncertainty quantification. Distributions can be uni- or multivariate and continuous, discrete or mixed.
Maintained by Niklas Hartung. Last updated 6 months ago.
5.4 match 3 stars 4.08 score 20 scripts
skranz
RoundingMatters:Tools for adjusting for rounding problems in metastudies about p-hacking and publication bias
Tools for adjusting for rounding problems in metastudies about p-hacking and publication bias
Maintained by Sebastian Kranz. Last updated 4 years ago.
12.7 match 1.70 score 8 scripts
fk83
scoringRules:Scoring Rules for Parametric and Simulated Distribution Forecasts
Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, <doi:10.18637/jss.v110.i08>.
Maintained by Fabian Krueger. Last updated 6 months ago.
1.9 match 59 stars 11.33 score 408 scripts 13 dependents
helske
bssm:Bayesian Inference of Non-Linear and Non-Gaussian State Space Models
Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.
Maintained by Jouni Helske. Last updated 6 months ago.
bayesian-inferencecppmarkov-chain-monte-carloparticle-filterstate-spacetime-seriesopenblascppopenmp
3.3 match 42 stars 6.43 score 11 scriptsspatstat
spatstat.geom:Geometrical Functionality of the 'spatstat' Family
Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)
Maintained by Adrian Baddeley. Last updated 2 days ago.
classes-and-objectsdistance-calculationgeometrygeometry-processingimagesmensurationplottingpoint-patternsspatial-dataspatial-data-analysis
1.8 match 7 stars 12.11 score 241 scripts 227 dependents
tjheaton
carbondate:Calibration and Summarisation of Radiocarbon Dates
Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.
Maintained by Timothy J Heaton. Last updated 2 months ago.
3.6 match 5 stars 5.78 score 20 scripts
tdhock
nc:Named Capture to Data Tables
User-friendly functions for extracting a data table (row for each match, column for each group) from non-tabular text data using regular expressions, and for melting columns that match a regular expression. Patterns are defined using a readable syntax that makes it easy to build complex patterns in terms of simpler, re-usable sub-patterns. Named R arguments are translated to column names in the output; capture groups without names are used internally in order to provide a standard interface to three regular expression 'C' libraries ('PCRE', 'RE2', 'ICU'). Output can also include numeric columns via user-specified type conversion functions.
Maintained by Toby Hocking. Last updated 2 months ago.
3.0 match 16 stars 6.85 score 46 scripts
mmaechler
sfsmisc:Utilities from 'Seminar fuer Statistik' ETH Zurich
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().
Maintained by Martin Maechler. Last updated 5 months ago.
1.9 match 11 stars 10.87 score 566 scripts 119 dependents
jinghuazhao
gap:Genetic Analysis Package
As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18. <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).
Maintained by Jing Hua Zhao. Last updated 16 days ago.
1.7 match 12 stars 11.88 score 448 scripts 16 dependents
nlmixr2
rxode2:Facilities for Simulating from ODE-Based Models
Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS.
Maintained by Matthew L. Fidler. Last updated 30 days ago.
1.8 match 40 stars 11.24 score 220 scripts 13 dependentsbioc
iClusterPlus:Integrative clustering of multi-type genomic data
Integrative clustering of multiple genomic data using a joint latent variable model.
Maintained by Qianxing Mo. Last updated 4 months ago.
multi-omicsclusteringfortranopenblas
3.5 match 5.76 score 190 scripts
zarquon42b
Rvcg:Manipulations of Triangular Meshes Based on the 'VCGLIB' API
Operations on triangular meshes based on 'VCGLIB'. This package integrates nicely with the R-package 'rgl' to render the meshes processed by 'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is an open source portable C++ templated library for manipulation, processing and displaying with OpenGL of triangle and tetrahedral meshes. The library, composed by more than 100k lines of code, is released under the GPL license, and it is the base of most of the software tools of the Visual Computing Lab of the Italian National Research Council Institute ISTI <https://vcg.isti.cnr.it/>, like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk <https://github.com/cnr-isti-vclab/vcglib> and patched to work with options determined by the configure script as well as to work with the header files included by 'RcppEigen'.
Maintained by Stefan Schlager. Last updated 5 months ago.
2.0 match 25 stars 10.05 score 195 scripts 29 dependentsr-forge
copula:Multivariate Dependence with Copulas
Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.
Maintained by Martin Maechler. Last updated 11 days ago.
1.7 match 11.83 score 1.2k scripts 86 dependents
guyabel
migest:Methods for the Indirect Estimation of Bilateral Migration
Tools for estimating, measuring and working with migration data.
Maintained by Guy J. Abel. Last updated 1 months ago.
3.4 match 32 stars 5.80 score 86 scripts
epiverse-trace
finalsize:Calculate the Final Size of an Epidemic
Calculate the final size of a susceptible-infectious-recovered epidemic in a population with demographic variation in contact patterns and susceptibility to disease, as discussed in Miller (2012) <doi:10.1007/s11538-012-9749-6>.
Maintained by Rosalind Eggo. Last updated 1 months ago.
epidemic-modellingepidemiologyepiverseoutbreak-analysisrcppsdg-3cpp
2.5 match 11 stars 8.11 score 46 scripts
molevolepid
SEEPS:Sequence evolution and epidemiological process simulator
A modular, modern simulation suite and toolkit for simulating transmission networks, phylogenies, and evolutionary pairwise distance matrices under different models and assumptions for viral/sequence evolution. While intially developed for HIV, SEEPS offers modular utilities for custom workflows for extension beyond HIV.
Maintained by Michael Kupperman. Last updated 2 months ago.
biological-sequencesepidemiologyevolutionhivsimulation-framework
5.0 match 1 stars 3.95 score 6 scripts
jtimonen
lgpr:Longitudinal Gaussian Process Regression
Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.
Maintained by Juho Timonen. Last updated 6 months ago.
bayesian-inferencegaussian-processeslongitudinal-datastancpp
3.3 match 25 stars 5.94 score 69 scripts
spatial-ews
spatialwarnings:Spatial Early Warning Signals of Ecosystem Degradation
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).
Maintained by Alexandre Genin. Last updated 6 months ago.
catastrophiccriticalecologyindicatorspointsshiftsspacetransitionscpp
3.7 match 15 stars 5.32 score 46 scripts
jungmoyoon
QTE.RD:Quantile Treatment Effects under the Regression Discontinuity Design
Provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.
Maintained by Jungmo Yoon. Last updated 7 months ago.
9.8 match 2.00 score
lbelzile
VaRES:Computes Value at Risk and Expected Shortfall for over 100 Parametric Distributions
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions. Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.
Maintained by Leo Belzile. Last updated 2 years ago.
4.3 match 1 stars 4.57 score 123 scripts 2 dependents
cneyens
raem:Analytic Element Modeling of Steady Single-Layer Groundwater Flow
A model of single-layer groundwater flow in steady-state under the Dupuit-Forchheimer assumption can be created by placing elements such as wells, area-sinks and line-sinks at arbitrary locations in the flow field. Output variables include hydraulic head and the discharge vector. Particle traces can be computed numerically in three dimensions. The underlying theory is described in Haitjema (1995) <doi:10.1016/B978-0-12-316550-3.X5000-4> and references therein.
Maintained by Cas Neyens. Last updated 7 months ago.
analytic-element-modelgroundwatergroundwater-modellinghydrogeologyhydrology
3.3 match 8 stars 5.81 score 6 scripts
lbbe-software
fitdistrplus:Help to Fit of a Parametric Distribution to Non-Censored or Censored Data
Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation.
Maintained by Aurélie Siberchicot. Last updated 13 days ago.
1.2 match 54 stars 16.15 score 4.5k scripts 153 dependents
marvels2031
PWEALL:Design and Monitoring of Survival Trials Accounting for Complex Situations
Calculates various functions needed for design and monitoring survival trials accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual, and different censoring distributions between groups. The event time distribution is assumed to be piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution. As compared with Version 1.2.1, two more types of hybrid crossover are added. A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, density, hazard and cumulative hazard functions. Also, to generate the crossover-adjusted event time random variable, a more efficient algorithm is used and the output includes crossover indicators.
Maintained by Xiaodong Luo. Last updated 2 years ago.
7.8 match 2.42 score 44 scripts 2 dependents
andremueller
colorpatch:Optimized Rendering of Fold Changes and Confidence Values
Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values within a single diagram. This is especially useful for rendering gene expression data as well as other types of differential experiments. In addition to different rendering methods (ggplot extensions) functionality for perceptually optimizing color palettes are provided. Furthermore the package provides extension methods of the colorspace color-class in order to simplify the work with palettes (a.o. length, as.list, and append are supported).
Maintained by Andre Mueller. Last updated 8 years ago.
5.8 match 3.23 score 34 scriptsbioc
TEQC:Quality control for target capture experiments
Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on.
Maintained by Sarah Bonnin. Last updated 5 months ago.
qualitycontrolmicroarraysequencinggenetics
4.3 match 4.30 score 8 scripts
pavel-fibich
gawdis:Multi-Trait Dissimilarity with more Uniform Contributions
R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.
Maintained by Pavel Fibich. Last updated 2 years ago.
dissimilarityfdgowdismulti-trait-dissimilaritytrait
3.5 match 5 stars 5.20 score 21 scripts 1 dependents
mhahsler
stream:Infrastructure for Data Stream Mining
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) <doi:10.18637/jss.v076.i14>.
Maintained by Michael Hahsler. Last updated 4 days ago.
data-stream-clusteringdatastreamstream-miningcpp
1.8 match 39 stars 10.05 score 132 scripts 3 dependents
dmazarei
ntsDists:Neutrosophic Distributions
Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) <doi:10.5281/zenodo.571153> and Rao et al (2023) <doi:10.5281/zenodo.7832786>.
Maintained by Danial Mazarei. Last updated 8 months ago.
distributiondistributionsneutrosophicneutrosophic-distributionsr-programming
3.9 match 2 stars 4.56 score 1 dependents
sqyu
genscore:Generalized Score Matching Estimators
Implementation of the Generalized Score Matching estimator in Yu et al. (2019) <http://jmlr.org/papers/v20/18-278.html> for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) <doi:10.1214/16-EJS1126>, with the addition of a diagonal multiplier.
Maintained by Shiqing Yu. Last updated 5 years ago.
density-estimationgraphical-modelsinteraction-modelsscore-matchingundirected-graphs
4.2 match 1 stars 4.18 score 3 scripts 1 dependents
cbielow
PTXQC:Quality Report Generation for MaxQuant and mzTab Results
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS 'QualityControl' tool). Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format. Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) <doi:10.1021/acs.jproteome.5b00780>.
Maintained by Chris Bielow. Last updated 1 years ago.
drag-and-drophacktoberfestheatmapmatch-between-runsmaxquantmetricmztabopenmsproteomicsquality-controlquality-metricsreport
1.9 match 42 stars 9.35 score 105 scripts 1 dependentsflorianhartig
BayesianTools:General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics
General-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.
Maintained by Florian Hartig. Last updated 1 years ago.
bayesecological-modelsmcmcoptimizationsmcsystems-biologycpp
1.7 match 122 stars 10.17 score 580 scripts 5 dependentsspatstat
spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family
Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.
Maintained by Adrian Baddeley. Last updated 2 months ago.
density-estimationheat-equationkernel-density-estimationnetwork-analysispoint-processesspatial-data-analysisstatistical-analysisstatistical-inferencestatistical-models
1.8 match 6 stars 9.64 score 35 scripts 43 dependents
jeremygelb
spNetwork:Spatial Analysis on Network
Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).
Maintained by Jeremy Gelb. Last updated 2 days ago.
kernelkernel-density-estimationnetworknetwork-analysisspatialspatial-analysisspatial-data-analysiscpp
2.3 match 38 stars 7.69 score 52 scriptsbioc
celda:CEllular Latent Dirichlet Allocation
Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included.
Maintained by Joshua Campbell. Last updated 28 days ago.
singlecellgeneexpressionclusteringsequencingbayesianimmunooncologydataimportcppopenmp
1.7 match 147 stars 10.47 score 256 scripts 2 dependentsr-forge
FME:A Flexible Modelling Environment for Inverse Modelling, Sensitivity, Identifiability and Monte Carlo Analysis
Provides functions to help in fitting models to data, to perform Monte Carlo, sensitivity and identifiability analysis. It is intended to work with models be written as a set of differential equations that are solved either by an integration routine from package 'deSolve', or a steady-state solver from package 'rootSolve'. However, the methods can also be used with other types of functions.
Maintained by Karline Soetaert. Last updated 2 years ago.
2.0 match 8.62 score 382 scripts 9 dependents
ltierney
snow:Simple Network of Workstations
Support for simple parallel computing in R.
Maintained by Luke Tierney. Last updated 3 years ago.
1.8 match 1 stars 9.82 score 2.0k scripts 1.3k dependents
tpetzoldt
simecol:Simulation of Ecological (and Other) Dynamic Systems
An object oriented framework to simulate ecological (and other) dynamic systems. It can be used for differential equations, individual-based (or agent-based) and other models as well. It supports structuring of simulation scenarios (to avoid copy and paste) and aims to improve readability and re-usability of code.
Maintained by Thomas Petzoldt. Last updated 7 months ago.
3.6 match 4.76 score 190 scripts
richfitz
diversitree:Comparative 'Phylogenetic' Analyses of Diversification
Contains a number of comparative 'phylogenetic' methods, mostly focusing on analysing diversification and character evolution. Contains implementations of 'BiSSE' (Binary State 'Speciation' and Extinction) and its unresolved tree extensions, 'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE', 'GeoSSE', and 'BiSSE-ness' Other included methods include Markov models of discrete and continuous trait evolution and constant rate 'speciation' and extinction.
Maintained by Richard G. FitzJohn. Last updated 6 months ago.
2.0 match 33 stars 8.51 score 524 scripts 4 dependents
jarrodhadfield
MCMCglmm:MCMC Generalised Linear Mixed Models
Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).
Maintained by Jarrod Hadfield. Last updated 3 months ago.
1.9 match 2 stars 8.83 score 1.2k scripts 13 dependents
vigou3
actuar:Actuarial Functions and Heavy Tailed Distributions
Functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. Support for many additional probability distributions to model insurance loss size and frequency: 23 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities. Main reference: <doi:10.18637/jss.v025.i07>. Implementation of the Feller-Pareto family of distributions: <doi:10.18637/jss.v103.i06>.
Maintained by Vincent Goulet. Last updated 2 months ago.
1.8 match 12 stars 9.44 score 732 scripts 35 dependentsjakobbossek
mcMST:A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem
Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems.
Maintained by Jakob Bossek. Last updated 2 years ago.
evolutionary-algorithmsmcmstminimum-spanning-treesmulti-objective-optimizationspanningtrees
3.5 match 4 stars 4.73 score 27 scripts
dynverse
dynparam:Creating Meta-Information for Parameters
Provides tools for describing parameters of algorithms in an abstract way. Description can include an id, a description, a domain (range or list of values), and a default value. 'dynparam' can also convert parameter sets to a 'ParamHelpers' format, in order to be able to use 'dynparam' in conjunction with 'mlrMBO'.
Maintained by Robrecht Cannoodt. Last updated 6 years ago.
4.1 match 2 stars 3.95 score 15 scripts 2 dependents
chrhennig
fpc:Flexible Procedures for Clustering
Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc.
Maintained by Christian Hennig. Last updated 6 months ago.
1.8 match 11 stars 9.25 score 2.6k scripts 70 dependents
oleksii-nikolaienko
ExtDist:Extending the Range of Functions for Probability Distributions
A consistent, unified and extensible framework for estimation of parameters for probability distributions, including parameter estimation procedures that allow for weighted samples; the current set of distributions included are: the standard beta, The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace, logistic, normal, symmetric truncated normal, truncated normal, symmetric-reflected truncated beta, standard symmetric-reflected truncated beta, triangular, uniform, and Weibull distributions; decision criteria and selections based on these decision criteria.
Maintained by Oleksii Nikolaienko. Last updated 2 years ago.
2.8 match 1 stars 5.84 score 58 scripts 2 dependentstidymodels
embed:Extra Recipes for Encoding Predictors
Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <doi:10.48550/arXiv.1611.09477> or nonlinear models <doi:10.48550/arXiv.1604.06737> can be used. There are also functions for dimension reduction and other approaches.
Maintained by Emil Hvitfeldt. Last updated 2 months ago.
1.7 match 142 stars 9.35 score 1.1k scripts
genentech
psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation
Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.
Maintained by Matt Secrest. Last updated 1 months ago.
bayesian-dynamic-borrowingpsborrow2simulation-study
2.0 match 18 stars 7.87 score 16 scripts
kjohnsson
intrinsicDimension:Intrinsic Dimension Estimation
A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.
Maintained by Kerstin Johnsson. Last updated 6 years ago.
2.6 match 12 stars 6.03 score 59 scripts 1 dependents
avehtari
aaltobda:Functionality and Data for the Aalto Course on Bayesian Data Analysis
Functionality and Data for the Aalto University Course on Bayesian Data Analysis.
Maintained by Aki Vehtari. Last updated 3 months ago.
bayesbayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-workflow
1.7 match 2.2k stars 8.93 score 159 scripts
caetanods
ratematrix:Bayesian Estimation of the Evolutionary Rate Matrix
The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.
Maintained by Daniel Caetano. Last updated 2 years ago.
2.6 match 10 stars 5.91 score 18 scripts 1 dependents
r-spatial
rgee:R Bindings for Calling the 'Earth Engine' API
Earth Engine <https://earthengine.google.com/> client library for R. All of the 'Earth Engine' API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See <https://r-spatial.github.io/rgee/> for further details.
Maintained by Cesar Aybar. Last updated 4 days ago.
earth-engineearthenginegoogle-earth-enginegoogleearthenginespatial-analysisspatial-data
1.1 match 715 stars 13.77 score 1.9k scripts 3 dependents
federicocomoglio
dupiR:Bayesian Inference from Count Data using Discrete Uniform Priors
We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. This package implements a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. This can be used for a variety of statistical problems involving absolute quantification under uncertainty. See Comoglio et al. (2013) <doi:10.1371/journal.pone.0074388>.
Maintained by Federico Comoglio. Last updated 12 months ago.
5.0 match 1 stars 3.00 score 7 scripts
yrobink
ROOPSD:R Object Oriented Programming for Statistical Distribution
Statistical distribution in OOP (Object Oriented Programming) way. This package proposes a R6 class interface to classic statistical distribution, and new distributions can be easily added with the class AbstractDist. A useful point is the generic fit() method for each class, which uses a maximum likelihood estimation to find the parameters of a dataset, see, e.g. Hastie, T. and al (2009) <isbn:978-0-387-84857-0>. Furthermore, the rv_histogram class gives a non-parametric fit, with the same accessors that for the classic distribution. Finally, three random generators useful to build synthetic data are given: a multivariate normal generator, an orthogonal matrix generator, and a symmetric positive definite matrix generator, see Mezzadri, F. (2007) <arXiv:math-ph/0609050>.
Maintained by Yoann Robin. Last updated 2 years ago.
3.3 match 1 stars 4.49 score 5 scripts 12 dependentslbelzile
mev:Modelling of Extreme Values
Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
Maintained by Leo Belzile. Last updated 5 months ago.
extreme-value-statisticslikelihood-functionsmax-stablesimulationthreshold-selectionopenblascppopenmp
1.8 match 13 stars 8.23 score 94 scripts 4 dependents
rfastofficial
Rfast2:A Collection of Efficient and Extremely Fast R Functions II
A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.
Maintained by Manos Papadakis. Last updated 1 years ago.
1.8 match 38 stars 8.09 score 75 scripts 26 dependents
sbgraves237
Ecfun:Functions for 'Ecdat'
Functions and vignettes to update data sets in 'Ecdat' and to create, manipulate, plot, and analyze those and similar data sets.
Maintained by Spencer Graves. Last updated 4 months ago.
1.8 match 7.94 score 85 scripts 4 dependents
tychelab
CoSMoS:Complete Stochastic Modelling Solution
Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fields’ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).
Maintained by Kevin Shook. Last updated 4 years ago.
2.0 match 11 stars 7.10 score 77 scripts
insightsengineering
teal.transform:Functions for Extracting and Merging Data in the 'teal' Framework
A standardized user interface for column selection, that facilitates dataset merging in 'teal' framework.
Maintained by Dawid Kaledkowski. Last updated 1 months ago.
1.7 match 3 stars 8.39 score 9 scripts 4 dependents
pmair78
smacof:Multidimensional Scaling
Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Maintained by Patrick Mair. Last updated 5 months ago.
1.8 match 5 stars 7.86 score 152 scripts 24 dependents
alec42
Distributacalcul:Probability Distribution Functions
Calculates expected values, variance, different moments (kth moment, truncated mean), stop-loss, mean excess loss, Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) as well as some density and cumulative (survival) functions of continuous, discrete and compound distributions. This package also includes a visual 'Shiny' component to enable students to visualize distributions and understand the impact of their parameters. This package is intended to expand the 'stats' package so as to enable students to develop an intuition for probability.
Maintained by Alec James van Rassel. Last updated 1 years ago.
4.3 match 2 stars 3.30 score 9 scripts
dwoll
DVHmetrics:Analyze Dose-Volume Histograms and Check Constraints
Functionality for analyzing dose-volume histograms (DVH) in radiation oncology: Read DVH text files, calculate DVH metrics as well as generalized equivalent uniform dose (gEUD), biologically effective dose (BED), equivalent dose in 2 Gy fractions (EQD2), normal tissue complication probability (NTCP), and tumor control probability (TCP). Show DVH diagrams, check and visualize quality assurance constraints for the DVH. Includes web-based graphical user interface.
Maintained by Daniel Wollschlaeger. Last updated 16 days ago.
2.3 match 12 stars 6.03 score
vfisikop
volesti:Volume Approximation and Sampling of Convex Polytopes
Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for estimating copulas useful in computational finance. Methods implemented in 'volesti' are described in A. Chalkis and V. Fisikopoulos (2022) <doi:10.32614/RJ-2021-077> and references therein.
Maintained by Vissarion Fisikopoulos. Last updated 5 months ago.
4.9 match 2 stars 2.84 score 69 scriptsdynverse
dynutils:Common Functionality for the 'dynverse' Packages
Provides common functionality for the 'dynverse' packages. 'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods. For more information, check out <https://dynverse.org>.
Maintained by Robrecht Cannoodt. Last updated 2 years ago.
2.0 match 3 stars 6.89 score 109 scripts 8 dependentsmuschellij2
fslr:Wrapper Functions for 'FSL' ('FMRIB' Software Library) from Functional MRI of the Brain ('FMRIB')
Wrapper functions that interface with 'FSL' <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging' software, using system commands. The goal is to be able to interface with 'FSL' completely in R, where you pass R objects of class 'nifti', implemented by package 'oro.nifti', and the function executes an 'FSL' command and returns an R object of class 'nifti' if desired.
Maintained by John Muschelli. Last updated 1 months ago.
fslfslrneuroimagingneuroimaging-analysisneuroimaging-data-science
1.7 match 41 stars 8.01 score 420 scripts
prabhanjan-tattar
ACSWR:A Companion Package for the Book "A Course in Statistics with R"
A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. "A Course in Statistics with R", J. Wiley, ISBN 978-1-119-15272-9.
Maintained by Prabhanjan Tattar. Last updated 10 years ago.
6.8 match 2.03 score 106 scripts
jleydold
rstream:Streams of Random Numbers
Unified object oriented interface for multiple independent streams of random numbers from different sources.
Maintained by Josef Leydold. Last updated 2 years ago.
5.1 match 2.69 score 54 scripts 3 dependents
uscbiostats
fmcmc:A friendly MCMC framework
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.
Maintained by George Vega Yon. Last updated 1 years ago.
adaptivebayesian-inferencemarkov-chain-monte-carlomcmcmetropolis-hastingsparallel-computing
2.0 match 16 stars 6.79 score 86 scripts 1 dependents
glenndavis52
munsellinterpol:Interpolate Munsell Renotation Data from Hue Value/Chroma to CIE/RGB
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB. Includes ISCC-NBS color block lookup. Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Maintained by Glenn Davis. Last updated 2 months ago.
3.3 match 2 stars 4.01 score 43 scripts 1 dependents
alexisderumigny
BoundEdgeworth:Bound on the Error of the First-Order Edgeworth Expansion
Computes uniform bounds on the distance between the cumulative distribution function of a standardized sum of random variables and its first-order Edgeworth expansion, following the article Derumigny, Girard, Guyonvarch (2023) <doi:10.1007/s13171-023-00320-y>.
Maintained by Alexis Derumigny. Last updated 7 months ago.
edgeworth-expansionprobability-boundsr-pkg
4.0 match 1 stars 3.30 score 1 scripts
adamlilith
fasterRaster:Faster Raster and Spatial Vector Processing Using 'GRASS GIS'
Processing of large-in-memory/large-on disk rasters and spatial vectors using 'GRASS GIS' <https://grass.osgeo.org/>. Most functions in the 'terra' package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using 'terra' or 'sf', but for large-in-memory/large-on-disk objects, 'fasterRaster' may be faster. To use most of the functions, you must have the stand-alone version (not the 'OSGeoW4' installer version) of 'GRASS GIS' 8.0 or higher.
Maintained by Adam B. Smith. Last updated 19 days ago.
aspectdistancefragmentationfragmentation-indicesgisgrassgrass-gisrasterraster-projectionrasterizeslopetopographyvectorization
1.7 match 58 stars 7.69 score 8 scripts
kenaho1
asbio:A Collection of Statistical Tools for Biologists
Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.
Maintained by Ken Aho. Last updated 2 months ago.
1.8 match 5 stars 7.32 score 310 scripts 3 dependents
rmheiberger
HH:Statistical Analysis and Data Display: Heiberger and Holland
Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Maintained by Richard M. Heiberger. Last updated 1 months ago.
2.0 match 3 stars 6.42 score 752 scripts 5 dependentscran
compositions:Compositional Data Analysis
Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
Maintained by K. Gerald van den Boogaart. Last updated 1 years ago.
2.0 match 1 stars 6.35 score 36 dependents
rcorty
ggQQunif:Compare Big Datasets to the Uniform Distribution
A quantile-quantile plot can be used to compare a sample of p-values to the uniform distribution. But when the dataset is big (i.e. > 1e4 p-values), plotting the quantile-quantile plot can be slow. geom_QQ uses all the data to calculate the quantiles, but thins it out in a way that focuses on points near zero before plotting to speed up plotting and decrease file size, when vector graphics are stored.
Maintained by Robert Corty. Last updated 7 years ago.
3.5 match 8 stars 3.60 score 3 scripts
rbgramacy
tgp:Bayesian Treed Gaussian Process Models
Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions. For details and tutorials, see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) <doi:10.18637/jss.v033.i06>.
Maintained by Robert B. Gramacy. Last updated 6 months ago.
1.7 match 9 stars 7.36 score 203 scripts 12 dependents
sujit-sahu
ipsRdbs:Introduction to Probability, Statistics and R for Data-Based Sciences
Contains data sets, programmes and illustrations discussed in the book, "Introduction to Probability, Statistics and R: Foundations for Data-Based Sciences." Sahu (2024, isbn:9783031378645) describes the methods in detail.
Maintained by Sujit K. Sahu. Last updated 11 months ago.
3.4 match 1 stars 3.70 score 2 scripts
agi-lab
SPLICE:Synthetic Paid Loss and Incurred Cost Experience (SPLICE) Simulator
An extension to the individual claim simulator called 'SynthETIC' (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" <arXiv:2109.04058>.
Maintained by Melantha Wang. Last updated 1 years ago.
2.3 match 6 stars 5.58 score 14 scripts
lvclark
polyRAD:Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids
Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. 'polyRAD' is described by Clark et al. (2019) <doi:10.1534/g3.118.200913>, and the Hind/He statistic for marker filtering is described by Clark et al. (2022) <doi:10.1186/s12859-022-04635-9>. A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020, Version 1) <doi:10.1101/2020.01.11.902890>.
Maintained by Lindsay V. Clark. Last updated 8 days ago.
bioinformaticsdna-sequencinggenotype-likelihoodsgenotyping-by-sequencinghacktoberfestrad-seqrad-sequencingsnp-genotypingcpp
1.8 match 28 stars 6.98 score 85 scriptsbioc
SPsimSeq:Semi-parametric simulation tool for bulk and single-cell RNA sequencing data
SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.
Maintained by Joris Meys. Last updated 5 months ago.
geneexpressionrnaseqsinglecellsequencingdnaseq
1.8 match 10 stars 7.14 score 29 scripts 1 dependents
ehsan66
ICAOD:Optimal Designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm (ICA)
Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.
Maintained by Ehsan Masoudi. Last updated 4 years ago.
5.0 match 2.49 score 31 scripts