Showing 200 of total 274 results (show query)
r-spatial
spdep:Spatial Dependence: Weighting Schemes, Statistics
A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured join count statistics, 'APLE' ('Li 'et al.' ) <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I', 'Gearys C' ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and 'Getis/Ord' G ('Ord' and 'Getis' 1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>, 'saddlepoint' approximations ('Tiefelsdorf' 2002) <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') <doi:10.1007/s00168-011-0492-y>. The implementation of most of these measures is described in 'Bivand' and 'Wong' (2018) <doi:10.1007/s11749-018-0599-x>, with further extensions in 'Bivand' (2022) <doi:10.1111/gean.12319>. 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) <doi:10.1016/0166-0462(95)02111-6>, as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) <doi:10.1080/17421772.2023.2256810>. A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021) <doi:10.1016/j.jas.2020.105306> and 'Bivand et al.' (2017) <doi:10.1016/j.spasta.2017.03.003> was added in 1.3-7. From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'.
Maintained by Roger Bivand. Last updated 18 days ago.
spatial-autocorrelationspatial-dependencespatial-weights
22.7 match 131 stars 16.62 score 6.0k scripts 107 dependents
robjhyndman
tsfeatures:Time Series Feature Extraction
Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
Maintained by Rob Hyndman. Last updated 8 months ago.
14.5 match 254 stars 11.47 score 268 scripts 22 dependents
nanxstats
protr:Generating Various Numerical Representation Schemes for Protein Sequences
Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042>. For full functionality, the software 'ncbi-blast+' is needed, see <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html> for more information.
Maintained by Nan Xiao. Last updated 6 months ago.
bioinformaticsfeature-engineeringfeature-extractionmachine-learningpeptidesprotein-sequencessequence-analysis
14.5 match 52 stars 10.02 score 173 scripts 3 dependents
japilo
colorednoise:Simulate Temporally Autocorrelated Populations
Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.
Maintained by July Pilowsky. Last updated 11 months ago.
23.5 match 10 stars 5.43 score 18 scripts
geobosh
sarima:Simulation and Prediction with Seasonal ARIMA Models
Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated (for the algebraic basis for this see <arXiv:2208.05055>, a paper on the methodology is being prepared).
Maintained by Georgi N. Boshnakov. Last updated 12 months ago.
arimakalman-filterreg-sarimasarimasarimaxseasonaltime-seriesxarimaopenblascpp
18.2 match 3 stars 6.71 score 112 scripts 1 dependents
hannameyer
CAST:'caret' Applications for Spatial-Temporal Models
Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2023) <doi:10.5194/egusphere-2023-1308>; Schumacher et al. (2024) <doi:10.5194/egusphere-2024-2730>. The package is described in detail in Meyer et al. (2024) <doi:10.48550/arXiv.2404.06978>.
Maintained by Hanna Meyer. Last updated 2 months ago.
autocorrelationcaretfeature-selectionmachine-learningoverfittingpredictive-modelingspatialspatio-temporalvariable-selection
10.0 match 114 stars 11.97 score 298 scripts 1 dependents
bioc
Rcpi:Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery
A molecular informatics toolkit with an integration of bioinformatics and chemoinformatics tools for drug discovery.
Maintained by Nan Xiao. Last updated 5 months ago.
softwaredataimportdatarepresentationfeatureextractioncheminformaticsbiomedicalinformaticsproteomicsgosystemsbiologybioconductorbioinformaticsdrug-discoveryfeature-extractionfingerprintmolecular-descriptorsprotein-sequences
14.3 match 37 stars 7.81 score 29 scripts
modeloriented
auditor:Model Audit - Verification, Validation, and Error Analysis
Provides an easy to use unified interface for creating validation plots for any model. The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.
Maintained by Alicja Gosiewska. Last updated 1 years ago.
classificationerror-analysisexplainable-artificial-intelligencemachine-learningmodel-validationregression-modelsresidualsxai
12.2 match 58 stars 8.76 score 94 scripts 2 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
6.2 match 226 stars 14.74 score 2.8k scripts 10 dependentsgeobosh
pcts:Periodically Correlated and Periodically Integrated Time Series
Classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996) <doi:10.1111/j.1467-9892.1996.tb00281.x>.
Maintained by Georgi N. Boshnakov. Last updated 1 years ago.
par-modelsperiodicperiodic-modelspiar-modelsseasonaltime-seriestime-series-models
21.0 match 2 stars 4.00 score 3 scripts
cran
nlme:Linear and Nonlinear Mixed Effects Models
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Maintained by R Core Team. Last updated 2 months ago.
6.0 match 6 stars 13.00 score 13k scripts 8.7k dependents
tidyverts
feasts:Feature Extraction and Statistics for Time Series
Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.
Maintained by Mitchell OHara-Wild. Last updated 4 months ago.
5.6 match 300 stars 12.38 score 1.4k scripts 7 dependents
bjw34032
waveslim:Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing
Basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002). All figures in chapters 4-7 of GSW (2001) are reproducible using this package and R code available at the book website(s) below.
Maintained by Brandon Whitcher. Last updated 10 months ago.
8.8 match 3 stars 7.88 score 108 scripts 23 dependents
jacolien
itsadug:Interpreting Time Series and Autocorrelated Data Using GAMMs
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).
Maintained by Jacolien van Rij. Last updated 3 years ago.
10.5 match 6.51 score 576 scripts 2 dependents
paul-buerkner
brms:Bayesian Regression Models using 'Stan'
Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bürkner. Last updated 3 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
4.1 match 1.3k stars 16.61 score 13k scripts 34 dependents
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
5.2 match 169 stars 12.97 score 2.6k scripts 19 dependentsrobjhyndman
forecast:Forecasting Functions for Time Series and Linear Models
Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
Maintained by Rob Hyndman. Last updated 7 months ago.
forecastforecastingopenblascpp
3.4 match 1.1k stars 18.63 score 16k scripts 239 dependents
martynplummer
coda:Output Analysis and Diagnostics for MCMC
Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain.
Maintained by Martyn Plummer. Last updated 1 years ago.
5.6 match 6 stars 11.33 score 8.3k scripts 1.1k dependents
braverock
PerformanceAnalytics:Econometric Tools for Performance and Risk Analysis
Collection of econometric functions for performance and risk analysis. In addition to standard risk and performance metrics, this package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.
Maintained by Brian G. Peterson. Last updated 3 months ago.
4.0 match 222 stars 15.93 score 4.8k scripts 20 dependents
r-forge
sandwich:Robust Covariance Matrix Estimators
Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC) covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC) covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators); clustered covariances (one-way and multi-way); panel and panel-corrected covariances; outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>, Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.
Maintained by Achim Zeileis. Last updated 2 months ago.
4.2 match 14.92 score 11k scripts 887 dependents
xfim
ggmcmc:Tools for Analyzing MCMC Simulations from Bayesian Inference
Tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables, and functions to work with hierarchical/multilevel batches of parameters (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).
Maintained by Xavier Fernández i Marín. Last updated 2 years ago.
bayesian-data-analysisggplot2graphicaljagsmcmcstan
5.2 match 112 stars 12.02 score 1.6k scripts 8 dependents
mlysy
SuperGauss:Superfast Likelihood Inference for Stationary Gaussian Time Series
Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.
Maintained by Martin Lysy. Last updated 1 months ago.
10.4 match 2 stars 5.60 score 33 scripts 2 dependents
jimmcl
trajr:Animal Trajectory Analysis
A toolbox to assist with statistical analysis of animal trajectories. It provides simple access to algorithms for calculating and assessing a variety of characteristics such as speed and acceleration, as well as multiple measures of straightness or tortuosity. Some support is provided for 3-dimensional trajectories. McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.
Maintained by Jim McLean. Last updated 8 months ago.
7.4 match 27 stars 7.69 score 151 scripts
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.
8.0 match 11 stars 7.10 score 77 scripts
morrowcj
remotePARTS:Spatiotemporal Autoregression Analyses for Large Data Sets
These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses.
Maintained by Clay Morrow. Last updated 2 years ago.
autocorrelationbig-dataremote-sensing-in-rstatistical-analysiscppopenmp
10.8 match 22 stars 5.25 score 16 scripts
rvalavi
blockCV:Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation
Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
Maintained by Roozbeh Valavi. Last updated 5 months ago.
cross-validationspatialspatial-cross-validationspatial-modellingspecies-distribution-modellingcpp
5.1 match 113 stars 10.49 score 302 scripts 3 dependents
gmestrem
fdaACF:Autocorrelation Function for Functional Time Series
Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.
Maintained by Guillermo Mestre Marcos. Last updated 4 years ago.
14.6 match 8 stars 3.64 score 11 scripts
smartdata-analysis-and-statistics
metamisc:Meta-Analysis of Diagnosis and Prognosis Research Studies
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Maintained by Thomas Debray. Last updated 1 months ago.
meta-analysisprognosisprognostic-models
7.0 match 7 stars 7.48 score 102 scripts
adeverse
adespatial:Multivariate Multiscale Spatial Analysis
Tools for the multiscale spatial analysis of multivariate data. Several methods are based on the use of a spatial weighting matrix and its eigenvector decomposition (Moran's Eigenvectors Maps, MEM). Several approaches are described in the review Dray et al (2012) <doi:10.1890/11-1183.1>.
Maintained by Aurélie Siberchicot. Last updated 12 days ago.
4.7 match 36 stars 11.06 score 398 scripts 2 dependents
laplacesdemonr
LaplacesDemon:Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Maintained by Henrik Singmann. Last updated 12 months ago.
3.8 match 93 stars 13.45 score 1.8k scripts 60 dependents
bgctw
lognorm:Functions for the Lognormal Distribution
The lognormal distribution (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>) can characterize uncertainty that is bounded by zero. This package provides estimation of distribution parameters, computation of moments and other basic statistics, and an approximation of the distribution of the sum of several correlated lognormally distributed variables (Lo 2013 <doi:10.12988/ams.2013.39511>) and the approximation of the difference of two correlated lognormally distributed variables (Lo 2012 <doi:10.1155/2012/838397>).
Maintained by Thomas Wutzler. Last updated 4 years ago.
8.8 match 6 stars 5.73 score 59 scripts
becarioprecario
DCluster:Functions for the Detection of Spatial Clusters of Diseases
A set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics.
Maintained by Virgilio Gómez-Rubio. Last updated 1 years ago.
11.0 match 4.47 score 99 scripts 1 dependents
covaruber
sommer:Solving Mixed Model Equations in R
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.
average-informationmixed-modelsrcpparmadilloopenblascppopenmp
3.9 match 43 stars 12.70 score 300 scripts 9 dependents
stan-dev
posterior:Tools for Working with Posterior Distributions
Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.
Maintained by Paul-Christian Bürkner. Last updated 10 days ago.
3.0 match 168 stars 16.13 score 3.3k scripts 342 dependents
r-a-dobson
dynamicSDM:Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution
A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.
Maintained by Rachel Dobson. Last updated 26 days ago.
dynamicsdmgoogle-earth-enginegoogledrivesdmspatiotemporalspatiotemporal-data-analysisspatiotemporal-forecastingspecies-distribution-modellingspecies-distributions
7.7 match 6 stars 6.16 score 20 scripts
ajmcneil
tscopula:Time Series Copula Models
Functions for the analysis of time series using copula models. The package is based on methodology described in the following references. McNeil, A.J. (2021) <doi:10.3390/risks9010014>, Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>, Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.
Maintained by Alexander McNeil. Last updated 24 days ago.
8.6 match 2 stars 5.53 score 12 scripts
danlwarren
rwty:R We There Yet? Visualizing MCMC Convergence in Phylogenetics
Implements various tests, visualizations, and metrics for diagnosing convergence of MCMC chains in phylogenetics. It implements and automates many of the functions of the AWTY package in the R environment, as well as a host of other functions. Warren, Geneva, and Lanfear (2017), <doi:10.1093/molbev/msw279>.
Maintained by Dan Warren. Last updated 4 years ago.
6.4 match 30 stars 7.32 score 117 scripts
rspatial
terra:Spatial Data Analysis
Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).
Maintained by Robert J. Hijmans. Last updated 23 hours ago.
geospatialrasterspatialvectoronetbbprojgdalgeoscpp
2.3 match 559 stars 17.64 score 17k scripts 851 dependentscran
mgwrsar:GWR, Mixed GWR and Multiscale GWR with Spatial Autocorrelation
Functions for computing (Mixed and Multiscale) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.
Maintained by Ghislain Geniaux. Last updated 24 days ago.
9.7 match 7 stars 4.08 score 34 scripts
uupharmacometrics
xpose4:Diagnostics for Nonlinear Mixed-Effect Models
A model building aid for nonlinear mixed-effects (population) model analysis using NONMEM, facilitating data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison. The methods are described in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) <doi:10.1016/s0169-2607(98)00067-4>.
Maintained by Andrew C. Hooker. Last updated 1 years ago.
diagnosticsnonmempharmacometricspopulation-modelxpose
5.4 match 35 stars 7.30 score 315 scripts
famuvie
breedR:Statistical Methods for Forest Genetic Resources Analysts
Statistical tools to build predictive models for the breeders community. It aims to assess the genetic value of individuals under a number of situations, including spatial autocorrelation, genetic/environment interaction and competition. It is under active development as part of the Trees4Future project, particularly developed having forest genetic trials in mind. But can be used for animals or other situations as well.
Maintained by Facundo Muñoz. Last updated 8 months ago.
7.2 match 33 stars 5.44 score 24 scriptsbioc
cellmigRation:Track Cells, Analyze Cell Trajectories and Compute Migration Statistics
Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions.
Maintained by Waldir Leoncio. Last updated 5 months ago.
cellbiologydatarepresentationdataimportbioconductor-packagecell-trackingshinytrajectory-analysis
8.5 match 4.60 score 4 scriptsrspatial
raster:Geographic Data Analysis and Modeling
Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.
Maintained by Robert J. Hijmans. Last updated 2 months ago.
2.3 match 164 stars 17.05 score 58k scripts 555 dependents
mpierrejean
acnr:Annotated Copy-Number Regions
This data package provides SNP array data from different types of copy-number regions. These regions were identified manually by the authors of the package and may be used to generate realistic data sets with known truth.
Maintained by Morgane Pierre-Jean. Last updated 3 years ago.
6.8 match 1 stars 5.23 score 19 scripts 3 dependents
dewittpe
qwraps2:Quick Wraps 2
A collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics, such as receiver operating curves or Bland-Altman plots, are also provided by 'qwraps2'. 'qwraps2' is a updated version of a package 'qwraps'. The original version 'qwraps' was never submitted to CRAN but can be found at <https://github.com/dewittpe/qwraps/>. The implementation and limited scope of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is fundamentally different from 'qwraps'.
Maintained by Peter DeWitt. Last updated 5 months ago.
3.6 match 37 stars 9.80 score 448 scripts
emmanuelparadis
ape:Analyses of Phylogenetics and Evolution
Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.
Maintained by Emmanuel Paradis. Last updated 12 hours ago.
2.0 match 64 stars 17.22 score 13k scripts 599 dependents
blasbenito
virtualPollen:Simulating Pollen Curves from Virtual Taxa with Different Life and Niche Traits
Tools to generate virtual environmental drivers with a given temporal autocorrelation, and to simulate pollen curves at annual resolution over millennial time-scales based on these drivers and virtual taxa with different life traits and niche features. It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples.
Maintained by Blas M. Benito. Last updated 3 years ago.
7.7 match 5 stars 4.40 score 5 scriptsstan-dev
bayesplot:Plotting for Bayesian Models
Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.
Maintained by Jonah Gabry. Last updated 1 months ago.
bayesianggplot2mcmcpandocstanstatistical-graphicsvisualization
2.0 match 436 stars 16.69 score 6.5k scripts 98 dependents
cdriveraus
ctsem:Continuous Time Structural Equation Modelling
Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . Stan based functions are not available on 32 bit Windows systems at present. <https://cdriver.netlify.app/> contains some tutorial blog posts.
Maintained by Charles Driver. Last updated 11 days ago.
stochastic-differential-equationstime-seriescpp
3.5 match 42 stars 9.58 score 366 scripts 1 dependents
lucas-castillo
samplr:Compare Human Performance to Sampling Algorithms
Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at <https://sampling.warwick.ac.uk>.
Maintained by Lucas Castillo. Last updated 3 days ago.
5.4 match 2 stars 6.02 score 25 scriptscran
lctools:Local Correlation, Spatial Inequalities, Geographically Weighted Regression and Other Tools
Provides researchers and educators with easy-to-learn user friendly tools for calculating key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Global and Local Moran's I), several Geographically Weighted Regression techniques and other Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Maintained by Stamatis Kalogirou. Last updated 12 months ago.
10.5 match 1 stars 3.03 score 53 scriptscran
wavethresh:Wavelets Statistics and Transforms
Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.
Maintained by Guy Nason. Last updated 7 months ago.
5.2 match 5.89 score 41 dependents
bsnatr
tswge:Time Series for Data Science
Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott. It is helpful for data analysis and for time series instruction.
Maintained by Bivin Sadler. Last updated 2 years ago.
11.3 match 2.70 score 496 scripts
rjdverse
rjd3toolkit:Utility Functions around 'JDemetra+ 3.0'
R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to model time series (create outlier regressors, user-defined calendar regressors, UCARIMA models...), to test the presence of trading days or seasonal effects and also to set specifications in pre-adjustment and benchmarking when using rjd3x13 or rjd3tramoseats.
Maintained by Tanguy Barthelemy. Last updated 5 months ago.
jdemetraseasonal-adjustmenttimeseriesopenjdk
5.3 match 5 stars 5.81 score 48 scripts 15 dependents
earowang
sugrrants:Supporting Graphs for Analysing Time Series
Provides 'ggplot2' graphics for analysing time series data. It aims to fit into the 'tidyverse' and grammar of graphics framework for handling temporal data.
Maintained by Earo Wang. Last updated 1 years ago.
statistical-graphicstime-series
4.0 match 82 stars 7.42 score 214 scripts 1 dependents
thiyangt
seer:Feature-Based Forecast Model Selection
A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.
Maintained by Thiyanga Talagala. Last updated 2 years ago.
5.5 match 78 stars 5.31 score 52 scripts
mrc-ide
drjacoby:Flexible Markov Chain Monte Carlo via Reparameterization
drjacoby is an R package for performing Bayesian inference via Markov chain monte carlo (MCMC). In addition to being highly flexible it implements some advanced techniques that can improve mixing in tricky situations.
Maintained by Bob Verity. Last updated 9 months ago.
4.5 match 12 stars 6.27 score 77 scriptsr-forge
car:Companion to Applied Regression
Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019.
Maintained by John Fox. Last updated 5 months ago.
1.8 match 15.29 score 43k scripts 901 dependents
dosorio
Peptides:Calculate Indices and Theoretical Physicochemical Properties of Protein Sequences
Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot 'XVG' output files from the 'GROMACS' molecular dynamics package.
Maintained by Daniel Osorio. Last updated 1 years ago.
bioinformaticscalculate-indicespeptidesprotein-sequencesqsarcpp
3.0 match 82 stars 9.14 score 245 scripts 7 dependents
smac-group
simts:Time Series Analysis Tools
A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.
Maintained by Stéphane Guerrier. Last updated 2 years ago.
rcpprcpparmadillosimulationtime-seriestimeseriestimeseries-dataopenblascpp
3.5 match 15 stars 7.68 score 59 scripts 4 dependents
geodacenter
rgeoda:R Library for Spatial Data Analysis
Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. The 'GeoDa' software and its documentation are available at <https://geodacenter.github.io>.
Maintained by Xun Li. Last updated 9 days ago.
dataanalysisgeodageospatialcpp
3.5 match 73 stars 7.85 score 179 scripts 1 dependentsbioc
GSVA:Gene Set Variation Analysis for Microarray and RNA-Seq Data
Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.
Maintained by Robert Castelo. Last updated 5 days ago.
functionalgenomicsmicroarrayrnaseqpathwaysgenesetenrichmentgene-set-enrichmentgenomicspathway-enrichment-analysis
1.8 match 210 stars 14.72 score 1.6k scripts 19 dependents
sjuhl
spfilteR:Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models
Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.
Maintained by Sebastian Juhl. Last updated 23 days ago.
5.0 match 7 stars 5.15 score 10 scriptsadeverse
ade4:Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences
Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.
Maintained by Aurélie Siberchicot. Last updated 12 days ago.
1.7 match 39 stars 14.96 score 2.2k scripts 256 dependents
babaknaimi
elsa:Entropy-Based Local Indicator of Spatial Association
A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) <doi:10.1016/j.spasta.2018.10.001>.
Maintained by Babak Naimi. Last updated 1 years ago.
4.8 match 14 stars 5.23 score 24 scriptsbioc
HiContacts:Analysing cool files in R with HiContacts
HiContacts provides a collection of tools to analyse and visualize Hi-C datasets imported in R by HiCExperiment.
Maintained by Jacques Serizay. Last updated 5 months ago.
4.3 match 12 stars 5.95 score 49 scripts
ovgu-sh
desk:Didactic Econometrics Starter Kit
Written to help undergraduate as well as graduate students to get started with R for basic econometrics without the need to import specific functions and datasets from many different sources. Primarily, the package is meant to accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2024, ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook Auer, L.v. (2023, ISBN: 978-3-658-42699-6).
Maintained by Soenke Hoffmann. Last updated 11 months ago.
5.7 match 4.30 score 10 scripts
distancedevelopment
dsm:Density Surface Modelling of Distance Sampling Data
Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.
Maintained by Laura Marshall. Last updated 2 years ago.
4.0 match 8 stars 6.09 score 146 scriptscran
ftsa:Functional Time Series Analysis
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
Maintained by Han Lin Shang. Last updated 22 days ago.
4.0 match 6 stars 5.95 score 96 scripts 10 dependents
luca-scr
qcc:Quality Control Charts
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Maintained by Luca Scrucca. Last updated 2 years ago.
2.0 match 46 stars 11.29 score 730 scripts 6 dependents
brian-j-smith
boa:Bayesian Output Analysis Program (BOA) for MCMC
A menu-driven program and library of functions for carrying out convergence diagnostics and statistical and graphical analysis of Markov chain Monte Carlo sampling output.
Maintained by Brian J. Smith. Last updated 9 years ago.
6.3 match 1 stars 3.58 score 38 scripts
pecanproject
PEcAn.assim.batch:PEcAn Functions Used for Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by Istem Fer. Last updated 2 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
2.3 match 216 stars 9.94 score 20 scripts 2 dependentscran
FinTS:Companion to Tsay (2005) Analysis of Financial Time Series
R companion to Tsay (2005) Analysis of Financial Time Series, second edition (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11.
Maintained by Georgi N. Boshnakov. Last updated 1 years ago.
4.1 match 4 stars 5.39 score 948 scripts 6 dependents
nxskok
mkac:Mann-Kendall correlation and Theil-Sen slope for possibly autocorrelated time series
A re-do of part of the fume package to be (hopefully) feasible for larger data sets.
Maintained by Ken Butler. Last updated 1 years ago.
9.5 match 4 stars 2.30 score 6 scripts
opengeos
whitebox:'WhiteboxTools' R Frontend
An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.
Maintained by Andrew Brown. Last updated 5 months ago.
geomorphometrygeoprocessinggeospatialgishydrologyremote-sensingrstudio
2.3 match 173 stars 9.65 score 203 scripts 2 dependents
jeffreyevans
spatialEco:Spatial Analysis and Modelling Utilities
Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.
Maintained by Jeffrey S. Evans. Last updated 13 days ago.
biodiversityconservationecologyr-spatialrasterspatialvector
2.3 match 110 stars 9.55 score 736 scripts 2 dependents
ropensci
waywiser:Ergonomic Methods for Assessing Spatial Models
Assessing predictive models of spatial data can be challenging, both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors, with "hot spots" of higher than expected error clustered geographically due to spatial structure in the underlying data. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the 'tidymodels' framework. Methods include Moran's I ('Moran' (1950) <doi:10.2307/2332142>), Geary's C ('Geary' (1954) <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from 'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>) and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an implementation of the area of applicability methodology from 'Meyer' and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an implementation of multi-scale assessment as described in 'Riemann' 'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).
Maintained by Michael Mahoney. Last updated 13 days ago.
spatialspatial-analysistidymodelstidyverse
3.1 match 37 stars 6.87 score 19 scripts
squidlobster
castor:Efficient Phylogenetics on Large Trees
Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.
Maintained by Stilianos Louca. Last updated 4 months ago.
3.6 match 2 stars 5.75 score 450 scripts 9 dependents
jeromeecoac
seewave:Sound Analysis and Synthesis
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) <doi:10.1080/09524622.2008.9753600> and Sueur (2018) <doi:10.1007/978-3-319-77647-7>.
Maintained by Jerome Sueur. Last updated 1 years ago.
2.3 match 18 stars 8.88 score 880 scripts 23 dependents
topepo
sparsediscrim:Sparse and Regularized Discriminant Analysis
A collection of sparse and regularized discriminant analysis methods intended for small-sample, high-dimensional data sets. The package features the High-Dimensional Regularized Discriminant Analysis classifier from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012) <doi:10.1093/bioinformatics/btr690>.
Maintained by Max Kuhn. Last updated 4 years ago.
5.0 match 3 stars 4.11 score 86 scripts
almutveraart
trawl:Estimation and Simulation of Trawl Processes
Contains R functions for simulating and estimating integer-valued trawl processes as described in the article Veraart (2019),"Modeling, simulation and inference for multivariate time series of counts using trawl processes", Journal of Multivariate Analysis, 169, pages 110-129, <doi:10.1016/j.jmva.2018.08.012> and for simulating random vectors from the bivariate negative binomial and the bi- and trivariate logarithmic series distributions.
Maintained by Almut E. D. Veraart. Last updated 4 years ago.
7.1 match 2.81 score 32 scripts
yxlin
ggdmc:Cognitive Models
The package provides tools to fit the LBA, DDM, PM and 2-D diffusion models, using the population-based Markov Chain Monte Carlo.
Maintained by Yi-Shin Lin. Last updated 7 months ago.
4.3 match 19 stars 4.66 score 24 scripts
duncanobrien
EWSmethods:Forecasting Tipping Points at the Community Level
Rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012) <doi:10.1371/journal.pone.0041010>, Deb et al. (2022) <doi:10.1098/rsos.211475>, Drake and Griffen (2010) <doi:10.1038/nature09389>, Ushio et al. (2018) <doi:10.1038/nature25504> and Weinans et al. (2021) <doi:10.1038/s41598-021-87839-y> for methodological details. Graphical presentation of the outputs are also provided for clear and publishable figures. Visit the 'EWSmethods' website for more information, and tutorials.
Maintained by Duncan OBrien. Last updated 7 months ago.
3.4 match 8 stars 5.51 score 20 scripts
mattmar
rasterdiv:Diversity Indices for Numerical Matrices
Provides methods to calculate diversity indices on numerical matrices based on information theory, as described in Rocchini, Marcantonio and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039>, and Rocchini et al. (2021) <doi:10.1101/2021.01.23.427872>.
Maintained by Matteo Marcantonio. Last updated 20 days ago.
2.5 match 15 stars 7.65 score 44 scripts 1 dependents
highamm
sptotal:Predicting Totals and Weighted Sums from Spatial Data
Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.
Maintained by Matt Higham. Last updated 7 months ago.
3.8 match 4 stars 4.90 score 10 scriptspecanproject
PEcAnRTM:PEcAn Functions Used for Radiative Transfer Modeling
Functions for performing forward runs and inversions of radiative transfer models (RTMs). Inversions can be performed using maximum likelihood, or more complex hierarchical Bayesian methods. Underlying numerical analyses are optimized for speed using Fortran code.
Maintained by Alexey Shiklomanov. Last updated 2 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsfortranjagscpp
1.9 match 216 stars 9.68 score 132 scripts
kvasilopoulos
ivx:Robust Econometric Inference
Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) <doi:10.1017/S0266466608090154> and Kostakis, Magdalinos and Stamatogiannis (2015) <doi:10.1093/rfs/hhu139>.
Maintained by Kostas Vasilopoulos. Last updated 4 years ago.
inferenceivxlocal-to-unitypredictive-regressionsopenblascpp
4.5 match 17 stars 3.93 score 7 scriptsrobjhyndman
fma:Data Sets from "Forecasting: Methods and Applications" by Makridakis, Wheelwright & Hyndman (1998)
All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.
Maintained by Rob Hyndman. Last updated 1 years ago.
2.0 match 19 stars 8.74 score 336 scripts 2 dependents
eckleyi
LS2W:Locally Stationary Two-Dimensional Wavelet Process Estimation Scheme
Estimates two-dimensional local wavelet spectra.
Maintained by Idris Eckley. Last updated 2 years ago.
8.9 match 1.88 score 25 scripts 1 dependentscran
spnaf:Spatial Network Autocorrelation for Flow Data
Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* proposed by 'Berglund' and 'Karlström' (1999) <doi:10.1007/s101090050013>. The metric, an extended statistic of 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, detects a group of flows having similar traits in terms of directionality. You provide OD data and the associated polygon to get results with several parameters, some of which are defined by spdep package.
Maintained by Youngbin Lee. Last updated 8 months ago.
5.2 match 3.18 score 2 scriptsbioc
HPiP:Host-Pathogen Interaction Prediction
HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations.
Maintained by Matineh Rahmatbakhsh. Last updated 5 months ago.
proteomicssystemsbiologynetworkinferencestructuralpredictiongenepredictionnetwork
3.3 match 3 stars 4.95 score 6 scripts
almutveraart
ambit:Simulation and Estimation of Ambit Processes
Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes.
Maintained by Almut E. D. Veraart. Last updated 3 years ago.
5.3 match 3.00 score 5 scripts
mikejseo
bnma:Bayesian Network Meta-Analysis using 'JAGS'
Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.
Maintained by Michael Seo. Last updated 1 years ago.
3.5 match 7 stars 4.54 score 7 scriptscran
wwntests:Hypothesis Tests for Functional Time Series
Provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, and Kim et al. (2023) <doi: 10.1214/23-SS143> respectively.
Maintained by Mihyun Kim. Last updated 1 years ago.
6.8 match 2 stars 2.30 score 3 scripts
carlosm77
bispdep:Statistical Tools for Bivariate Spatial Dependence Analysis
A collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established.
Maintained by Carlos Melo. Last updated 23 days ago.
5.9 match 2.60 scorebioc
pengls:Fit Penalised Generalised Least Squares models
Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data.
Maintained by Stijn Hawinkel. Last updated 5 months ago.
transcriptomicsregressiontimecoursespatial
3.8 match 4.00 score 4 scripts
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.
1.9 match 38 stars 8.09 score 75 scripts 26 dependents
rbarkerclarke
gtexture:Generalized Application of Co-Occurrence Matrices and Haralick Texture
Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was used in our publication, Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.
Maintained by Rowan Barker-Clarke. Last updated 12 months ago.
5.0 match 3.00 score 1 scriptscran
TSdist:Distance Measures for Time Series Data
A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series. U. Mori, A. Mendiburu and J.A. Lozano (2016), <doi:10.32614/RJ-2016-058>.
Maintained by Usue Mori. Last updated 3 years ago.
3.9 match 5 stars 3.85 score 94 scripts 5 dependents
adunaic
lpacf:Local Partial Autocorrelation Function Estimation for Locally Stationary Wavelet Processes
Provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.
Maintained by Rebecca Killick. Last updated 2 years ago.
10.1 match 1.48 score 2 scripts 1 dependents
dwarton
ecostats:Code and Data Accompanying the Eco-Stats Text (Warton 2022)
Functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises.
Maintained by David Warton. Last updated 1 years ago.
2.2 match 8 stars 6.58 score 53 scripts
flr
FLCore:Core Package of FLR, Fisheries Modelling in R
Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.
Maintained by Iago Mosqueira. Last updated 10 days ago.
fisheriesflrfisheries-modelling
1.7 match 16 stars 8.78 score 956 scripts 23 dependents
torewentzel-larsen
crossrun:Joint Distribution of Number of Crossings and Longest Run
Joint distribution of number of crossings and the longest run in a series of independent Bernoulli trials. The computations uses an iterative procedure where computations are based on results from shorter series. The procedure conditions on the start value and partitions by further conditioning on the position of the first crossing (or none).
Maintained by Tore Wentzel-Larsen. Last updated 3 years ago.
3.3 match 4.45 score 14 scripts
stats-uoa
s20x:Functions for University of Auckland Course STATS 201/208 Data Analysis
A set of functions used in teaching STATS 201/208 Data Analysis at the University of Auckland. The functions are designed to make parts of R more accessible to a large undergraduate population who are mostly not statistics majors.
Maintained by James Curran. Last updated 2 years ago.
2.3 match 3 stars 6.40 score 211 scripts 3 dependentscran
fBasics:Rmetrics - Markets and Basic Statistics
Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management.
Maintained by Georgi N. Boshnakov. Last updated 7 months ago.
2.0 match 2 stars 7.11 score 129 dependents
matthewclegg
egcm:Engle-Granger Cointegration Models
An easy-to-use implementation of the Engle-Granger two-step procedure for identifying pairs of cointegrated series. It is geared towards the analysis of pairs of securities. Summary and plot functions are provided, and the package is able to fetch closing prices of securities from Yahoo. A variety of unit root tests are supported, and an improved unit root test is included.
Maintained by Matthew Clegg. Last updated 2 years ago.
3.0 match 17 stars 4.69 score 57 scripts
santiagobarreda
phonTools:Tools for Phonetic and Acoustic Analyses
Contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files.
Maintained by Santiago Barreda. Last updated 1 years ago.
2.3 match 4 stars 6.21 score 157 scripts 7 dependents
agandy
mcunit:Unit Tests for MC Methods
Unit testing for Monte Carlo methods, particularly Markov Chain Monte Carlo (MCMC) methods, are implemented as extensions of the 'testthat' package. The MCMC methods check whether the MCMC chain has the correct invariant distribution. They do not check other properties of successful samplers such as whether the chain can reach all points, i.e. whether is recurrent. The tests require the ability to sample from the prior and to run steps of the MCMC chain. The methodology is described in Gandy and Scott (2020) <arXiv:2001.06465>.
Maintained by Axel Gandy. Last updated 3 years ago.
3.4 match 4.00 score 1 scripts
liamdbailey
climwin:Climate Window Analysis
Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response. Please see van de Pol et al. (2016) <doi:10.1111/2041-210X.12590> and Bailey and van de Pol (2016) <doi:10.1371/journal.pone.0167980> for details.
Maintained by Liam D. Bailey. Last updated 5 years ago.
1.8 match 12 stars 7.42 score 138 scripts
adrian-bowman
sm:Smoothing Methods for Nonparametric Regression and Density Estimation
This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Maintained by Adrian Bowman. Last updated 1 years ago.
1.9 match 1 stars 6.99 score 732 scripts 36 dependents
xcding1212
Sie2nts:Sieve Methods for Non-Stationary Time Series
We provide functions for estimation and inference of locally-stationary time series using the sieve methods and bootstrapping procedure. In addition, it also contains functions to generate Daubechies and Coiflet wavelet by Cascade algorithm and to process data visualization.
Maintained by Xiucai Ding. Last updated 2 years ago.
5.3 match 2.48 score 2 scripts 1 dependents
vabar
vibass:Valencia International Bayesian Summer School
Materials for the introductory course on Bayesian inference. Practicals, data and interactive apps.
Maintained by Facundo Muñoz. Last updated 8 months ago.
2.4 match 7 stars 5.40 score 2 scripts
angusian
ltsa:Linear Time Series Analysis
Methods of developing linear time series modelling. Methods are given for loglikelihood computation, forecasting and simulation.
Maintained by A.I. McLeod. Last updated 6 months ago.
3.8 match 3.43 score 47 scripts 11 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
2.0 match 42 stars 6.43 score 11 scripts
green-striped-gecko
PopGenReport:A Simple Framework to Analyse Population and Landscape Genetic Data
Provides beginner friendly framework to analyse population genetic data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit <http://www.popgenreport.org/>.
Maintained by Bernd Gruber. Last updated 1 years ago.
1.8 match 5 stars 7.27 score 82 scripts 1 dependents
stscl
sdsfun:Spatial Data Science Complementary Features
Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.
Maintained by Wenbo Lv. Last updated 15 days ago.
geoinformaticsspatial-data-analysisspatial-data-sciencespatial-statisticsopenblascppopenmp
1.9 match 16 stars 6.58 score 6 scripts 8 dependents
s-mckay-curtis
mcmcplots:Create Plots from MCMC Output
Functions for convenient plotting and viewing of MCMC output.
Maintained by S. McKay Curtis. Last updated 7 years ago.
1.9 match 4 stars 6.53 score 880 scripts 4 dependentscran
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.
1.8 match 8 stars 6.78 score 94 dependents
vlyubchich
funtimes:Functions for Time Series Analysis
Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.
Maintained by Vyacheslav Lyubchich. Last updated 2 years ago.
1.8 match 7 stars 6.69 score 93 scripts
jazznbass
scan:Single-Case Data Analyses for Single and Multiple Baseline Designs
A collection of procedures for analysing, visualising, and managing single-case data. These include piecewise linear regression models, multilevel models, overlap indices ('PND', 'PEM', 'PAND', 'PET', 'tau-u', 'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions support outlier detection, handling missing values, scaling, and custom transformations. An export function helps to generate html, word, and latex tables in a publication friendly style. More details can be found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2025) <https://jazznbass.github.io/scan-Book/>.
Maintained by Juergen Wilbert. Last updated 15 days ago.
1.9 match 4 stars 6.42 score 62 scripts 1 dependents
mandymejia
templateICAr:Estimate Brain Networks and Connectivity with ICA and Empirical Priors
Implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the spatial template ICA model proposed in proposed in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level brain as deviations from known population-level networks, which are estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats.
Maintained by Amanda Mejia. Last updated 3 days ago.
1.9 match 10 stars 6.35 score 25 scripts
jolars
tactile:New and Extended Plots, Methods, and Panel Functions for 'lattice'
Extensions to 'lattice', providing new high-level functions, methods for existing functions, panel functions, and a theme.
Maintained by Johan Larsson. Last updated 2 years ago.
latticelinear-modelsplottingtime-series
1.9 match 7 stars 6.33 score 154 scripts
weecology
LDATS:Latent Dirichlet Allocation Coupled with Time Series Analyses
Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.
Maintained by Juniper L. Simonis. Last updated 5 years ago.
changepointldaparallel-temperingportalsoftmax
1.7 match 25 stars 6.93 score 45 scripts
craddm
eegUtils:Utilities for Electroencephalographic (EEG) Analysis
Electroencephalography data processing and visualization tools. Includes import functions for 'BioSemi' (.BDF), 'Neuroscan' (.CNT), 'Brain Vision Analyzer' (.VHDR), 'EEGLAB' (.set) and 'Fieldtrip' (.mat). Many preprocessing functions such as referencing, epoching, filtering, and ICA are available. There are a variety of visualizations possible, including timecourse and topographical plotting.
Maintained by Matt Craddock. Last updated 5 months ago.
eegeeg-analysiseeg-dataeeg-signalseeg-signals-processingopenblascppopenmp
1.8 match 106 stars 6.54 score 82 scriptscran
SSDforR:Functions to Analyze Single System Data
Functions to visually and statistically analyze single system data.
Maintained by Charles Auerbach. Last updated 3 months ago.
8.0 match 1.48 score
pbs-software
PBSmodelling:GUI Tools Made Easy: Interact with Models and Explore Data
Provides software to facilitate the design, testing, and operation of computer models. It focuses particularly on tools that make it easy to construct and edit a customized graphical user interface ('GUI'). Although our simplified 'GUI' language depends heavily on the R interface to the 'Tcl/Tk' package, a user does not need to know 'Tcl/Tk'. Examples illustrate models built with other R packages, including 'PBSmapping', 'PBSddesolve', and 'BRugs'. A complete user's guide 'PBSmodelling-UG.pdf' shows how to use this package effectively.
Maintained by Rowan Haigh. Last updated 4 months ago.
1.7 match 2 stars 6.76 score 120 scripts 4 dependentsbioc
SynExtend:Tools for Working With Synteny Objects
Shared order between genomic sequences provide a great deal of information. Synteny objects produced by the R package DECIPHER provides quantitative information about that shared order. SynExtend provides tools for extracting information from Synteny objects.
Maintained by Nicholas Cooley. Last updated 3 days ago.
geneticsclusteringcomparativegenomicsdataimportfortranopenmp
1.8 match 1 stars 6.42 score 77 scripts
pmontman
TSclust:Time Series Clustering Utilities
A set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.
Maintained by Pablo Montero Manso. Last updated 5 years ago.
2.0 match 2 stars 5.76 score 170 scripts 8 dependents
rogih
acfMPeriod:Robust Estimation of the ACF from the M-Periodogram
Non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) <doi:10.1002/9780470316917>. The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.
Maintained by Higor Cotta. Last updated 6 years ago.
5.7 match 2.00 score
a91quaini
intrinsicFRP:An R Package for Factor Model Asset Pricing
Functions for evaluating and testing asset pricing models, including estimation and testing of factor risk premia, selection of "strong" risk factors (factors having nonzero population correlation with test asset returns), heteroskedasticity and autocorrelation robust covariance matrix estimation and testing for model misspecification and identification. The functions for estimating and testing factor risk premia implement the Fama-MachBeth (1973) <doi:10.1086/260061> two-pass approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) <doi:10.1111/jofi.12035>, and the approaches based on tradable factor risk premia of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683>. The functions for selecting the "strong" risk factors are based on the Oracle estimator of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683> and the factor screening procedure of Gospodinov-Kan-Robotti (2014) <doi:10.2139/ssrn.2579821>. The functions for evaluating model misspecification implement the HJ model misspecification distance of Kan-Robotti (2008) <doi:10.1016/j.jempfin.2008.03.003>, which is a modification of the prominent Hansen-Jagannathan (1997) <doi:10.1111/j.1540-6261.1997.tb04813.x> distance. The functions for testing model identification specialize the Kleibergen-Paap (2006) <doi:10.1016/j.jeconom.2005.02.011> and the Chen-Fang (2019) <doi:10.1111/j.1540-6261.1997.tb04813.x> rank test to the regression coefficient matrix of test asset returns on risk factors. Finally, the function for heteroskedasticity and autocorrelation robust covariance estimation implements the Newey-West (1994) <doi:10.2307/2297912> covariance estimator.
Maintained by Alberto Quaini. Last updated 8 months ago.
factor-modelsfactor-selectionfinanceidentification-testsmisspecificationrcpparmadillorisk-premiumopenblascppopenmp
2.6 match 7 stars 4.45 score 1 scriptsstscl
gdverse:Analysis of Spatial Stratified Heterogeneity
Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, while also taking into account local spatial dependencies, spatial interpretability, complex spatial interactions, and robust spatial stratification. Additionally, it supports the spatial stratified heterogeneity family established in academic literature.
Maintained by Wenbo Lv. Last updated 2 days ago.
geographical-detectorgeoinformaticsgeospatial-analysisspatial-statisticsspatial-stratified-heterogeneitycpp
1.3 match 32 stars 9.07 score 41 scripts 2 dependents
maarten14c
coffee:Chronological Ordering for Fossils and Environmental Events
While individual calibrated radiocarbon dates can span several centuries, combining multiple dates together with any chronological constraints can make a chronology much more robust and precise. This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 <doi:10.11141/ia.13.2>. Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 <doi:10.1017/S0003598X00080534>; Nicholls and Jones 2001 <doi:10.1111/1467-9876.00250>). The paper accompanying this R package is Blaauw et al. 2024 <doi:10.1017/RDC.2024.56>.
Maintained by Maarten Blaauw. Last updated 3 months ago.
1.9 match 7 stars 6.02 score 6 scripts
chrhennig
prabclus:Functions for Clustering and Testing of Presence-Absence, Abundance and Multilocus Genetic Data
Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview.
Maintained by Christian Hennig. Last updated 6 months ago.
1.9 match 1 stars 5.99 score 90 scripts 71 dependentsbioc
VanillaICE:A Hidden Markov Model for high throughput genotyping arrays
Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays.
Maintained by Robert Scharpf. Last updated 5 months ago.
2.0 match 5.53 score 63 scripts 1 dependents
bioxgeo
geodiv:Methods for Calculating Gradient Surface Metrics
Methods for calculating gradient surface metrics for continuous analysis of landscape features.
Maintained by Annie C. Smith. Last updated 1 years ago.
1.9 match 11 stars 5.88 score 23 scripts 1 dependents
bioc
PrInCE:Predicting Interactomes from Co-Elution
PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE.
Maintained by Michael Skinnider. Last updated 5 months ago.
proteomicssystemsbiologynetworkinference
1.7 match 8 stars 6.38 score 25 scripts
jwiley
multilevelTools:Multilevel and Mixed Effects Model Diagnostics and Effect Sizes
Effect sizes, diagnostics and performance metrics for multilevel and mixed effects models. Includes marginal and conditional 'R2' estimates for linear mixed effects models based on Johnson (2014) <doi:10.1111/2041-210X.12225>.
Maintained by Joshua F. Wiley. Last updated 12 months ago.
1.9 match 4 stars 5.74 score 136 scripts
gtromano
DeCAFS:Detecting Changes in Autocorrelated and Fluctuating Signals
Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2021) <doi:10.1080/01621459.2021.1909598>.
Maintained by Gaetano Romano. Last updated 2 years ago.
change-detectionchangepoint-detectiontime-series-analysiscpp
3.5 match 2 stars 3.00 score 2 scripts
saviviro
sstvars:Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models
Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.
Maintained by Savi Virolainen. Last updated 17 days ago.
1.6 match 4 stars 6.36 score 41 scriptscran
GNAR:Methods for Fitting Network Time Series Models
Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables. Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>. Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.
Maintained by Matt Nunes. Last updated 6 months ago.
7.8 match 2 stars 1.30 score
alexhroom
rshift:Paleoecology Functions for Regime Shift Analysis
Contains a variety of functions, based around regime shift analysis of paleoecological data. Citations: Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448> Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L> Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680) rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276> Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795> Sample data set January_PDO from NOAA: <https://www.ncei.noaa.gov/access/monitoring/pdo/>.
Maintained by Alex H. Room. Last updated 2 months ago.
ecologypaleoecologyregime-shiftrustcargo
2.3 match 4 stars 4.38 score 8 scripts
anespinosa
netmem:Social Network Measures using Matrices
Measures to describe and manipulate networks using matrices.
Maintained by Alejandro Espinosa-Rada. Last updated 5 days ago.
matricesmultilayer-networksnetwork-analysisnetwork-sciencesnasocial-networksocial-network-analysissociology
2.3 match 11 stars 4.33 score 13 scripts
florale
multilevelcoda:Estimate Bayesian Multilevel Models for Compositional Data
Implement Bayesian Multilevel Modelling for compositional data in a multilevel framework. Compute multilevel compositional data and Isometric log ratio (ILR) at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2024) <doi:10.48550/arXiv.2405.03985>, Le, Dumuid, Stanford, and Wiley (2024) <doi:10.48550/arXiv.2411.12407>.
Maintained by Flora Le. Last updated 3 days ago.
bayesian-inferencecompositional-data-analysismultilevel-modelsmultilevelcoda
1.2 match 14 stars 8.31 score 118 scriptsbioc
mfa:Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations
MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers.
Maintained by Kieran Campbell. Last updated 5 months ago.
immunooncologyrnaseqgeneexpressionbayesiansinglecellcpp
2.0 match 4.85 score 35 scripts
levisc8
spind:Spatial Methods and Indices
Functions for spatial methods based on generalized estimating equations (GEE) and wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR), conducting multi-model inference, and stepwise model selection. Further, contains functions for spatially corrected model accuracy measures.
Maintained by Sam Levin. Last updated 12 months ago.
2.0 match 3 stars 4.84 score 46 scripts
andyphilips
dynamac:Dynamic Simulation and Testing for Single-Equation ARDL Models
While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).
Maintained by Soren Jordan. Last updated 4 years ago.
ardlstatatime-seriestime-series-analysis
1.7 match 7 stars 5.59 score 37 scripts 1 dependents
jonathansmart
BayesGrowth:Estimate Fish Growth Using MCMC Analysis
Estimate fish length-at-age models using MCMC analysis with 'rstan' models. This package allows a multimodel approach to growth fitting to be applied to length-at-age data and is supported by further analyses to determine model selection and result presentation. The core methods of this package are presented in Smart and Grammer (2021) "Modernising fish and shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2): e0246734 <doi:10.1371/journal.pone.0246734>.
Maintained by Jonathan Smart. Last updated 1 years ago.
2.0 match 11 stars 4.74 score 8 scripts
nicokubi
penetrance:Methods for Penetrance Estimation in Family-Based Studies
Implements statistical methods for estimating disease penetrance in family-based studies. Penetrance refers to the probability of disease§ manifestation in individuals carrying specific genetic variants. The package provides tools for age-specific penetrance estimation, handling missing data, and accounting for ascertainment bias in family studies. Cite as: Kubista, N., Braun, D. & Parmigiani, G. (2024) <doi:10.48550/arXiv.2411.18816>.
Maintained by Nicolas Kubista. Last updated 17 days ago.
1.7 match 5.41 scorer-forge
tscount:Analysis of Count Time Series
Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided, see Liboschik et al. (2017) <doi:10.18637/jss.v082.i05>. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
Maintained by Tobias Liboschik. Last updated 2 years ago.
1.8 match 5.28 score 91 scripts 1 dependents
ssa-statistical-team-projects
povmap:Extension to the 'emdi' Package
The R package 'povmap' supports small area estimation of means and poverty headcount rates. It adds several new features to the 'emdi' package (see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>). These include new options for incorporating survey weights, ex-post benchmarking of estimates, two additional transformations, several new convenient functions to assist with reporting results, and a wrapper function to facilitate access from 'Stata'.
Maintained by Ifeanyi Edochie. Last updated 5 months ago.
2.0 match 1 stars 4.60 score 10 scriptsbioc
smoppix:Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index
Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.
Maintained by Stijn Hawinkel. Last updated 28 days ago.
transcriptomicsspatialsinglecellcpp
1.8 match 1 stars 5.18 score 4 scriptsrogih
tsqn:Applications of the Qn Estimator to Time Series (Univariate and Multivariate)
Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.
Maintained by Higor Cotta. Last updated 6 years ago.
4.1 match 1 stars 2.23 score 17 scripts
tianxia-jia
mcgf:Markov Chain Gaussian Fields Simulation and Parameter Estimation
Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>. It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.
Maintained by Tianxia Jia. Last updated 9 months ago.
1.9 match 1 stars 4.82 score 11 scripts
statmanrobin
Stat2Data:Datasets for Stat2
Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). The package also includes data for the first edition, Stat2: Building Models for a World of Data and a few functions for plotting diagnostics.
Maintained by Robin Lock. Last updated 6 years ago.
1.8 match 5 stars 4.94 score 544 scripts
hjboonstra
mcmcsae:Markov Chain Monte Carlo Small Area Estimation
Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. Such models allow smoothing over space and time and are useful in, for example, small area estimation.
Maintained by Harm Jan Boonstra. Last updated 3 months ago.
3.5 match 2.48 score 8 scripts
thomaswpike
social:Social Autocorrelation
A set of functions to quantify and visualise social autocorrelation.
Maintained by Tom Pike. Last updated 8 years ago.
4.3 match 2.00 score 10 scripts
fcheysson
starma:Modelling Space Time AutoRegressive Moving Average (STARMA) Processes
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
Maintained by Felix Cheysson. Last updated 4 years ago.
3.6 match 2 stars 2.38 score 12 scripts
arni-magnusson
plotMCMC:MCMC Diagnostic Plots
Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the 'coda' and 'lattice' packages, and make it easy to adjust graphical details.
Maintained by Arni Magnusson. Last updated 1 years ago.
2.0 match 1 stars 4.27 score 37 scriptssaviviro
gmvarkit:Estimate Gaussian and Student's t Mixture Vector Autoregressive Models
Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (2022) <doi:10.48550/arXiv.2109.13648>.
Maintained by Savi Virolainen. Last updated 2 months ago.
1.6 match 3 stars 5.32 score 45 scripts
njtierney
mmcc:tidy mcmc.list using data.table
Tidy up, diagnose, and visualise your mcmc samples quickly and easily so you can get on with your analysis.
Maintained by Nicholas Tierney. Last updated 3 years ago.
1.8 match 24 stars 4.68 score 10 scripts
rainers48
tsapp:Time Series, Analysis and Application
Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.
Maintained by Rainer Schlittgen. Last updated 3 years ago.
8.2 match 1.00 score 1 scripts
leilamarvian
ADTSA:Time Series Analysis
Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.
Maintained by Leila Marvian Mashhad. Last updated 1 years ago.
8.2 match 1 stars 1.00 score
steve-the-bayesian
Boom:Bayesian Object Oriented Modeling
A C++ library for Bayesian modeling, with an emphasis on Markov chain Monte Carlo. Although boom contains a few R utilities (mainly plotting functions), its primary purpose is to install the BOOM C++ library on your system so that other packages can link against it.
Maintained by Steven L. Scott. Last updated 1 years ago.
1.7 match 9 stars 4.82 score 57 scripts 6 dependentssaviviro
uGMAR:Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models
Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR), Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) <doi:10.1080/03610926.2021.1916531>, Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.
Maintained by Savi Virolainen. Last updated 2 months ago.
1.7 match 1 stars 4.88 score 51 scripts
matthieustigler
partsm:Periodic Autoregressive Time Series Models
Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.
Maintained by Matthieu Stigler. Last updated 4 years ago.
1.8 match 3 stars 4.57 score 25 scripts
c7rishi
BAMBI:Bivariate Angular Mixture Models
Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.
Maintained by Saptarshi Chakraborty. Last updated 5 months ago.
1.7 match 3 stars 4.83 score 65 scripts 1 dependentsbioc
CatsCradle:This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters
This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.
Maintained by Michael Shapiro. Last updated 1 months ago.
biologicalquestionstatisticalmethodgeneexpressionsinglecelltranscriptomicsspatial
1.2 match 3 stars 6.50 score
fernandalschumacher
skewlmm:Scale Mixture of Skew-Normal Linear Mixed Models
It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.
Maintained by Fernanda L. Schumacher. Last updated 2 months ago.
1.7 match 6 stars 4.43 score 10 scripts
nicholasjclark
MRFcov:Markov Random Fields with Additional Covariates
Approximate node interaction parameters of Markov Random Fields graphical networks. Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) <doi:10.1002/ecy.2221>.
Maintained by Nicholas J Clark. Last updated 12 months ago.
conditional-random-fieldsgraphical-modelsmachine-learningmarkov-random-fieldmultivariate-analysismultivariate-statisticsnetwork-analysisnetworks
1.3 match 24 stars 6.03 score 30 scripts
lfduquey
IETD:Inter-Event Time Definition
Computes characteristics of independent rainfall events (duration, total rainfall depth, and intensity) extracted from a sub-daily rainfall time series based on the inter-event time definition (IETD) method. To have a reference value of IETD, it also analyzes/computes IETD values through three methods: autocorrelation analysis, the average annual number of events analysis, and coefficient of variation analysis. Ideal for analyzing the sensitivity of IETD to characteristics of independent rainfall events. Adams B, Papa F (2000) <ISBN: 978-0-471-33217-6>. Joo J et al. (2014) <doi:10.3390/w6010045>. Restrepo-Posada P, Eagleson P (1982) <doi:10.1016/0022-1694(82)90136-6>.
Maintained by Luis F. Duque. Last updated 5 years ago.
2.8 match 1 stars 2.70 score 2 scriptscran
BSS:Brownian Semistationary Processes
Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes to data, and functions to estimate the stochastic volatility process of a BSS process.
Maintained by Phillip Murray. Last updated 5 years ago.
3.7 match 2.00 score 2 scripts
rkillick
EnvCpt:Detection of Structural Changes in Climate and Environment Time Series
Tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call.
Maintained by Rebecca Killick. Last updated 4 years ago.
2.2 match 5 stars 3.34 score 44 scripts
rodivinity
mbreaks:Estimation and Inference for Structural Breaks in Linear Regression Models
Functions provide comprehensive treatments for estimating, inferring, testing and model selecting in linear regression models with structural breaks. The tests, estimation methods, inference and information criteria implemented are discussed in Bai and Perron (1998) "Estimating and Testing Linear Models with Multiple Structural Changes" <doi:10.2307/2998540>.
Maintained by Linh Nguyen. Last updated 4 months ago.
1.8 match 4.04 score 11 scriptsgreen-striped-gecko
dartR.spatial:Applying Landscape Genomic Methods on 'SNP' and 'Silicodart' Data
Provides landscape genomic functions to analyse 'SNP' (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. 'dartR.spatial' is a package that belongs to the 'dartRverse' suit of packages and depends on 'dartR.base' and 'dartR.data'.
Maintained by Bernd Gruber. Last updated 1 years ago.
3.6 match 2.00 score
avm00016
predtoolsTS:Time Series Prediction Tools
Makes the time series prediction easier by automatizing this process using four main functions: prep(), modl(), pred() and postp(). Features different preprocessing methods to homogenize variance and to remove trend and seasonality. Also has the potential to bring together different predictive models to make comparatives. Features ARIMA and Data Mining Regression models (using caret).
Maintained by Alberto Vico Moreno. Last updated 7 years ago.
2.3 match 1 stars 3.20 score 32 scriptsbioc
MinimumDistance:A Package for De Novo CNV Detection in Case-Parent Trios
Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms.
Maintained by Robert Scharpf. Last updated 5 months ago.
microarraysnpcopynumbervariation
2.0 match 3.60 score 10 scripts
clandere
AnaCoDa:Analysis of Codon Data under Stationarity using a Bayesian Framework
Is a collection of models to analyze genome scale codon data using a Bayesian framework. Provides visualization routines and checkpointing for model fittings. Currently published models to analyze gene data for selection on codon usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In addition 'AnaCoDa' contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data.
Maintained by Cedric Landerer. Last updated 4 years ago.
1.8 match 1 stars 4.00 score 100 scripts
friendly
Guerry:Maps, Data and Methods Related to Guerry (1833) "Moral Statistics of France"
Contains maps of France in 1830 and multivariate datasets from A.-M. Guerry and others. Statistical and graphic methods related to Guerry's "Moral Statistics of France" are used to understand Guerry's data and illustrate methods. The goal is to facilitate the exploration and development of statistical and graphic methods for multivariate data in a geospatial context of historical interest.
Maintained by Michael Friendly. Last updated 2 months ago.
francemoral-statisticsmultivariate-spatial-analysis
1.5 match 1 stars 4.72 score 53 scripts
gallegoj
tfarima:Transfer Function and ARIMA Models
Building customized transfer function and ARIMA models with multiple operators and parameter restrictions. Functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment. See Bell and Hillmer (1983) <doi:10.1080/01621459.1983.10478005>, Box, Jenkins, Reinsel and Ljung <ISBN:978-1-118-67502-1>, Box, Pierce and Newbold (1987) <doi:10.1080/01621459.1987.10478430>, Box and Tiao (1975) <doi:10.1080/01621459.1975.10480264>, Chen and Liu (1993) <doi:10.1080/01621459.1993.10594321>.
Maintained by Jose L. Gallego. Last updated 12 months ago.
1.8 match 2 stars 4.04 score 11 scripts
freezenik
BayesX:R Utilities Accompanying the Software Package BayesX
Functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX.
Maintained by Nikolaus Umlauf. Last updated 1 years ago.
1.9 match 3.71 score 48 scripts 3 dependentsstscl
sesp:Spatially Explicit Stratified Power
Assesses spatial associations between variables through an equivalent geographical detector (q-statistic) within a regression framework and incorporates a spatially explicit stratified power model by integrating spatial dependence and spatial stratified heterogeneity, facilitating the modeling of complex spatial relationships.
Maintained by Wenbo Lv. Last updated 2 months ago.
spatial-explicit-geographical-detectorspatial-stratified-heterogeneitycpp
1.3 match 15 stars 5.43 score
giabaio
bmhe:This Package Creates a Set of Functions Useful for Bayesian modelling
A set of utility functions that can be used to post-process BUGS or JAGS objects as well as other to facilitate various Bayesian modelling activities (including in HTA).
Maintained by Gianluca Baio. Last updated 11 days ago.
bayesian-statisticsbugscost-effectiveness-analysisjagstidyverse
2.3 match 2 stars 3.00 score 7 scripts
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.
1.3 match 1 stars 5.24 score 65 scripts 1 dependents
gabrielodom
mvMonitoring:Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring
Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data generated from a continuous-time multivariate industrial or natural process. Employ PCA-based dimension reduction to extract linear combinations of relevant features, reducing computational burdens. For a description of ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al. The multi-state application of ADPCA is from a manuscript under current revision entitled "Multi-State Multivariate Statistical Process Control" by Odom, Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018.
Maintained by Gabriel Odom. Last updated 1 years ago.
1.3 match 4 stars 5.24 score 29 scriptsrjdverse
rjd3revisions:Revision analysis with 'JDemetra+ 3.x'
Revision analysis tool part of 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It performs a battery of tests on revisions and submit a report with the results. The various tests enable the users to detect potential bias and sources of inefficiency in preliminary estimates.
Maintained by Corentin Lemasson. Last updated 7 months ago.
1.3 match 3 stars 4.89 score 4 scriptsr-spatial
spatialreg:Spatial Regression Analysis
A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) <doi:10.3390/math9111276>. 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation <doi:10.1177/016001769702000109>.
Maintained by Roger Bivand. Last updated 3 days ago.
bayesianimpactsmaximum-likelihoodspatial-dependencespatial-econometricsspatial-regressionopenblas
0.5 match 46 stars 12.92 score 916 scripts 24 dependents
hli226
mvnimpute:Simultaneously Impute the Missing and Censored Values
Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.
Maintained by Hesen Li. Last updated 3 years ago.
2.3 match 2.70 scorestan-dev
posteriordb:R functionality for posteriordb
R functionality of easy handling of the posteriordb posteriors.
Maintained by Mans Magnusson. Last updated 2 years ago.
1.8 match 8 stars 3.37 score 59 scriptscran
erer:Empirical Research in Economics with R
Several functions, datasets, and sample codes related to empirical research in economics are included. They cover the marginal effects for binary or ordered choice models, static and dynamic Almost Ideal Demand System (AIDS) models, and a typical event analysis in finance.
Maintained by Changyou Sun. Last updated 6 months ago.
1.8 match 3 stars 3.34 score 211 scripts 1 dependents
pmildenb
SteppedPower:Power Calculation for Stepped Wedge Designs
Tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>, extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.
Maintained by Philipp Mildenberger. Last updated 4 months ago.
1.3 match 4.30 score 5 scripts