Showing 200 of total 2213 results (show query)
ohdsi
FeatureExtraction:Generating Features for a Cohort
An R interface for generating features for a cohort using data in the Common Data Model. Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics.
Maintained by Ger Inberg. Last updated 5 months ago.
88.3 match 62 stars 10.30 score 209 scripts 1 dependentsopenpharma
mmrm:Mixed Models for Repeated Measures
Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.
Maintained by Daniel Sabanes Bove. Last updated 10 days ago.
48.1 match 138 stars 12.15 score 113 scripts 4 dependentsradicalcommecol
cxr:A Toolbox for Modelling Species Coexistence in R
Recent developments in modern coexistence theory have advanced our understanding on how species are able to persist and co-occur with other species at varying abundances. However, applying this mathematical framework to empirical data is still challenging, precluding a larger adoption of the theoretical tools developed by empiricists. This package provides a complete toolbox for modelling interaction effects between species, and calculate fitness and niche differences. The functions are flexible, may accept covariates, and different fitting algorithms can be used. A full description of the underlying methods is available in Garcรญa-Callejas, D., Godoy, O., and Bartomeus, I. (2020) <doi:10.1111/2041-210X.13443>. Furthermore, the package provides a series of functions to calculate dynamics for stage-structured populations across sites.
Maintained by David Garcia-Callejas. Last updated 1 months ago.
85.1 match 10 stars 6.51 score 27 scriptsstatnet
ergm:Fit, Simulate and Diagnose Exponential-Family Models for Networks
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i03> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
Maintained by Pavel N. Krivitsky. Last updated 7 days ago.
32.3 match 100 stars 15.36 score 1.4k scripts 36 dependentsjwb133
smcfcs:Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification
Implements multiple imputation of missing covariates by Substantive Model Compatible Fully Conditional Specification. This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model.
Maintained by Jonathan Bartlett. Last updated 2 days ago.
48.8 match 11 stars 9.00 score 59 scripts 1 dependentscovaruber
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
33.2 match 43 stars 12.70 score 300 scripts 9 dependentsr-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.
27.6 match 14.92 score 11k scripts 887 dependentspln-team
PLNmodels:Poisson Lognormal Models
The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.
Maintained by Julien Chiquet. Last updated 4 days ago.
count-datamultivariate-analysisnetwork-inferencepcapoisson-lognormal-modelopenblascpp
41.4 match 56 stars 9.50 score 226 scriptsjoeguinness
GpGp:Fast Gaussian Process Computation Using Vecchia's Approximation
Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>. Model fitting employs a Fisher scoring algorithm described in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.
Maintained by Joseph Guinness. Last updated 5 months ago.
61.4 match 10 stars 6.16 score 160 scripts 6 dependentsuupharmacometrics
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
43.5 match 35 stars 7.30 score 315 scriptskwstat
agridat:Agricultural Datasets
Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.
Maintained by Kevin Wright. Last updated 28 days ago.
28.8 match 125 stars 11.02 score 1.7k scripts 2 dependentscalvagone
campsis:Generic PK/PD Simulation Platform CAMPSIS
A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R packages 'rxode2' and 'mrgsolve'. CAMPSIS provides an abstraction layer over the underlying processes of writing a PK/PD model, assembling a custom dataset and running a simulation. CAMPSIS has a strong dependency to the R package 'campsismod', which allows to read/write a model from/to files and adapt it further on the fly in the R environment. Package 'campsis' allows the user to assemble a dataset in an intuitive manner. Once the userโs dataset is ready, the package is in charge of preparing the simulation, calling 'rxode2' or 'mrgsolve' (at the user's choice) and returning the results, for the given model, dataset and desired simulation settings.
Maintained by Nicolas Luyckx. Last updated 1 months ago.
41.1 match 8 stars 7.52 score 93 scriptsniehs
amadeus:Accessing and Analyzing Large-Scale Environmental Data
Functions are designed to facilitate access to and utility with large scale, publicly available environmental data in R. The package contains functions for downloading raw data files from web URLs (download_data()), processing the raw data files into clean spatial objects (process_covariates()), and extracting values from the spatial data objects at point and polygon locations (calculate_covariates()). These functions call a series of source-specific functions which are tailored to each data sources/datasets particular URL structure, data format, and spatial/temporal resolution. The functions are tested, versioned, and open source and open access. For sum_edc() method details, see Messier, Akita, and Serre (2012) <doi:10.1021/es203152a>.
Maintained by Kyle Messier. Last updated 19 days ago.
40.5 match 8 stars 7.16 score 13 scriptsbioc
MOFA2:Multi-Omics Factor Analysis v2
The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available.
Maintained by Ricard Argelaguet. Last updated 5 months ago.
dimensionreductionbayesianvisualizationfactor-analysismofamulti-omics
26.6 match 319 stars 10.02 score 502 scriptssmouksassi
coveffectsplot:Produce Forest Plots to Visualize Covariate Effects
Produce forest plots to visualize covariate effects using either the command line or an interactive 'Shiny' application.
Maintained by Samer Mouksassi. Last updated 1 months ago.
32.9 match 32 stars 7.86 score 40 scriptsglmmtmb
glmmTMB:Generalized Linear Mixed Models using Template Model Builder
Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.
Maintained by Mollie Brooks. Last updated 12 days ago.
15.2 match 312 stars 16.77 score 3.7k scripts 24 dependentscran
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.
18.6 match 6 stars 13.00 score 13k scripts 8.7k dependentsdavidbolin
rSPDE:Rational Approximations of Fractional Stochastic Partial Differential Equations
Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.
Maintained by David Bolin. Last updated 9 days ago.
30.9 match 11 stars 7.57 score 188 scripts 3 dependentsfabrice-rossi
mixvlmc:Variable Length Markov Chains with Covariates
Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bรผhlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615> for VLMC with covariates.
Maintained by Fabrice Rossi. Last updated 10 months ago.
machine-learningmarkov-chainmarkov-modelstatisticstime-seriescpp
36.5 match 2 stars 6.23 score 20 scriptsmikewlcheung
metaSEM:Meta-Analysis using Structural Equation Modeling
A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' and 'lavaan' packages. It also implements various procedures to perform meta-analytic structural equation modeling on the correlation and covariance matrices, see Cheung (2015) <doi:10.3389/fpsyg.2014.01521>.
Maintained by Mike Cheung. Last updated 10 days ago.
meta-analysismeta-analytic-semmissing-datamultilevel-modelsmultivariate-analysisstructural-equation-modelingstructural-equation-models
24.0 match 30 stars 9.43 score 208 scripts 1 dependentsinsightrx
PKPDsim:Tools for Performing Pharmacokinetic-Pharmacodynamic Simulations
Simulate dose regimens for pharmacokinetic-pharmacodynamic (PK-PD) models described by differential equation (DE) systems. Simulation using ADVAN-style analytical equations is also supported (Abuhelwa et al. (2015) <doi:10.1016/j.vascn.2015.03.004>).
Maintained by Ron Keizer. Last updated 20 days ago.
odepharmacodynamicspharmacokineticspharmacometricscpp
22.5 match 36 stars 9.47 score 100 scriptsagi-lab
SynthETIC:Synthetic Experience Tracking Insurance Claims
Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" <arXiv:2008.05693>.
Maintained by Melantha Wang. Last updated 1 years ago.
34.0 match 12 stars 6.22 score 23 scripts 2 dependentsmurrayefford
secr:Spatially Explicit Capture-Recapture
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Maintained by Murray Efford. Last updated 3 days ago.
19.9 match 3 stars 10.18 score 410 scripts 5 dependentskisungyou
CovTools:Statistical Tools for Covariance Analysis
Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.
Maintained by Kisung You. Last updated 2 years ago.
covariancecovariance-estimationopenblascpp
44.6 match 13 stars 4.55 score 55 scriptsgenentech
psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation
Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.
Maintained by Matt Secrest. Last updated 1 months ago.
bayesian-dynamic-borrowingpsborrow2simulation-study
25.5 match 18 stars 7.87 score 16 scriptsbraverock
PortfolioAnalytics:Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios
Portfolio optimization and analysis routines and graphics.
Maintained by Brian G. Peterson. Last updated 3 months ago.
17.4 match 81 stars 11.49 score 626 scripts 2 dependentsbsaul
geex:An API for M-Estimation
Provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample variance corrections.
Maintained by Bradley Saul. Last updated 10 months ago.
asymptoticscovariance-estimatescovariance-estimationestimate-parametersestimating-equationsestimationinferencem-estimationrobustsandwich
24.9 match 8 stars 7.70 score 131 scripts 2 dependentsstephenslab
mashr:Multivariate Adaptive Shrinkage
Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.
Maintained by Peter Carbonetto. Last updated 4 months ago.
16.2 match 91 stars 11.04 score 624 scripts 3 dependentsxrobin
pROC:Display and Analyze ROC Curves
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Maintained by Xavier Robin. Last updated 4 months ago.
bootstrappingcovariancehypothesis-testingmachine-learningplotplottingrocroc-curvevariancecpp
11.8 match 125 stars 15.18 score 16k scripts 445 dependentsbioc
RnBeads:RnBeads
RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.
Maintained by Fabian Mueller. Last updated 1 months ago.
dnamethylationmethylationarraymethylseqepigeneticsqualitycontrolpreprocessingbatcheffectdifferentialmethylationsequencingcpgislandimmunooncologytwochanneldataimport
25.0 match 6.85 score 169 scripts 1 dependentsbiodiverse
unmarked:Models for Data from Unmarked Animals
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Maintained by Ken Kellner. Last updated 1 days ago.
13.1 match 4 stars 13.03 score 652 scripts 12 dependentsblasif
cocons:Covariate-Based Covariance Functions for Nonstationary Spatial Modeling
Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.
Maintained by Federico Blasi. Last updated 2 months ago.
covariance-matrixcppestimationgaussian-processeslarge-datasetnonstationarityoptimizationpredictioncpp
31.0 match 3 stars 5.48 score 1 scriptswviechtb
metafor:Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Maintained by Wolfgang Viechtbauer. Last updated 2 days ago.
meta-analysismixed-effectsmultilevel-modelsmultivariate
10.2 match 246 stars 16.30 score 4.9k scripts 92 dependentscran
scam:Shape Constrained Additive Models
Generalized additive models under shape constraints on the component functions of the linear predictor. Models can include multiple shape-constrained (univariate and bivariate) and unconstrained terms. Routines of the package 'mgcv' are used to set up the model matrix, print, and plot the results. Multiple smoothing parameter estimation by the Generalized Cross Validation or similar. See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7> for an overview. A broad selection of shape-constrained smoothers, linear functionals of smooths with shape constraints, and Gaussian models with AR1 residuals.
Maintained by Natalya Pya. Last updated 2 months ago.
23.1 match 5 stars 7.17 score 388 scripts 23 dependentsspatstat
spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family
Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.
Maintained by Adrian Baddeley. Last updated 8 days ago.
analysis-of-variancecluster-processconfidence-intervalscox-processdeterminantal-point-processesgibbs-processinfluenceleveragemodel-diagnosticsneyman-scottparameter-estimationpoisson-processspatial-analysisspatial-modellingspatial-point-processesstatistical-inference
18.2 match 5 stars 9.09 score 6 scripts 46 dependentskkholst
mets:Analysis of Multivariate Event Times
Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.
Maintained by Klaus K. Holst. Last updated 3 days ago.
multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp
12.0 match 14 stars 13.47 score 236 scripts 42 dependentsphilboileau
cvCovEst:Cross-Validated Covariance Matrix Estimation
An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.
Maintained by Philippe Boileau. Last updated 1 years ago.
covariance-matrix-estimationcross-validationhigh-dimensional-statisticsnonparametric-statistics
23.5 match 13 stars 6.78 score 26 scripts 2 dependentsmatteo21q
jomo:Multilevel Joint Modelling Multiple Imputation
Similarly to Schafer's package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi:10.1002/9781119942283>. Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
Maintained by Matteo Quartagno. Last updated 3 years ago.
16.6 match 3 stars 9.58 score 126 scripts 154 dependentsswihart
rmutil:Utilities for Nonlinear Regression and Repeated Measurements Models
A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at <https://www.commanster.eu/rcode.html>).
Maintained by Bruce Swihart. Last updated 2 years ago.
18.9 match 1 stars 8.35 score 358 scripts 70 dependentsconvexfi
fitHeavyTail:Mean and Covariance Matrix Estimation under Heavy Tails
Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).
Maintained by Daniel P. Palomar. Last updated 2 years ago.
cauchycovariance-estimationcovariance-matrixheavy-tailed-distributionsoutliersrobust-estimationstudent-ttyler
23.3 match 22 stars 6.27 score 28 scripts 1 dependentsjonathancornelissen
highfrequency:Tools for Highfrequency Data Analysis
Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity. A detailed vignette can be found in the paper "Analyzing Intraday Financial Data in R: The highfrequency Package" by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>). The DOI in the CITATION is for a new Journal of Statistical Software publication that will be registered after publication on CRAN. A working paper version can be found on SSRN: <doi:10.2139/ssrn.3917548>.
Maintained by Kris Boudt. Last updated 2 years ago.
19.7 match 152 stars 7.37 score 286 scriptsggpmxdevelopment
ggPMX:'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME Models
At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling activities in order to reduce the overall effort required for generating such plots. For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP <doi:10.1002/psp4.12161>. While not required, you can get/install the 'R' 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url <https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/>. When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required Chart Data instead of exporting it from the 'Monolix' gui.
Maintained by Matthew Fidler. Last updated 1 years ago.
19.8 match 39 stars 7.23 score 80 scriptsngreifer
cobalt:Covariate Balance Tables and Plots
Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.
Maintained by Noah Greifer. Last updated 11 months ago.
causal-inferencepropensity-scores
10.7 match 75 stars 12.98 score 1.0k scripts 8 dependentslcbc-uio
galamm:Generalized Additive Latent and Mixed Models
Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).
Maintained by รystein Sรธrensen. Last updated 6 months ago.
generalized-additive-modelshierarchical-modelsitem-response-theorylatent-variable-modelsstructural-equation-modelscpp
18.5 match 29 stars 7.33 score 41 scriptscollinerickson
GauPro:Gaussian Process Fitting
Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.
Maintained by Collin Erickson. Last updated 9 hours ago.
15.7 match 16 stars 8.44 score 104 scripts 1 dependentsrvlenth
emmeans:Estimated Marginal Means, aka Least-Squares Means
Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Maintained by Russell V. Lenth. Last updated 3 days ago.
6.9 match 377 stars 19.19 score 13k scripts 187 dependentscran
mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.
Maintained by Simon Wood. Last updated 1 years ago.
10.3 match 32 stars 12.71 score 17k scripts 7.8k dependentscran
CUB:A Class of Mixture Models for Ordinal Data
For ordinal rating data, estimate and test models within the family of CUB models and their extensions (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions); Simulation routines, plotting facilities and fitting measures are also provided.
Maintained by Rosaria Simone. Last updated 1 years ago.
29.9 match 4.37 score 79 scripts 1 dependentsbachmannpatrick
CLVTools:Tools for Customer Lifetime Value Estimation
A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individualsโ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.
Maintained by Patrick Bachmann. Last updated 3 months ago.
clvcustomer-lifetime-valuecustomer-relationship-managementopenblasgslcppopenmp
19.8 match 55 stars 6.47 score 12 scriptsstochastictree
stochtree:Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference
Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers.
Maintained by Drew Herren. Last updated 18 days ago.
bartbayesian-machine-learningbayesian-methodsdecision-treesgradient-boosted-treesmachine-learningprobabilistic-modelstree-ensemblescpp
14.6 match 20 stars 8.52 score 40 scriptstbates
umx:Structural Equation Modeling and Twin Modeling in R
Quickly create, run, and report structural equation models, and twin models. See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.
Maintained by Timothy C. Bates. Last updated 2 days ago.
behavior-geneticsgeneticsopenmxpsychologysemstatisticsstructural-equation-modelingtutorialstwin-modelsumx
13.2 match 44 stars 9.45 score 472 scriptsbioc
nullranges:Generation of null ranges via bootstrapping or covariate matching
Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package.
Maintained by Michael Love. Last updated 5 months ago.
visualizationgenesetenrichmentfunctionalgenomicsepigeneticsgeneregulationgenetargetgenomeannotationannotationgenomewideassociationhistonemodificationchipseqatacseqdnaseseqrnaseqhiddenmarkovmodelbioconductorbootstrapgenomicsmatchingstatistics
15.1 match 27 stars 8.16 score 50 scripts 1 dependentsdistancedevelopment
mrds:Mark-Recapture Distance Sampling
Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.
Maintained by Laura Marshall. Last updated 2 months ago.
15.2 match 4 stars 8.05 score 78 scripts 7 dependentsgavinsimpson
gratia:Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Maintained by Gavin L. Simpson. Last updated 12 hours ago.
distributional-regressiongamgammgeneralized-additive-mixed-modelsgeneralized-additive-modelsggplot2glmlmmgcvpenalized-splinerandom-effectssmoothingsplines
9.1 match 217 stars 12.99 score 1.6k scripts 2 dependentsspatstat
spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family
Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Maintained by Adrian Baddeley. Last updated 1 months ago.
cluster-detectionconfidence-intervalshypothesis-testingk-functionroc-curvesscan-statisticssignificance-testingsimulation-envelopesspatial-analysisspatial-data-analysisspatial-sharpeningspatial-smoothingspatial-statistics
11.6 match 1 stars 10.17 score 67 scripts 148 dependentsjuergenknauer
bigleaf:Physical and Physiological Ecosystem Properties from Eddy Covariance Data
Calculation of physical (e.g. aerodynamic conductance, surface temperature), and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. Calculations assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables.
Maintained by Juergen Knauer. Last updated 8 months ago.
15.8 match 7.23 score 124 scripts 17 dependentsyanyachen
FinCovRegularization:Covariance Matrix Estimation and Regularization for Finance
Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation, three major types of factor models are included: macroeconomic factor model, fundamental factor model and statistical factor model. For covariance matrix regularization, four regularized estimators are included: banding, tapering, hard-thresholding and soft- thresholding. The tuning parameters of these regularized estimators are selected via cross-validation.
Maintained by YaChen Yan. Last updated 8 years ago.
25.3 match 7 stars 4.30 score 19 scripts 1 dependentsmitchelloharawild
distributional:Vectorised Probability Distributions
Vectorised distribution objects with tools for manipulating, visualising, and using probability distributions. Designed to allow model prediction outputs to return distributions rather than their parameters, allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.
Maintained by Mitchell OHara-Wild. Last updated 2 months ago.
probability-distributionstatisticsvctrs
8.0 match 101 stars 13.50 score 744 scripts 384 dependentsngreifer
WeightIt:Weighting for Covariate Balance in Observational Studies
Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any package 'WeightIt' uses, including those that may not be on CRAN.
Maintained by Noah Greifer. Last updated 5 days ago.
causal-inferenceinverse-probability-weightsobservational-studypropensity-scores
9.3 match 112 stars 11.58 score 508 scripts 3 dependentsbioc
swfdr:Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates
This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ.
Maintained by Simina M. Boca. Last updated 5 months ago.
multiplecomparisonstatisticalmethodsoftware
17.2 match 3 stars 6.25 score 37 scriptsdcgerard
tensr:Covariance Inference and Decompositions for Tensor Datasets
A collection of functions for Kronecker structured covariance estimation and testing under the array normal model. For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
Maintained by David Gerard. Last updated 2 years ago.
16.4 match 5 stars 6.53 score 56 scripts 4 dependentspsychmeta
psychmeta:Psychometric Meta-Analysis Toolkit
Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
Maintained by Jeffrey A. Dahlke. Last updated 9 months ago.
hacktoberfestmeta-analysispsychologypsychometricpsychometrics
12.9 match 57 stars 8.25 score 151 scriptstherneau
survival:Survival Analysis
Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.
Maintained by Terry M Therneau. Last updated 3 months ago.
5.1 match 400 stars 20.43 score 29k scripts 3.9k dependentsanestistouloumis
ShrinkCovMat:Shrinkage Covariance Matrix Estimators
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Maintained by Anestis Touloumis. Last updated 2 years ago.
covariance-matrixshrinkage-estimatorsopenblascppopenmp
20.7 match 8 stars 4.83 score 17 scriptskkholst
lava:Latent Variable Models
A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
Maintained by Klaus K. Holst. Last updated 2 months ago.
latent-variable-modelssimulationstatisticsstructural-equation-models
7.8 match 33 stars 12.85 score 610 scripts 476 dependentscecileproust-lima
lcmm:Extended Mixed Models Using Latent Classes and Latent Processes
Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).
Maintained by Cecile Proust-Lima. Last updated 1 months ago.
8.6 match 62 stars 11.41 score 249 scripts 7 dependentscran
tensorA:Advanced Tensor Arithmetic with Named Indices
Provides convenience functions for advanced linear algebra with tensors and computation with data sets of tensors on a higher level abstraction. It includes Einstein and Riemann summing conventions, dragging, co- and contravariate indices, parallel computations on sequences of tensors.
Maintained by K. Gerald van den Boogaart. Last updated 1 years ago.
16.9 match 5.83 score 399 dependentstopepo
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.
23.3 match 3 stars 4.11 score 86 scriptsemmanuelparadis
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 14 hours ago.
5.5 match 64 stars 17.22 score 13k scripts 599 dependentstagteam
riskRegression:Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks
Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.
Maintained by Thomas Alexander Gerds. Last updated 18 days ago.
7.1 match 46 stars 13.00 score 736 scripts 35 dependentsohdsi
Cyclops:Cyclic Coordinate Descent for Logistic, Poisson and Survival Analysis
This model fitting tool incorporates cyclic coordinate descent and majorization-minimization approaches to fit a variety of regression models found in large-scale observational healthcare data. Implementations focus on computational optimization and fine-scale parallelization to yield efficient inference in massive datasets. Please see: Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.
Maintained by Marc A. Suchard. Last updated 4 months ago.
10.2 match 39 stars 9.05 score 73 scripts 4 dependentspaul-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: Bรผrkner (2017) <doi:10.18637/jss.v080.i01>; Bรผrkner (2018) <doi:10.32614/RJ-2018-017>; Bรผrkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bรผrkner. Last updated 3 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
5.5 match 1.3k stars 16.61 score 13k scripts 34 dependentsnerler
JointAI:Joint Analysis and Imputation of Incomplete Data
Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.
Maintained by Nicole S. Erler. Last updated 12 months ago.
bayesiangeneralized-linear-modelsglmglmmimputationimputationsjagsjoint-analysislinear-mixed-modelslinear-regression-modelsmcmc-samplemcmc-samplingmissing-datamissing-valuessurvivalcpp
12.4 match 28 stars 7.30 score 59 scripts 1 dependentsroustant
kergp:Gaussian Process Laboratory
Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation.
Maintained by Olivier Roustant. Last updated 4 months ago.
48.9 match 1 stars 1.83 score 67 scriptsdeankoch
snapKrig:Fast Kriging and Geostatistics on Grids with Kronecker Covariance
Geostatistical modeling and kriging with gridded data using spatially separable covariance functions (Kronecker covariances). Kronecker products in these models provide shortcuts for solving large matrix problems in likelihood and conditional mean, making 'snapKrig' computationally efficient with large grids. The package supplies its own S3 grid object class, and a host of methods including plot, print, Ops, square bracket replace/assign, and more. Our computational methods are described in Koch, Lele, Lewis (2020) <doi:10.7939/r3-g6qb-bq70>.
Maintained by Dean Koch. Last updated 5 months ago.
17.6 match 5 stars 5.05 score 15 scriptsjenniniku
gllvm:Generalized Linear Latent Variable Models
Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).
Maintained by Jenni Niku. Last updated 13 hours ago.
8.3 match 52 stars 10.53 score 176 scripts 1 dependentskisungyou
SHT:Statistical Hypothesis Testing Toolbox
We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.
Maintained by Kisung You. Last updated 19 days ago.
17.0 match 6 stars 5.13 score 50 scripts 1 dependentsbioc
IHW:Independent Hypothesis Weighting
Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.
Maintained by Nikos Ignatiadis. Last updated 5 months ago.
immunooncologymultiplecomparisonrnaseq
12.0 match 7.25 score 264 scripts 2 dependentsjlaake
RMark:R Code for Mark Analysis
An interface to the software package MARK that constructs input files for MARK and extracts the output. MARK was developed by Gary White and is freely available at <http://www.phidot.org/software/mark/downloads/> but is not open source.
Maintained by Jeff Laake. Last updated 3 years ago.
17.6 match 4.90 score 366 scripts 4 dependentsbioc
zinbwave:Zero-Inflated Negative Binomial Model for RNA-Seq Data
Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.
Maintained by Davide Risso. Last updated 5 months ago.
immunooncologydimensionreductiongeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecell
8.0 match 43 stars 10.53 score 190 scripts 6 dependentskeefe-murphy
MoEClust:Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
Maintained by Keefe Murphy. Last updated 11 days ago.
gaussian-mixture-modelsmixture-of-expertsmodel-based-clustering
12.8 match 7 stars 6.51 score 44 scripts 1 dependentsgdurif
plsgenomics:PLS Analyses for Genomics
Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS.
Maintained by Ghislain Durif. Last updated 12 months ago.
15.0 match 5.55 score 140 scripts 2 dependentslem-usp
evolqg:Evolutionary Quantitative Genetics
Provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.
Maintained by Diogo Melo. Last updated 11 months ago.
13.3 match 10 stars 6.26 score 114 scriptsbioc
Maaslin2:"Multivariable Association Discovery in Population-scale Meta-omics Studies"
MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin.
Maintained by Lauren McIver. Last updated 5 months ago.
metagenomicssoftwaremicrobiomenormalizationbiobakerybioconductordifferential-abundance-analysisfalse-discovery-ratemultiple-covariatespublicrepeated-measurestools
7.5 match 133 stars 11.03 score 532 scripts 3 dependentsusepa
spmodel:Spatial Statistical Modeling and Prediction
Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
Maintained by Michael Dumelle. Last updated 4 days ago.
10.7 match 15 stars 7.66 score 112 scripts 3 dependentswrathematics
coop:Co-Operation: Fast Covariance, Correlation, and Cosine Similarity Operations
Fast implementations of the co-operations: covariance, correlation, and cosine similarity. The implementations are fast and memory-efficient and their use is resolved automatically based on the input data, handled by R's S3 methods. Full descriptions of the algorithms and benchmarks are available in the package vignettes.
Maintained by Drew Schmidt. Last updated 3 years ago.
9.2 match 35 stars 8.92 score 214 scripts 16 dependentsstatnet
latentnet:Latent Position and Cluster Models for Statistical Networks
Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.
Maintained by Pavel N. Krivitsky. Last updated 6 days ago.
9.8 match 19 stars 8.36 score 191 scripts 4 dependentsadibender
pammtools:Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis
The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated, competing risks and recurrent events data. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.
Maintained by Andreas Bender. Last updated 2 months ago.
additive-modelspammpammtoolspiece-wise-exponentialsurvival-analysis
9.2 match 48 stars 8.78 score 310 scripts 8 dependentssujit-sahu
bmstdr:Bayesian Modeling of Spatio-Temporal Data with R
Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.
Maintained by Sujit K. Sahu. Last updated 1 years ago.
bayesianmodellingspatio-temporal-datacpp
16.3 match 15 stars 4.95 score 12 scriptsopenpharma
DoseFinding:Planning and Analyzing Dose Finding Experiments
The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).
Maintained by Marius Thomas. Last updated 5 days ago.
7.8 match 8 stars 10.32 score 98 scripts 10 dependentsfriendly
heplots:Visualizing Hypothesis Tests in Multivariate Linear Models
Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.
Maintained by Michael Friendly. Last updated 9 days ago.
linear-hypothesesmatricesmultivariate-linear-modelsplotrepeated-measure-designsvisualizing-hypothesis-tests
6.9 match 9 stars 11.49 score 1.1k scripts 7 dependentsnicholasjclark
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
13.2 match 24 stars 6.03 score 30 scriptsgpfda
GPFDA:Gaussian Process for Functional Data Analysis
Functionalities for modelling functional data with multidimensional inputs, multivariate functional data, and non-separable and/or non-stationary covariance structure of function-valued processes. In addition, there are functionalities for functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure depends on functional covariates. The development version of the package can be found on <https://github.com/gpfda/GPFDA-dev>.
Maintained by Evandro Konzen. Last updated 2 years ago.
20.9 match 1 stars 3.81 score 36 scripts 1 dependentsbstewart
stm:Estimation of the Structural Topic Model
The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions. Methods developed in Roberts et. al. (2014) <doi:10.1111/ajps.12103> and Roberts et. al. (2016) <doi:10.1080/01621459.2016.1141684>. Vignette is Roberts et. al. (2019) <doi:10.18637/jss.v091.i02>.
Maintained by Brandon Stewart. Last updated 1 years ago.
6.2 match 404 stars 12.63 score 1.6k scripts 6 dependentsmariarizzo
energy:E-Statistics: Multivariate Inference via the Energy of Data
E-statistics (energy) tests and statistics for multivariate and univariate inference, including distance correlation, one-sample, two-sample, and multi-sample tests for comparing multivariate distributions, are implemented. Measuring and testing multivariate independence based on distance correlation, partial distance correlation, multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy distance, testing for multivariate normality, distance components (disco) for non-parametric analysis of structured data, and other energy statistics/methods are implemented.
Maintained by Maria Rizzo. Last updated 7 months ago.
distance-correlationenergymultivariate-analysisstatisticscpp
7.4 match 45 stars 10.60 score 634 scripts 45 dependentsmd-anderson-bioinformatics
NGCHM:Next Generation Clustered Heat Maps
Next-Generation Clustered Heat Maps (NG-CHMs) allow for dynamic exploration of heat map data in a web browser. 'NGCHM' allows users to create both stand-alone HTML files containing a Next-Generation Clustered Heat Map, and .ngchm files to view in the NG-CHM viewer. See Ryan MC, Stucky M, et al (2020) <doi:10.12688/f1000research.20590.2> for more details.
Maintained by Mary A Rohrdanz. Last updated 9 days ago.
14.2 match 9 stars 5.48 score 28 scriptsbmcclintock
momentuHMM:Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models
Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, discrete- or continuous-time HMMs, continuous- or discrete-space movement models, approximate Langevin diffusion models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
Maintained by Brett McClintock. Last updated 1 months ago.
9.1 match 43 stars 8.47 score 162 scriptscran
intccr:Semiparametric Competing Risks Regression under Interval Censoring
Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi{10.1002/sim.7350} and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi{10.1093/biostatistics/kxaa052}. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.
Maintained by Jun Park. Last updated 3 years ago.
29.7 match 1 stars 2.57 score 37 scriptslme4
lme4:Linear Mixed-Effects Models using 'Eigen' and S4
Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
Maintained by Ben Bolker. Last updated 3 days ago.
3.7 match 647 stars 20.69 score 35k scripts 1.5k dependentssfcheung
stdmod:Standardized Moderation Effect and Its Confidence Interval
Functions for computing a standardized moderation effect in moderated regression and forming its confidence interval by nonparametric bootstrapping as proposed in Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Also includes simple-to-use functions for computing conditional effects (unstandardized or standardized) and plotting moderation effects.
Maintained by Shu Fai Cheung. Last updated 6 months ago.
bootstrappingconfidence-intervaleffect-sizesmoderationregressionstandardizationstandardized-moderation
13.4 match 1 stars 5.62 score 46 scriptshojsgaard
pbkrtest:Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models
Computes p-values based on (a) Satterthwaite or Kenward-Rogers degree of freedom methods and (b) parametric bootstrap for mixed effects models as implemented in the 'lme4' package. Implements parametric bootstrap test for generalized linear mixed models as implemented in 'lme4' and generalized linear models. The package is documented in the paper by Halekoh and Hรธjsgaard, (2012, <doi:10.18637/jss.v059.i09>). Please see 'citation("pbkrtest")' for citation details.
Maintained by Sรธren Hรธjsgaard. Last updated 10 days ago.
5.2 match 5 stars 14.36 score 648 scripts 915 dependentsbioc
ramwas:Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms
A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) <doi:10.1093/bioinformatics/bty069>.
Maintained by Andrey A Shabalin. Last updated 5 months ago.
dnamethylationsequencingqualitycontrolcoveragepreprocessingnormalizationbatcheffectprincipalcomponentdifferentialmethylationvisualization
12.2 match 10 stars 6.08 score 85 scriptswenjie2wang
reda:Recurrent Event Data Analysis
Contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304> and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>, (4) gamma frailty model with spline rate function following Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.
Maintained by Wenjie Wang. Last updated 1 years ago.
mcfmean-cumulative-functionrecurrent-eventsurvival-analysiscpp
9.8 match 15 stars 7.52 score 55 scripts 3 dependentsdppalomar
sparseEigen:Computation of Sparse Eigenvectors of a Matrix
Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar, "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing, IEEE Trans. on Signal Processing, vol. 64, no. 23, pp. 6211-6226, Dec. 2016. <doi:10.1109/TSP.2016.2605073>.
Maintained by Daniel P. Palomar. Last updated 6 years ago.
covariance-matrixeigenvectorspcasparse
13.6 match 12 stars 5.42 score 22 scriptsweiliang
powerSurvEpi:Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies
Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study.
Maintained by Weiliang Qiu. Last updated 4 years ago.
19.8 match 3.72 score 77 scripts 2 dependentsrfastofficial
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.
9.1 match 38 stars 8.09 score 75 scripts 26 dependentshardin47
biwt:Functions to Compute the Biweight Mean Vector and Covariance and Correlation Matrices
The base functions compute multivariate location, scale, and correlation estimates based on Tukey's biweight M-estimator. Using the base function, the computations can be applied to a large number of observations to create either a matrix of biweight distances or biweight correlations.
Maintained by Johanna Hardin. Last updated 6 months ago.
13.1 match 5.58 score 16 scripts 2 dependentsfate-ewi
bayesdfa:Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.
Maintained by Eric J. Ward. Last updated 5 months ago.
8.8 match 28 stars 8.28 score 101 scriptsprzechoj
gips:Gaussian Model Invariant by Permutation Symmetry
Find the permutation symmetry group such that the covariance matrix of the given data is approximately invariant under it. Discovering such a permutation decreases the number of observations needed to fit a Gaussian model, which is of great use when it is smaller than the number of variables. Even if that is not the case, the covariance matrix found with 'gips' approximates the actual covariance with less statistical error. The methods implemented in this package are described in Graczyk et al. (2022) <doi:10.1214/22-AOS2174>.
Maintained by Adam Przemysลaw Chojecki. Last updated 8 months ago.
covariance-estimationmachine-learningnormal-distribution
11.3 match 6 stars 6.40 score 31 scriptsvalentint
rrcov:Scalable Robust Estimators with High Breakdown Point
Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point: principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>), linear and quadratic discriminant analysis (Todorov and Pires (2007)), multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>), outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>). See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>, Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
Maintained by Valentin Todorov. Last updated 7 months ago.
6.8 match 2 stars 10.57 score 484 scripts 96 dependentsstocnet
RSiena:Siena - Simulation Investigation for Empirical Network Analysis
The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), <doi:10.1146/annurev-statistics-060116-054035>.
Maintained by Tom A.B. Snijders. Last updated 1 months ago.
longitudinal-datarsienasocial-network-analysisstatistical-network-analysisstatisticscpp
7.1 match 107 stars 9.93 score 346 scripts 1 dependentsrefunders
refund:Regression with Functional Data
Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.
Maintained by Julia Wrobel. Last updated 6 months ago.
6.9 match 41 stars 10.25 score 472 scripts 16 dependentsrauschenberger
palasso:Sparse Regression with Paired Covariates
Implements sparse regression with paired covariates (<doi:10.1007/s11634-019-00375-6>). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install 'ashr' (<https://github.com/stephens999/ashr>) and 'CorShrink' (<https://github.com/kkdey/CorShrink>) from GitHub (see README).
Maintained by Armin Rauschenberger. Last updated 6 months ago.
11.6 match 1 stars 6.01 score 19 scripts 6 dependentsmbannick
RobinCar:Robust Inference for Covariate Adjustment in Randomized Clinical Trials
Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized clinical trials. Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>. Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>. Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>. Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <doi:10.48550/arXiv.2306.10213>.
Maintained by Marlena Bannick. Last updated 6 days ago.
15.5 match 6 stars 4.42 score 11 scriptsrominsal
pspatreg:Spatial and Spatio-Temporal Semiparametric Regression Models with Spatial Lags
Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>; Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.
Maintained by Roman Minguez. Last updated 3 years ago.
10.7 match 12 stars 6.44 score 77 scriptsroustant
DiceKriging:Kriging Methods for Computer Experiments
Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.
Maintained by Olivier Roustant. Last updated 4 years ago.
9.8 match 4 stars 6.99 score 526 scripts 37 dependentsveronica0206
nlpsem:Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework
Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) <arXiv:2302.03237v2>.
Maintained by Jin Liu. Last updated 4 months ago.
9.8 match 145 stars 6.91 score 16 scriptspecanproject
PEcAn.DB: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 David LeBauer. Last updated 2 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplants
5.7 match 216 stars 11.88 score 127 scripts 27 dependentsccy-dev
LongDat:A Tool for 'Covariate'-Sensitive Longitudinal Analysis on 'omics' Data
This tool takes longitudinal dataset as input and analyzes if there is significant change of the features over time (a proxy for treatments), while detects and controls for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input, including count, proportion, binary, ordinal and continuous data. The output table contains p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.
Maintained by Chia-Yu Chen. Last updated 4 months ago.
14.7 match 4 stars 4.60 score 4 scriptsdovinij
GxEprs:Genotype-by-Environment Interaction in Polygenic Score Models
A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).
Maintained by Dovini Jayasinghe. Last updated 10 months ago.
20.4 match 2 stars 3.30 scoregregorkastner
factorstochvol:Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
Maintained by Gregor Kastner. Last updated 1 years ago.
14.1 match 7 stars 4.73 score 17 scripts 1 dependentskbroman
qtl:Tools for Analyzing QTL Experiments
Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits. Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Maintained by Karl W Broman. Last updated 7 months ago.
5.2 match 80 stars 12.79 score 2.4k scripts 29 dependentsr-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 27 days ago.
dynamicsdmgoogle-earth-enginegoogledrivesdmspatiotemporalspatiotemporal-data-analysisspatiotemporal-forecastingspecies-distribution-modellingspecies-distributions
10.7 match 6 stars 6.16 score 20 scriptsinlabru-org
inlabru:Bayesian Latent Gaussian Modelling using INLA and Extensions
Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.
Maintained by Finn Lindgren. Last updated 4 days ago.
5.2 match 96 stars 12.62 score 832 scripts 6 dependentsjarrodhadfield
MCMCglmm:MCMC Generalised Linear Mixed Models
Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).
Maintained by Jarrod Hadfield. Last updated 3 months ago.
7.3 match 2 stars 8.83 score 1.2k scripts 13 dependentslindanab
mecor:Measurement Error Correction in Linear Models with a Continuous Outcome
Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).
Maintained by Linda Nab. Last updated 3 years ago.
linear-modelsmeasurement-errorstatistics
12.8 match 6 stars 5.07 score 13 scriptssimulatr
simrel:Simulation of Multivariate Linear Model Data
Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sรฆbรธ, S., Almรธy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almรธy, T., Sรฆbรธ, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.
Maintained by Raju Rimal. Last updated 2 years ago.
bivariate-simulationmultivariate-simulationrelevant-predictor-componentssimulated-datasimulationunivariate-simulation
13.6 match 3 stars 4.78 score 40 scriptsdavezes
widals:Weighting by Inverse Distance with Adaptive Least Squares
Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data.
Maintained by Dave Zes. Last updated 5 years ago.
22.2 match 2.89 score 39 scriptsmicbtz
equateIRT:IRT Equating Methods
Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, Battauz (2015) <doi:10.18637/jss.v068.i07>). Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) <doi:10.1007/s10260-018-00442-w>). The package includes tests to assess the stability of the equating transformations (Battauz(2022) <doi:10.1111/stan.12277>).
Maintained by Michela Battauz. Last updated 5 months ago.
13.5 match 4.70 score 35 scripts 2 dependentschjackson
flexsurv:Flexible Parametric Survival and Multi-State Models
Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. There are also tools for fitting and predicting from fully parametric multi-state models, based on either cause-specific hazards or mixture models.
Maintained by Christopher Jackson. Last updated 2 months ago.
4.8 match 57 stars 13.31 score 632 scripts 43 dependentsluca-scr
mclust:Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Maintained by Luca Scrucca. Last updated 11 months ago.
5.2 match 21 stars 12.23 score 6.6k scripts 587 dependentsdata-edu
tidyLPA:Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software
Easily carry out latent profile analysis ("LPA"), determine the correct number of classes based on best practices, and tabulate and plot the results. Provides functionality to estimate commonly-specified models with free means, variances, and covariances for each profile. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Models can be estimated using the free open source 'R' packages 'Mclust' and 'OpenMx', or using the commercial program 'MPlus', via the 'MplusAutomation' package.
Maintained by Joshua M Rosenberg. Last updated 1 years ago.
7.3 match 57 stars 8.71 score 121 scriptsrpolars
polars:Lightning-Fast 'DataFrame' Library
Lightning-fast 'DataFrame' library written in 'Rust'. Convert R data to 'Polars' data and vice versa. Perform fast, lazy, larger-than-memory and optimized data queries. 'Polars' is interoperable with the package 'arrow', as both are based on the 'Apache Arrow' Columnar Format.
Maintained by Soren Welling. Last updated 3 days ago.
5.3 match 499 stars 12.01 score 1.0k scripts 2 dependentssmartdata-analysis-and-statistics
precmed:Precision Medicine
A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.
Maintained by Thomas Debray. Last updated 5 months ago.
15.0 match 4 stars 4.20 score 4 scriptsjclavel
mvMORPH:Multivariate Comparative Tools for Fitting Evolutionary Models to Morphometric Data
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.
Maintained by Julien Clavel. Last updated 1 months ago.
6.6 match 17 stars 9.46 score 189 scripts 3 dependentsdistancedevelopment
Distance:Distance Sampling Detection Function and Abundance Estimation
A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on methods and <https://examples.distancesampling.org/> for example analyses.
Maintained by Laura Marshall. Last updated 11 days ago.
7.0 match 11 stars 8.89 score 358 scripts 3 dependentsjtimonen
lgpr:Longitudinal Gaussian Process Regression
Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.
Maintained by Juho Timonen. Last updated 6 months ago.
bayesian-inferencegaussian-processeslongitudinal-datastancpp
10.4 match 25 stars 5.94 score 69 scriptsstavakol
covsep:Tests for Determining if the Covariance Structure of 2-Dimensional Data is Separable
Functions for testing if the covariance structure of 2-dimensional data (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2. A complete descriptions of the implemented tests can be found in the paper Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017), no. 4, 1431--1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>.
Maintained by Shahin Tavakoli. Last updated 7 years ago.
28.8 match 2.11 score 13 scriptsyexiaoqingruc
carat:Covariate-Adaptive Randomization for Clinical Trials
Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) <doi: 10.18637/jss.v107.i02> for details.
Maintained by Xiaoqing Ye. Last updated 2 years ago.
30.0 match 2.02 score 35 scriptsardiad
RiskPortfolios:Computation of Risk-Based Portfolios
Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Maintained by David Ardia. Last updated 4 years ago.
covarianceoptimizationportfolioportfolio-optimizationrisk
12.0 match 51 stars 5.05 score 44 scriptskeyatm
keyATM:Keyword Assisted Topic Models
Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2024) <doi:10.1111/ajps.12779>.
Maintained by Shusei Eshima. Last updated 11 months ago.
latent-dirichlet-allocationnatural-language-processingpolitical-sciencercpprcppeigensocial-sciencetopic-modelscpp
9.6 match 106 stars 6.30 score 63 scriptsangelacar
TwoTimeScales:Analysis of Event Data with Two Time Scales
Analyse time to event data with two time scales by estimating a smooth hazard that varies over two time scales. If covariates are available, estimate a proportional hazards model with such a two-dimensional baseline hazard. Functions are provided to prepare the raw data for estimation, to estimate and to plot the two-dimensional smooth hazard. Extension to a competing risks model are implemented. For details about the method please refer to Carollo et al. (2024) <doi:10.1002/sim.10297>.
Maintained by Angela Carollo. Last updated 1 months ago.
9.6 match 9 stars 6.26 score 5 scriptsbxc147
Epi:Statistical Analysis in Epidemiology
Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.
Maintained by Bendix Carstensen. Last updated 2 months ago.
6.2 match 4 stars 9.65 score 708 scripts 11 dependentsohdsi
PatientLevelPrediction:Develop Clinical Prediction Models Using the Common Data Model
A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.
Maintained by Egill Fridgeirsson. Last updated 9 days ago.
5.5 match 190 stars 10.85 score 297 scriptsbioc
COCOA:Coordinate Covariation Analysis
COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.
Maintained by John Lawson. Last updated 5 months ago.
epigeneticsdnamethylationatacseqdnaseseqmethylseqmethylationarrayprincipalcomponentgenomicvariationgeneregulationgenomeannotationsystemsbiologyfunctionalgenomicschipseqsequencingimmunooncologydna-methylationpca
8.3 match 10 stars 7.02 score 21 scriptsbioc
DEGreport:Report of DEG analysis
Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.
Maintained by Lorena Pantano. Last updated 5 months ago.
differentialexpressionvisualizationrnaseqreportwritinggeneexpressionimmunooncologybioconductordifferential-expressionqcreportrna-seqsmallrna
6.2 match 24 stars 9.42 score 354 scripts 1 dependentsbozenne
LMMstar:Repeated Measurement Models for Discrete Times
Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values.
Maintained by Brice Ozenne. Last updated 5 months ago.
9.2 match 4 stars 6.28 score 141 scriptschkiefer
lavacreg:Latent Variable Count Regression Models
Estimation of a multi-group count regression models (i.e., Poisson, negative binomial) with latent covariates. This packages provides two extensions compared to ordinary count regression models based on a generalized linear model: First, measurement models for the predictors can be specified allowing to account for measurement error. Second, the count regression can be simultaneously estimated in multiple groups with stochastic group weights. The marginal maximum likelihood estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.
Maintained by Christoph Kiefer. Last updated 23 days ago.
count-regressionlatent-covariatesstructural-equation-modelingopenblascppopenmp
12.1 match 3 stars 4.78 score 5 scriptsvalentint
pcaPP:Robust PCA by Projection Pursuit
Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.
Maintained by Valentin Todorov. Last updated 7 months ago.
5.5 match 1 stars 10.56 score 186 scripts 351 dependentsr-cas
Ryacas:R Interface to the 'Yacas' Computer Algebra System
Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
Maintained by Mikkel Meyer Andersen. Last updated 2 years ago.
5.7 match 40 stars 10.15 score 167 scripts 14 dependentsvitomuggeo
segmented:Regression Models with Break-Points / Change-Points Estimation (with Possibly Random Effects)
Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>). Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, <doi:10.1177/1471082X13504721>). Estimation of piecewise-constant relationships and changepoints (mean-shift models) is discussed in Fasola et al. (2018, <doi:10.1007/s00180-017-0740-4>).
Maintained by Vito M. R. Muggeo. Last updated 16 days ago.
5.7 match 9 stars 10.03 score 1.2k scripts 203 dependentsadw96
breakaway:Species Richness Estimation and Modeling
Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. 'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.
Maintained by Amy D Willis. Last updated 1 years ago.
7.0 match 68 stars 8.18 score 211 scriptssebkrantz
collapse:Advanced and Fast Data Transformation
A C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It is well integrated with base R classes, 'dplyr'/'tibble', 'data.table', 'sf', 'units', 'plm' (panel-series and data frames), and 'xts'/'zoo'.
Maintained by Sebastian Krantz. Last updated 6 days ago.
data-aggregationdata-analysisdata-manipulationdata-processingdata-sciencedata-transformationeconometricshigh-performancepanel-datascientific-computingstatisticstime-seriesweightedweightscppopenmp
3.4 match 672 stars 16.63 score 708 scripts 97 dependentsdanigiro
FoReco:Forecast Reconciliation
Classical (bottom-up and top-down), optimal combination and heuristic point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and probabilistic (Girolimetto et al. 2023 <doi:10.1016/j.ijforecast.2023.10.003>) forecast reconciliation procedures for linearly constrained time series (e.g., hierarchical or grouped time series) in cross-sectional, temporal, or cross-temporal frameworks.
Maintained by Daniele Girolimetto. Last updated 2 months ago.
forecastingreconciliationtime-series
9.2 match 33 stars 6.19 score 104 scriptsmsalibian
RobStatTM:Robust Statistics: Theory and Methods
Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>. This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.
Maintained by Matias Salibian-Barrera. Last updated 3 days ago.
robustrobust-estimationrobust-regressionrobust-statisticsrobustnessstatisticsfortranopenblas
5.5 match 17 stars 10.23 score 84 scripts 8 dependentskingaa
pomp:Statistical Inference for Partially Observed Markov Processes
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Maintained by Aaron A. King. Last updated 1 months ago.
abcb-splinedifferential-equationsdynamical-systemsiterated-filteringlikelihoodlikelihood-freemarkov-chain-monte-carlomarkov-modelmathematical-modellingmeasurement-errorparticle-filtersequential-monte-carlosimulation-based-inferencesobol-sequencestate-spacestatistical-inferencestochastic-processestime-seriesopenblas
4.8 match 115 stars 11.81 score 1.3k scripts 4 dependentsdkaschek
dMod:Dynamic Modeling and Parameter Estimation in ODE Models
The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives.
Maintained by Daniel Kaschek. Last updated 10 days ago.
6.8 match 20 stars 8.35 score 251 scriptszhanxw
seqminer:Efficiently Read Sequence Data (VCF Format, BCF Format, METAL Format and BGEN Format) into R
Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.
Maintained by Xiaowei Zhan. Last updated 6 months ago.
annotationbcfbgenmeta-analysisnext-generation-sequencingplinksequencingtabixvcfworkflowzlibbzip2libzstdsqlite3cpp
6.8 match 30 stars 8.29 score 111 scripts 6 dependentstidymodels
recipes:Preprocessing and Feature Engineering Steps for Modeling
A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.
Maintained by Max Kuhn. Last updated 6 days ago.
3.0 match 584 stars 18.71 score 7.2k scripts 380 dependentsjfrench
SpatialTools:Tools for Spatial Data Analysis
Tools for spatial data analysis. Emphasis on kriging. Provides functions for prediction and simulation. Intended to be relatively straightforward, fast, and flexible.
Maintained by Joshua French. Last updated 2 years ago.
12.8 match 2 stars 4.38 score 100 scripts 4 dependentszhenkewu
baker:"Nested Partially Latent Class Models"
Provides functions to specify, fit and visualize nested partially-latent class models ( Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>; Wu and Chen (2021) <doi:10.1002/sim.8804>) for inference of population disease etiology and individual diagnosis. In the motivating Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities of interest sum to one hundred percent, the PERCH scientists frequently refer to them as population etiology pie and individual etiology pie, hence the name of the package.
Maintained by Zhenke Wu. Last updated 11 months ago.
bayesiancase-controllatent-class-analysisjagscpp
9.3 match 8 stars 6.00 score 21 scriptsrstudio
tfprobability:Interface to 'TensorFlow Probability'
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
Maintained by Tomasz Kalinowski. Last updated 3 years ago.
6.4 match 54 stars 8.63 score 221 scripts 3 dependentsr-forge
coin:Conditional Inference Procedures in a Permutation Test Framework
Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in <doi:10.18637/jss.v028.i08>.
Maintained by Torsten Hothorn. Last updated 9 months ago.
4.8 match 11.68 score 1.6k scripts 74 dependentslucaskook
comets:Covariance Measure Tests for Conditional Independence
Covariance measure tests for conditional independence testing against conditional covariance and nonlinear conditional mean alternatives. The package implements versions of the generalised covariance measure test (Shah and Peters, 2020, <doi:10.1214/19-aos1857>) and projected covariance measure test (Lundborg et al., 2023, <doi:10.1214/24-AOS2447>). The tram-GCM test, for censored responses, is implemented including the Cox model and survival forests (Kook et al., 2024, <doi:10.1080/01621459.2024.2395588>). Application examples to variable significance testing and modality selection can be found in Kook and Lundborg (2024, <doi:10.1093/bib/bbae475>).
Maintained by Lucas Kook. Last updated 1 months ago.
11.2 match 9 stars 4.95 score 2 scriptsneonscience
neonUtilities:Utilities for Working with NEON Data
NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org> or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.
Maintained by Claire Lunch. Last updated 1 months ago.
5.2 match 57 stars 10.66 score 944 scripts 15 dependentsjeksterslab
betaSandwich:Robust Confidence Intervals for Standardized Regression Coefficients
Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.
Maintained by Ivan Jacob Agaloos Pesigan. Last updated 2 months ago.
confidence-intervalsheteroskedasticity-consistent-standard-errorsstandardized-regression-coefficients
13.3 match 4.16 score 16 scriptsjtextor
dagitty:Graphical Analysis of Structural Causal Models
A port of the web-based software 'DAGitty', available at <https://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.
Maintained by Johannes Textor. Last updated 3 months ago.
4.3 match 302 stars 12.83 score 1.7k scripts 11 dependentskeefe-murphy
MEDseq:Mixtures of Exponential-Distance Models with Covariates
Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
Maintained by Keefe Murphy. Last updated 7 days ago.
distance-based-clusteringmixture-of-expertsmodel-based-clusteringsequence-analysis
9.9 match 5 stars 5.49 score 25 scriptsr-forge
distrMod:Object Oriented Implementation of Probability Models
Implements S4 classes for probability models based on packages 'distr' and 'distrEx'.
Maintained by Peter Ruckdeschel. Last updated 2 months ago.
8.1 match 6.71 score 139 scripts 6 dependentscran
VCA:Variance Component Analysis
ANOVA and REML estimation of linear mixed models is implemented, once following Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package. The primary objective of this package is to perform a variance component analysis (VCA) according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement Procedures" (2014). There are plotting methods for visualization of an experimental design, plotting random effects and residuals. For ANOVA type estimation two methods for computing ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of variance components can be derived, which is used in estimating confidence intervals. Linear hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific values of covariables and with custom weighting schemes for factor variables. See ?VCA for a more comprehensive description of the features.
Maintained by Andre Schuetzenmeister. Last updated 1 years ago.
11.9 match 2 stars 4.51 score 5 dependentszhangh12
multipleOutcomes:Asymptotic Covariance Matrix of Regression Models for Multiple Outcomes
Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including PATED (Prognostic Variables Assisted Treatment Effect Detection), multiple comparison adjustment, are illustrated.
Maintained by Han Zhang. Last updated 4 months ago.
14.9 match 3.60 score 1 scriptsjohn-harrold
ubiquity:PKPD, PBPK, and Systems Pharmacology Modeling Tools
Complete work flow for the analysis of pharmacokinetic pharmacodynamic (PKPD), physiologically-based pharmacokinetic (PBPK) and systems pharmacology models including: creation of ordinary differential equation-based models, pooled parameter estimation, individual/population based simulations, rule-based simulations for clinical trial design and modeling assays, deployment with a customizable 'Shiny' app, and non-compartmental analysis. System-specific analysis templates can be generated and each element includes integrated reporting with 'PowerPoint' and 'Word'.
Maintained by John Harrold. Last updated 17 days ago.
7.5 match 13 stars 7.14 score 33 scriptsohdsi
Characterization:Implement Descriptive Studies Using the Common Data Model
An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.
Maintained by Jenna Reps. Last updated 17 days ago.
8.7 match 3 stars 6.13 scorer-spatial
gstat:Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation
Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions; supports sf and stars.
Maintained by Edzer Pebesma. Last updated 10 days ago.
3.6 match 197 stars 14.78 score 4.8k scripts 57 dependentsspatstat
spatstat.geom:Geometrical Functionality of the 'spatstat' Family
Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)
Maintained by Adrian Baddeley. Last updated 2 days ago.
classes-and-objectsdistance-calculationgeometrygeometry-processingimagesmensurationplottingpoint-patternsspatial-dataspatial-data-analysis
4.4 match 7 stars 12.11 score 241 scripts 227 dependentsjmsigner
amt:Animal Movement Tools
Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.
Maintained by Johannes Signer. Last updated 4 months ago.
5.0 match 41 stars 10.54 score 418 scriptspharmaverse
admiral:ADaM in R Asset Library
A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>).
Maintained by Ben Straub. Last updated 4 days ago.
cdiscclinical-trialsopen-source
3.8 match 236 stars 13.89 score 486 scripts 4 dependentszarquon42b
Morpho:Calculations and Visualisations Related to Geometric Morphometrics
A toolset for Geometric Morphometrics and mesh processing. This includes (among other stuff) mesh deformations based on reference points, permutation tests, detection of outliers, processing of sliding semi-landmarks and semi-automated surface landmark placement.
Maintained by Stefan Schlager. Last updated 5 months ago.
5.2 match 51 stars 10.00 score 218 scripts 13 dependentsjbryer
multilevelPSA:Multilevel Propensity Score Analysis
Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.
Maintained by Jason Bryer. Last updated 5 years ago.
10.8 match 16 stars 4.81 score 80 scriptsfranzmohr
bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models
Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).
Maintained by Franz X. Mohr. Last updated 1 years ago.
bayesianbayesian-inferencebayesian-varbvarbvecmgibbs-samplingmcmcvector-autoregressionvector-error-correction-modelopenblascpp
7.6 match 31 stars 6.80 score 34 scripts 1 dependentsyuimaproject
yuima:The YUIMA Project Package for SDEs
Simulation and Inference for SDEs and Other Stochastic Processes.
Maintained by Stefano M. Iacus. Last updated 3 days ago.
7.1 match 9 stars 7.26 score 92 scripts 2 dependentsnsaph-software
CausalGPS:Matching on Generalized Propensity Scores with Continuous Exposures
Provides a framework for estimating causal effects of a continuous exposure using observational data, and implementing matching and weighting on the generalized propensity score. Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29.
Maintained by Naeem Khoshnevis. Last updated 9 months ago.
6.7 match 24 stars 7.67 score 39 scriptssyneoshealth
puzzle:Assembling Data Sets for Non-Linear Mixed Effects Modeling
To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.
Maintained by Mario Gonzalez Sales. Last updated 5 years ago.
14.0 match 3 stars 3.65 score 9 scriptsinsightsengineering
rbmi:Reference Based Multiple Imputation
Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular, this package supports deterministic conditional mean imputation and jackknifing as described in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.
Maintained by Isaac Gravestock. Last updated 24 days ago.
5.8 match 18 stars 8.78 score 33 scripts 1 dependentsjeksterslab
cTMed:Continuous Time Mediation
Calculates standard errors and confidence intervals for effects in continuous-time mediation models. This package extends the work of Deboeck and Preacher (2015) <doi:10.1080/10705511.2014.973960> and Ryan and Hamaker (2021) <doi:10.1007/s11336-021-09767-0> by providing methods to generate standard errors and confidence intervals for the total, direct, and indirect effects in these models.
Maintained by Ivan Jacob Agaloos Pesigan. Last updated 20 days ago.
centralitycontinuous-timedelta-methodmediationmonte-carlo-methodnetworkopenblascppopenmp
11.2 match 4.56 score 24 scripts