Showing 200 of total 951 results (show query)
bbolker
margins:Marginal Effects for Model Objects
An R port of the margins command from 'Stata', which can be used to calculate marginal (or partial) effects from model objects.
Maintained by Ben Bolker. Last updated 8 months ago.
83.7 match 2 stars 9.85 score 956 scripts 1 dependentsstrengejacke
ggeffects:Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
Compute marginal effects and adjusted predictions from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Effects and predictions can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.
Maintained by Daniel Lüdecke. Last updated 5 days ago.
estimated-marginal-meanshacktoberfestmarginal-effectsprediction
22.8 match 588 stars 15.55 score 3.6k scripts 7 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.
16.4 match 377 stars 19.19 score 13k scripts 187 dependentseasystats
modelbased:Estimation of Model-Based Predictions, Contrasts and Means
Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, see 'insight::supported_models()'.
Maintained by Dominique Makowski. Last updated 2 days ago.
contrast-analysiscontrastseasystatsestimateggplot2hacktoberfestmarginalmarginal-effectsmeanspredict
25.5 match 241 stars 12.35 score 315 scripts 4 dependentsmlverse
torch:Tensors and Neural Networks with 'GPU' Acceleration
Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
Maintained by Daniel Falbel. Last updated 6 days ago.
15.6 match 520 stars 16.52 score 1.4k scripts 38 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 2 days ago.
multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp
18.2 match 14 stars 13.47 score 236 scripts 42 dependentskornl
mutoss:Unified Multiple Testing Procedures
Designed to ease the application and comparison of multiple hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are standardized and usable by the accompanying 'mutossGUI'.
Maintained by Kornelius Rohmeyer. Last updated 12 months ago.
27.7 match 4 stars 8.44 score 24 scripts 16 dependentsanniejw6
modmarg:Calculating Marginal Effects and Levels with Errors
Calculate predicted levels and marginal effects, using the delta method to calculate standard errors. This is an R-based version of the 'margins' command from Stata.
Maintained by Annie Wang. Last updated 4 years ago.
deltamarginmarginal-effectsstata
37.8 match 17 stars 5.89 score 23 scriptsdaattali
ggExtra:Add Marginal Histograms to 'ggplot2', and More 'ggplot2' Enhancements
Collection of functions and layers to enhance 'ggplot2'. The flagship function is 'ggMarginal()', which can be used to add marginal histograms/boxplots/density plots to 'ggplot2' scatterplots.
Maintained by Dean Attali. Last updated 9 months ago.
ggplot2ggplot2-enhancementsmarginal-plots
16.1 match 387 stars 13.45 score 3.3k scripts 28 dependentsquentingronau
bridgesampling:Bridge Sampling for Marginal Likelihoods and Bayes Factors
Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, <https://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>). Gronau, Singmann, & Wagenmakers (2020) <doi:10.18637/jss.v092.i10>.
Maintained by Quentin F. Gronau. Last updated 2 years ago.
17.4 match 32 stars 12.12 score 314 scripts 53 dependentstidyverse
ggplot2:Create Elegant Data Visualisations Using the Grammar of Graphics
A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
Maintained by Thomas Lin Pedersen. Last updated 9 days ago.
data-visualisationvisualisation
8.3 match 6.6k stars 25.10 score 645k scripts 7.5k dependentsopenpharma
brms.mmrm:Bayesian MMRMs using 'brms'
The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a powerful and versatile package for fitting Bayesian regression models. The 'brms.mmrm' R package leverages 'brms' to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008) <doi:10.1177/009286150804200402>.
Maintained by William Michael Landau. Last updated 6 months ago.
brmslife-sciencesmc-stanmmrmstanstatistics
23.2 match 21 stars 8.80 score 13 scriptslarmarange
broom.helpers:Helpers for Model Coefficients Tibbles
Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.
Maintained by Joseph Larmarange. Last updated 10 days ago.
16.4 match 22 stars 11.45 score 165 scripts 2 dependentsstan-dev
rstanarm:Bayesian Applied Regression Modeling via Stan
Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.
Maintained by Ben Goodrich. Last updated 9 months ago.
bayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticsmultilevel-modelsrstanrstanarmstanstatistical-modelingcpp
11.1 match 393 stars 15.68 score 5.0k scripts 13 dependentshehta
RESIDE:Rapid Easy Synthesis to Inform Data Extraction
Developed to assist researchers with planning analysis, prior to obtaining data from Trusted Research Environments (TREs) also known as safe havens. With functionality to export and import marginal distributions as well as synthesise data, both with and without correlations from these marginal distributions. Using a multivariate cumulative distribution (COPULA). Additionally the International Stroke Trial (IST) is included as an example dataset under ODC-By licence Sandercock et al. (2011) <doi:10.7488/ds/104>, Sandercock et al. (2011) <doi:10.1186/1745-6215-12-101>.
Maintained by Ryan Field. Last updated 10 days ago.
31.2 match 5.44 score 5 scriptsvincentarelbundock
marginaleffects:Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests
Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.
Maintained by Vincent Arel-Bundock. Last updated 5 hours ago.
10.7 match 505 stars 14.51 score 1.8k scripts 9 dependentsjwiley
brmsmargins:Bayesian Marginal Effects for 'brms' Models
Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).
Maintained by Joshua F. Wiley. Last updated 2 months ago.
24.1 match 20 stars 6.22 score 42 scriptstysonstanley
MarginalMediation:Marginal Mediation
Provides the ability to perform "Marginal Mediation"--mediation wherein the indirect and direct effects are in terms of the average marginal effects (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package. This framework allows the use of categorical mediators and outcomes with little change in interpretation from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details on the method.
Maintained by Tyson S Barrett. Last updated 3 years ago.
average-marginal-effectsmarginsmediationmediation-analysismediatorpartial-effectsrstudio
34.4 match 3 stars 4.29 score 13 scriptsajmcneil
tscopula:Time Series Copula Models
Functions for the analysis of time series using copula models. The package is based on methodology described in the following references. McNeil, A.J. (2021) <doi:10.3390/risks9010014>, Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>, Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.
Maintained by Alexander McNeil. Last updated 24 days ago.
24.8 match 2 stars 5.53 score 12 scriptsdrizopoulos
ltm:Latent Trait Models under IRT
Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
Maintained by Dimitris Rizopoulos. Last updated 3 years ago.
14.0 match 30 stars 9.59 score 1.0k scripts 27 dependentsstefanwilhelm
tmvtnorm:Truncated Multivariate Normal and Student t Distribution
Random number generation for the truncated multivariate normal and Student t distribution. Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
Maintained by Stefan Wilhelm. Last updated 1 years ago.
14.5 match 1 stars 8.84 score 338 scripts 59 dependentskisungyou
Rdimtools:Dimension Reduction and Estimation Methods
We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.
Maintained by Kisung You. Last updated 2 years ago.
dimension-estimationdimension-reductionmanifold-learningsubspace-learningopenblascppopenmp
15.3 match 52 stars 8.37 score 186 scripts 8 dependentsr-forge
copula:Multivariate Dependence with Copulas
Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.
Maintained by Martin Maechler. Last updated 11 days ago.
10.3 match 11.83 score 1.2k scripts 86 dependentseasystats
insight:Easy Access to Model Information for Various Model Objects
A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.
Maintained by Daniel Lüdecke. Last updated 5 days ago.
easystatshacktoberfestinsightmodelsnamespredictorsrandom
6.9 match 412 stars 17.24 score 568 scripts 210 dependentswalkerke
tidycensus:Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames
An integrated R interface to several United States Census Bureau APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis.
Maintained by Kyle Walker. Last updated 2 months ago.
7.2 match 647 stars 14.27 score 7.5k scripts 10 dependentsrte-antares-rpackage
antaresProcessing:'Antares' Results Processing
Process results generated by 'Antares', a powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors.
Maintained by Tatiana Vargas. Last updated 3 months ago.
infrastructuredataimportadequacyantaresbilandatatableenergylinear-algebramarginsmonte-carlo-simulationoptimizationprevisionnelrtesimulationsurplustyndp
14.3 match 8 stars 6.70 score 35 scripts 1 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.
13.2 match 5 stars 7.17 score 388 scripts 23 dependentsaphalo
ggpp:Grammar Extensions to 'ggplot2'
Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Geometries: geom_table(), geom_plot() and geom_grob() add insets to plots using native data coordinates, while geom_table_npc(), geom_plot_npc() and geom_grob_npc() do the same using "npc" coordinates through new aesthetics "npcx" and "npcy". Statistics: select observations based on 2D density. Positions: radial nudging away from a center point and nudging away from a line or curve; combined stacking and nudging; combined dodging and nudging.
Maintained by Pedro J. Aphalo. Last updated 21 days ago.
data-labelsdatavizggplot2-enhancementsggplot2-geomsggplot2-insetsggplot2-positions
7.5 match 130 stars 12.49 score 582 scripts 24 dependentsdalenbe2
bayesMeanScale:Bayesian Post-Estimation on the Mean Scale
Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.
Maintained by David M. Dalenberg. Last updated 2 months ago.
18.8 match 4.85 score 1 scriptspaul-buerkner
brms:Bayesian Regression Models using 'Stan'
Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bürkner. Last updated 2 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
5.4 match 1.3k stars 16.61 score 13k scripts 34 dependentsjdjohn215
pollster:Calculate Crosstab and Topline Tables of Weighted Survey Data
Calculate common types of tables for weighted survey data. Options include topline and (2-way and 3-way) crosstab tables of categorical or ordinal data as well as summary tables of weighted numeric variables. Optionally, include the margin of error at selected confidence intervals including the design effect. The design effect is calculated as described by Kish (1965) <doi:10.1002/bimj.19680100122> beginning on page 257. Output takes the form of tibbles (simple data frames). This package conveniently handles labelled data, such as that commonly used by 'Stata' and 'SPSS.' Complex survey design is not supported at this time.
Maintained by John D. Johnson. Last updated 2 years ago.
15.3 match 9 stars 5.80 score 47 scriptsjojo-
mipfp:Multidimensional Iterative Proportional Fitting and Alternative Models
An implementation of the iterative proportional fitting (IPFP), maximum likelihood, minimum chi-square and weighted least squares procedures for updating a N-dimensional array with respect to given target marginal distributions (which, in turn can be multidimensional). The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions.
Maintained by Johan Barthelemy. Last updated 4 years ago.
13.0 match 24 stars 6.79 score 86 scripts 3 dependentsawstringer1
aghq:Adaptive Gauss Hermite Quadrature for Bayesian Inference
Adaptive Gauss Hermite Quadrature for Bayesian inference. The AGHQ method for normalizing posterior distributions and making Bayesian inferences based on them. Functions are provided for doing quadrature and marginal Laplace approximations, and summary methods are provided for making inferences based on the results. See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package" <arXiv:2101.04468>.
Maintained by Alex Stringer. Last updated 1 years ago.
15.6 match 5 stars 5.67 score 62 scripts 1 dependentslbelzile
BMAmevt:Multivariate Extremes: Bayesian Estimation of the Spectral Measure
Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014) <doi:10.1016/j.csda.2013.04.021> and Sabourin, Naveau and Fougeres (2013) <doi:10.1007/s10687-012-0163-0>.
Maintained by Leo Belzile. Last updated 2 years ago.
21.9 match 3.90 score 16 scriptsthej022214
hisse:Hidden State Speciation and Extinction
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
Maintained by Jeremy Beaulieu. Last updated 1 months ago.
10.1 match 6 stars 8.45 score 152 scriptscecileproust-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.
7.4 match 62 stars 11.41 score 249 scripts 7 dependentsanestistouloumis
SimCorMultRes:Simulates Correlated Multinomial Responses
Simulates correlated multinomial responses conditional on a marginal model specification.
Maintained by Anestis Touloumis. Last updated 12 months ago.
binarylongitudinal-studiesmultinomialsimulation
13.9 match 7 stars 6.04 score 26 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.
6.3 match 57 stars 13.31 score 632 scripts 43 dependentsr-forge
car:Companion to Applied Regression
Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019.
Maintained by John Fox. Last updated 5 months ago.
5.4 match 15.29 score 43k scripts 901 dependentstidymodels
broom:Convert Statistical Objects into Tidy Tibbles
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
Maintained by Simon Couch. Last updated 4 months ago.
3.8 match 1.5k stars 21.56 score 37k scripts 1.4k dependentsfbartos
BayesTools:Tools for Bayesian Analyses
Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.
Maintained by František Bartoš. Last updated 2 months ago.
12.6 match 7 stars 6.42 score 17 scripts 3 dependentscausal-lda
TrialEmulation:Causal Analysis of Observational Time-to-Event Data
Implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.
Maintained by Isaac Gravestock. Last updated 23 days ago.
causal-inferencelongitudinal-datasurvival-analysiscpp
10.4 match 25 stars 7.72 score 29 scriptsbriencj
dae:Functions Useful in the Design and ANOVA of Experiments
The content falls into the following groupings: (i) Data, (ii) Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v) Matrix functions, (vi) Projector and canonical efficiency functions, and (vii) Miscellaneous functions. There is a vignette describing how to use the design functions for randomizing and assessing designs available as a vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of information when the 'Error' function has been used in the call to 'aov'. The package 'dae' can also be installed from <http://chris.brien.name/rpackages/>.
Maintained by Chris Brien. Last updated 4 months ago.
9.2 match 1 stars 8.62 score 356 scripts 7 dependentsscheike
timereg:Flexible Regression Models for Survival Data
Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package.
Maintained by Thomas Scheike. Last updated 6 months ago.
7.5 match 31 stars 10.42 score 289 scripts 44 dependentsopenanalytics
BIGL:Biochemically Intuitive Generalized Loewe Model
Response surface methods for drug synergy analysis. Available methods include generalized and classical Loewe formulations as well as Highest Single Agent methodology. Response surfaces can be plotted in an interactive 3-D plot and formal statistical tests for presence of synergistic effects are available. Implemented methods and tests are described in the article "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism" by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) <doi:10.1038/s41598-017-18068-5>.
Maintained by Maxim Nazarov. Last updated 2 years ago.
12.9 match 7 stars 6.02 score 37 scriptsalexanderrobitzsch
sirt:Supplementary Item Response Theory Models
Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), MCMC for hierarchical IRT models and testlet models (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>), ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, <doi:10.1177/014662169602000403>), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, <doi:10.1080/00273171.2016.1142856>).
Maintained by Alexander Robitzsch. Last updated 3 months ago.
item-response-theoryopenblascpp
7.7 match 23 stars 10.01 score 280 scripts 22 dependentsa-fernihough
mfx:Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. Greene (2008, pp. 780-7) provides a textbook introduction to this topic.
Maintained by Alan Fernihough. Last updated 6 years ago.
15.4 match 4.97 score 386 scriptsluciu5
antitrust:Tools for Antitrust Practitioners
A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.
Maintained by Charles Taragin. Last updated 6 months ago.
13.3 match 5 stars 5.64 score 36 scripts 2 dependentsharrelfe
Hmisc:Harrell Miscellaneous
Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.
Maintained by Frank E Harrell Jr. Last updated 5 hours ago.
4.3 match 210 stars 17.61 score 17k scripts 750 dependentsopenpharma
beeca:Binary Endpoint Estimation with Covariate Adjustment
Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.
Maintained by Alex Przybylski. Last updated 4 months ago.
13.1 match 6 stars 5.48 score 8 scriptsspatstat
spatstat.geom:Geometrical Functionality of the 'spatstat' Family
Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)
Maintained by Adrian Baddeley. Last updated 2 days ago.
classes-and-objectsdistance-calculationgeometrygeometry-processingimagesmensurationplottingpoint-patternsspatial-dataspatial-data-analysis
5.9 match 7 stars 12.11 score 241 scripts 227 dependentsinlabru-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 3 days ago.
5.6 match 96 stars 12.62 score 832 scripts 6 dependentsbenkeser
drord:Doubly-Robust Estimators for Ordinal Outcomes
Efficient covariate-adjusted estimators of quantities that are useful for establishing the effects of treatments on ordinal outcomes (Benkeser, Diaz, Luedtke 2020 <doi:10.1111/biom.13377>)
Maintained by David Benkeser. Last updated 4 years ago.
causal-inferencecovid-19double-robustmann-whitneyordinal-regression
15.8 match 4 stars 4.38 score 12 scriptsrntq472
precisePlacement:Suite of Functions to Help Get Plot Elements Exactly Where You Want Them
Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().
Maintained by Jasper Watson. Last updated 4 years ago.
17.2 match 2 stars 4.00 score 10 scriptsatorus-research
pharmaRTF:Enhanced RTF Wrapper for Use with Existing Table Packages
Enhanced RTF wrapper written in R for use with existing R tables packages such as 'Huxtable' or 'GT'. This package fills a gap where tables in certain packages can be written out to RTF, but cannot add certain metadata or features to the document that are required/expected in a report for a regulatory submission, such as multiple levels of titles and footnotes, making the document landscape, and controlling properties such as margins.
Maintained by Michael Stackhouse. Last updated 3 years ago.
8.5 match 33 stars 8.01 score 128 scripts 2 dependentsmerliseclyde
BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Maintained by Merlise Clyde. Last updated 4 months ago.
bayesianbayesian-inferencegeneralized-linear-modelslinear-regressionlogistic-regressionmcmcmodel-selectionpoisson-regressionpredictive-modelingregressionvariable-selectionfortranopenblas
6.3 match 44 stars 10.81 score 420 scripts 3 dependentspchausse
gmm:Generalized Method of Moments and Generalized Empirical Likelihood
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Maintained by Pierre Chausse. Last updated 1 years ago.
7.3 match 2 stars 9.28 score 304 scripts 66 dependentsanimint
animint2:Animated Interactive Grammar of Graphics
Functions are provided for defining animated, interactive data visualizations in R code, and rendering on a web page. The 2018 Journal of Computational and Graphical Statistics paper, <doi:10.1080/10618600.2018.1513367> describes the concepts implemented.
Maintained by Toby Hocking. Last updated 27 days ago.
7.6 match 64 stars 8.87 score 173 scriptsocbe-uio
contingencytables:Statistical Analysis of Contingency Tables
Provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.
Maintained by Waldir Leoncio. Last updated 7 months ago.
15.7 match 3 stars 4.13 score 8 scripts 1 dependentsguyabel
migest:Methods for the Indirect Estimation of Bilateral Migration
Tools for estimating, measuring and working with migration data.
Maintained by Guy J. Abel. Last updated 1 months ago.
11.2 match 32 stars 5.80 score 86 scriptsandyliaw-mrk
randomForest:Breiman and Cutlers Random Forests for Classification and Regression
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
Maintained by Andy Liaw. Last updated 6 months ago.
5.3 match 47 stars 12.11 score 35k scripts 282 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
5.0 match 43 stars 12.70 score 300 scripts 9 dependentsinseefr
icarus:Calibrates and Reweights Units in Samples
Provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to 'icarus'. In addition to calibration (with linear, raking and logit methods), 'icarus' features functions for calibration on tight bounds and penalized calibration.
Maintained by Antoine Rebecq. Last updated 2 years ago.
calibrationicarussamplingstatssurvey-sampling
17.1 match 10 stars 3.70 scorepettermostad
lestat:A Package for Learning Statistics
Some simple objects and functions to do statistics using linear models and a Bayesian framework.
Maintained by Petter Mostad. Last updated 7 years ago.
27.5 match 2.28 score 64 scripts 1 dependentsr-forge
latticeExtra:Extra Graphical Utilities Based on Lattice
Building on the infrastructure provided by the lattice package, this package provides several new high-level functions and methods, as well as additional utilities such as panel and axis annotation functions.
Maintained by Deepayan Sarkar. Last updated 3 years ago.
6.0 match 10.18 score 2.6k scripts 233 dependentsoakleyj
SHELF:Tools to Support the Sheffield Elicitation Framework
Implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included.
Maintained by Jeremy Oakley. Last updated 15 days ago.
6.9 match 19 stars 8.90 score 73 scripts 3 dependentsokasag
orf:Ordered Random Forests
An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>.
Maintained by Gabriel Okasa. Last updated 3 years ago.
11.2 match 12 stars 5.38 score 22 scripts 2 dependentslcrawlab
smer:Sparse Marginal Epistasis Test
The Sparse Marginal Epistasis Test is a computationally efficient genetics method which detects statistical epistasis in complex traits; see Stamp et al. (2025, <doi:10.1101/2025.01.11.632557>) for details.
Maintained by Julian Stamp. Last updated 2 months ago.
genomewideassociationepistasisgeneticssnplinearmixedmodelcppepistasis-analysisepistatisgwasgwas-toolsmapitzlibcppopenmp
12.1 match 1 stars 4.95 score 8 scriptscran
oglmx:Estimation of Ordered Generalized Linear Models
Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased (Weiss (1997)). This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.
Maintained by Nathan Carroll. Last updated 7 years ago.
29.9 match 1 stars 2.00 scorebiodiverse
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.
4.6 match 4 stars 13.03 score 652 scripts 12 dependentsdmurdoch
plotrix:Various Plotting Functions
Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.
Maintained by Duncan Murdoch. Last updated 1 years ago.
5.3 match 5 stars 11.31 score 9.2k scripts 361 dependentsbioc
SeqArray:Data Management of Large-Scale Whole-Genome Sequence Variant Calls
Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language.
Maintained by Xiuwen Zheng. Last updated 10 days ago.
infrastructuredatarepresentationsequencinggeneticsbioinformaticsgds-formatsnpsnvweswgscpp
4.9 match 45 stars 12.08 score 1.1k scripts 9 dependentseddelbuettel
tint:'tint' is not 'Tufte'
A 'tufte'-alike style for 'rmarkdown'. A modern take on the 'Tufte' design for pdf and html vignettes, building on the 'tufte' package with additional contributions from the 'knitr' and 'ggtufte' package, and also acknowledging the key influence of 'envisioned css'.
Maintained by Dirk Eddelbuettel. Last updated 4 months ago.
6.0 match 262 stars 9.70 score 318 scripts 1 dependentsbioc
gdsfmt:R Interface to CoreArray Genomic Data Structure (GDS) Files
Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel.
Maintained by Xiuwen Zheng. Last updated 2 days ago.
infrastructuredataimportbioinformaticsgds-formatgenomicscpp
5.0 match 18 stars 11.34 score 920 scripts 29 dependentsstathin
ggm:Graphical Markov Models with Mixed Graphs
Provides functions for defining mixed graphs containing three types of edges, directed, undirected and bi-directed, with possibly multiple edges. These graphs are useful because they capture fundamental independence structures in multivariate distributions and in the induced distributions after marginalization and conditioning. The package is especially concerned with Gaussian graphical models for (i) ML estimation for directed acyclic graphs, undirected and bi-directed graphs and ancestral graph models (ii) testing several conditional independencies (iii) checking global identification of DAG Gaussian models with one latent variable (iv) testing Markov equivalences and generating Markov equivalent graphs of specific types.
Maintained by Giovanni M. Marchetti. Last updated 1 years ago.
7.9 match 7.07 score 295 scripts 29 dependentsbiometry
bipartite:Visualising Bipartite Networks and Calculating Some (Ecological) Indices
Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.
Maintained by Carsten F. Dormann. Last updated 6 days ago.
5.1 match 37 stars 10.93 score 592 scripts 15 dependentsbioc
ggtree:an R package for visualization of tree and annotation data
'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data.
Maintained by Guangchuang Yu. Last updated 5 months ago.
alignmentannotationclusteringdataimportmultiplesequencealignmentphylogeneticsreproducibleresearchsoftwarevisualizationannotationsggplot2phylogenetic-trees
3.3 match 864 stars 16.86 score 5.1k scripts 109 dependentsphilchalmers
mirt:Multidimensional Item Response Theory
Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.
Maintained by Phil Chalmers. Last updated 11 days ago.
3.7 match 210 stars 14.98 score 2.5k scripts 40 dependentsjinli22
spm:Spatial Predictive Modeling
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407> Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015> Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004> Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.
Maintained by Jin Li. Last updated 3 years ago.
9.7 match 3 stars 5.46 score 107 scripts 3 dependentsyunuuuu
ggalign:A 'ggplot2' Extension for Consistent Axis Alignment
A 'ggplot2' extension offers various tools the creation of complex, multi-plot visualizations. Built on the familiar grammar of graphics, it provides intuitive tools to align and organize plots, making it ideal for complex visualizations. It excels in multi-omics research—such as genomics and microbiomes—by simplifying the visualization of intricate relationships between datasets, for example, linking genes to pathways. Whether you need to stack plots, arrange them around a central figure, or create a circular layout, 'ggalign' delivers flexibility and accuracy with minimal effort.
Maintained by Yun Peng. Last updated 16 hours ago.
complex-heatmapsdendrogramdendrogram-heatmapggplotggplot-extensionggplot2heatmapheatmap-visualizationheatmapsmarginal-plotsoncoplotoncoprinttanglegramupsetupsetplot
7.5 match 267 stars 7.08 score 27 scriptsalexpkeil1
qgcomp:Quantile G-Computation
G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. This approach estimates a regression line corresponding to the expected change in the outcome (on the link basis) given a simultaneous increase in the quantile-based category for all exposures. Works with continuous, binary, and right-censored time-to-event outcomes. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
Maintained by Alexander Keil. Last updated 4 days ago.
exposureexposure-mixtureexposure-mixturesquantile-gcomputationsurvival
6.1 match 37 stars 8.73 score 70 scripts 2 dependentsmjskay
ggdist:Visualizations of Distributions and Uncertainty
Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.
Maintained by Matthew Kay. Last updated 4 months ago.
ggplot2uncertaintyuncertainty-visualizationvisualizationcpp
3.4 match 856 stars 15.24 score 3.1k scripts 61 dependentsr-forge
survey:Analysis of Complex Survey Samples
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase and multiphase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Dual-frame designs.
Maintained by "Thomas Lumley". Last updated 6 months ago.
3.8 match 1 stars 13.94 score 13k scripts 232 dependentsrichfitz
diversitree:Comparative 'Phylogenetic' Analyses of Diversification
Contains a number of comparative 'phylogenetic' methods, mostly focusing on analysing diversification and character evolution. Contains implementations of 'BiSSE' (Binary State 'Speciation' and Extinction) and its unresolved tree extensions, 'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE', 'GeoSSE', and 'BiSSE-ness' Other included methods include Markov models of discrete and continuous trait evolution and constant rate 'speciation' and extinction.
Maintained by Richard G. FitzJohn. Last updated 6 months ago.
6.1 match 33 stars 8.51 score 524 scripts 4 dependentstlverse
tmle3shift:Targeted Learning of the Causal Effects of Stochastic Interventions
Targeted maximum likelihood estimation (TMLE) of population-level causal effects under stochastic treatment regimes and related nonparametric variable importance analyses. Tools are provided for TML estimation of the counterfactual mean under a stochastic intervention characterized as a modified treatment policy, such as treatment policies that shift the natural value of the exposure. The causal parameter and estimation were described in Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x> and an improved estimation approach was given by Díaz and van der Laan (2018) <doi:10.1007/978-3-319-65304-4_14>.
Maintained by Nima Hejazi. Last updated 6 months ago.
causal-inferencemachine-learningmarginal-structural-modelsstochastic-interventionstargeted-learningtreatment-effectsvariable-importance
9.7 match 17 stars 5.33 score 42 scripts 1 dependentsholgstr
fmeffects:Model-Agnostic Interpretations with Forward Marginal Effects
Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and 'fmeffects' computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <doi:10.48550/arXiv.2201.08837>.
Maintained by Holger Löwe. Last updated 4 months ago.
9.0 match 2 stars 5.73 score 6 scriptsindrajeetpatil
ggstatsplot:'ggplot2' Based Plots with Statistical Details
Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.
Maintained by Indrajeet Patil. Last updated 20 days ago.
bayes-factorsdatasciencedatavizeffect-sizeggplot-extensionhypothesis-testingnon-parametric-statisticsregression-modelsstatistical-analysis
3.5 match 2.1k stars 14.49 score 3.0k scripts 1 dependentswinvector
WVPlots:Common Plots for Analysis
Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'. Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, conditioned scatter plot with marginal densities, box and stem with matching theoretical distribution, and density with matching theoretical distribution.
Maintained by John Mount. Last updated 11 months ago.
6.3 match 85 stars 8.00 score 280 scriptsyouyifong
marginalizedRisk:Estimating Marginalized Risk
Estimates risk as a function of a marker by integrating over other covariates in a conditional risk model.
Maintained by Youyi Fong. Last updated 10 months ago.
13.5 match 3.71 score 51 scriptsbioc
GeneSelectMMD:Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions
Gene selection based on a mixture of marginal distributions.
Maintained by Weiliang Qiu. Last updated 5 months ago.
13.2 match 3.78 score 1 scripts 1 dependentskcuilla
reactablefmtr:Streamlined Table Styling and Formatting for Reactable
Provides various features to streamline and enhance the styling of interactive reactable tables with easy-to-use and highly-customizable functions and themes. Apply conditional formatting to cells with data bars, color scales, color tiles, and icon sets. Utilize custom table themes inspired by popular websites such and bootstrap themes. Apply sparkline line & bar charts (note this feature requires the 'dataui' package which can be downloaded from <https://github.com/timelyportfolio/dataui>). Increase the portability and reproducibility of reactable tables by embedding images from the web directly into cells. Save the final table output as a static image or interactive file.
Maintained by Kyle Cuilla. Last updated 2 years ago.
customizationdata-visualizationeasy-to-usereproducibletables
5.6 match 209 stars 8.79 score 460 scripts 4 dependentsr-forge
mlogit:Multinomial Logit Models
Maximum Likelihood estimation of random utility discrete choice models, as described in Kenneth Train (2009) Discrete Choice Methods with Simulations <doi:10.1017/CBO9780511805271>.
Maintained by Yves Croissant. Last updated 5 years ago.
4.8 match 9.81 score 1.2k scripts 14 dependentsbioc
SPIAT:Spatial Image Analysis of Tissues
SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis.
Maintained by Yuzhou Feng. Last updated 12 hours ago.
biomedicalinformaticscellbiologyspatialclusteringdataimportimmunooncologyqualitycontrolsinglecellsoftwarevisualization
5.5 match 22 stars 8.59 score 69 scriptscardiomoon
interpretCI:Estimate the Confidence Interval and Interpret Step by Step
Estimate confidence intervals for mean, proportion, mean difference for unpaired and paired samples and proportion difference. Plot the confidence intervals. Generate documents explaining the statistical result step by step.
Maintained by Keon-Woong Moon. Last updated 3 years ago.
7.7 match 4 stars 6.03 score 49 scriptsepinowcast
epidist:Estimate Epidemiological Delay Distributions With brms
Understanding and accurately estimating epidemiological delay distributions is important for public health policy. These estimates influence epidemic situational awareness, control strategies, and resource allocation. This package provides methods to address the key challenges in estimating these distributions, including truncation, interval censoring, and dynamical biases. These issues are frequently overlooked, resulting in biased conclusions. Built on top of 'brms', it allows for flexible modelling including time-varying spatial components and partially pooled estimates of demographic characteristics.
Maintained by Sam Abbott. Last updated 9 days ago.
7.1 match 14 stars 6.52 score 7 scriptslcrawlab
mvMAPIT:Multivariate Genome Wide Marginal Epistasis Test
Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this package, we present the 'multivariate MArginal ePIstasis Test' ('mvMAPIT') – a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact – thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search based methods. Our proposed 'mvMAPIT' builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate 'mvMAPIT' as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>. Stamp et al. (2023) <doi:10.1093/g3journal/jkad118>.
Maintained by Julian Stamp. Last updated 5 months ago.
cppepistasisepistasis-analysisgwasgwas-toolslinear-mixed-modelsmapitmvmapitvariance-componentsopenblascppopenmp
6.7 match 11 stars 6.90 score 17 scripts 1 dependentsnjm18
tboot:Tilted Bootstrap
The tilted bootstrap as implemented in the 'tboot' package is an approach to resampling where samples are drawn from the observed data with some samples appearing more frequently than others. Tilted bootstraping may be used to create simulated clinical trail data with realistic correlation structures and assumed efficacy levels. The 'tboot' package may also be used for simulating a joint Bayesian distribution along along with clinical trials based on the Bayesian distribution.
Maintained by Nathan Morris. Last updated 5 years ago.
9.2 match 1 stars 4.90 score 9 scriptsjknowles
merTools:Tools for Analyzing Mixed Effect Regression Models
Provides methods for extracting results from mixed-effect model objects fit with the 'lme4' package. Allows construction of prediction intervals efficiently from large scale linear and generalized linear mixed-effects models. This method draws from the simulation framework used in the Gelman and Hill (2007) textbook: Data Analysis Using Regression and Multilevel/Hierarchical Models.
Maintained by Jared E. Knowles. Last updated 1 years ago.
4.3 match 105 stars 10.49 score 768 scriptshadley
reshape:Flexibly Reshape Data
Flexibly restructure and aggregate data using just two functions: melt and cast.
Maintained by Hadley Wickham. Last updated 3 years ago.
4.5 match 9.83 score 21k scripts 231 dependentsiqss
clarify:Simulation-Based Inference for Regression Models
Performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316>. 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.
Maintained by Noah Greifer. Last updated 10 months ago.
7.1 match 24 stars 6.14 score 44 scriptspbreheny
ncvreg:Regularization Paths for SCAD and MCP Penalized Regression Models
Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided. For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388> or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.
Maintained by Patrick Breheny. Last updated 3 days ago.
3.6 match 43 stars 12.04 score 458 scripts 38 dependentsbioc
tLOH:Assessment of evidence for LOH in spatial transcriptomics pre-processed data using Bayes factor calculations
tLOH, or transcriptomicsLOH, assesses evidence for loss of heterozygosity (LOH) in pre-processed spatial transcriptomics data. This tool requires spatial transcriptomics cluster and allele count information at likely heterozygous single-nucleotide polymorphism (SNP) positions in VCF format. Bayes factors are calculated at each SNP to determine likelihood of potential loss of heterozygosity event. Two plotting functions are included to visualize allele fraction and aggregated Bayes factor per chromosome. Data generated with the 10X Genomics Visium Spatial Gene Expression platform must be pre-processed to obtain an individual sample VCF with columns for each cluster. Required fields are allele depth (AD) with counts for reference/alternative alleles and read depth (DP).
Maintained by Michelle Webb. Last updated 5 months ago.
copynumbervariationtranscriptionsnpgeneexpressiontranscriptomics
9.8 match 3 stars 4.48 score 4 scriptsmrc-ide
EpiEstim:Estimate Time Varying Reproduction Numbers from Epidemic Curves
Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) <doi:10.1093/aje/kwh255>.
Maintained by Anne Cori. Last updated 7 months ago.
3.6 match 95 stars 12.00 score 1.0k scripts 7 dependentstalbano
equate:Observed-Score Linking and Equating
Contains methods for observed-score linking and equating under the single-group, equivalent-groups, and nonequivalent-groups with anchor test(s) designs. Equating types include identity, mean, linear, general linear, equipercentile, circle-arc, and composites of these. Equating methods include synthetic, nominal weights, Tucker, Levine observed score, Levine true score, Braun/Holland, frequency estimation, and chained equating. Plotting and summary methods, and methods for multivariate presmoothing and bootstrap error estimation are also provided.
Maintained by Anthony Albano. Last updated 2 years ago.
6.3 match 9 stars 6.72 score 31 scripts 3 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
7.0 match 8 stars 6.00 score 21 scriptsavdrark
cmm:Categorical Marginal Models
Quite extensive package for maximum likelihood estimation and weighted least squares estimation of categorical marginal models (CMMs; e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?; Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>.
Maintained by L. A. van der Ark. Last updated 2 years ago.
15.3 match 2.73 score 25 scripts 4 dependentsjacob-long
interactions:Comprehensive, User-Friendly Toolkit for Probing Interactions
A suite of functions for conducting and interpreting analysis of statistical interaction in regression models that was formerly part of the 'jtools' package. Functionality includes visualization of two- and three-way interactions among continuous and/or categorical variables as well as calculation of "simple slopes" and Johnson-Neyman intervals (see e.g., Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These capabilities are implemented for generalized linear models in addition to the standard linear regression context.
Maintained by Jacob A. Long. Last updated 8 months ago.
interactionsmoderationsocial-sciencesstatistics
3.6 match 131 stars 11.39 score 1.2k scripts 5 dependentsawamaeva
trajmsm:Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories
Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.
Maintained by Awa Diop. Last updated 1 years ago.
g-computationinverse-probability-weightsmarginal-structural-modelstmletrajectory-analysis
11.9 match 5 stars 3.40 scoreropensci
mcbette:Model Comparison Using 'babette'
'BEAST2' (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'mcbette' allows to do a Bayesian model comparison over some site and clock models, using 'babette' (<https://github.com/ropensci/babette/>).
Maintained by Richèl J.C. Bilderbeek. Last updated 8 months ago.
7.3 match 7 stars 5.50 score 18 scriptsnorskregnesentral
shapr:Prediction Explanation with Dependence-Aware Shapley Values
Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.
Maintained by Martin Jullum. Last updated 1 months ago.
explainable-aiexplainable-mlrcpprcpparmadilloshapleyopenblascppopenmp
3.7 match 153 stars 10.62 score 175 scripts 1 dependentsdavidrusi
mombf:Model Selection with Bayesian Methods and Information Criteria
Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).
Maintained by David Rossell. Last updated 1 months ago.
5.0 match 7 stars 7.89 score 73 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.
3.2 match 138 stars 12.15 score 113 scripts 4 dependentsecospat
ecospat:Spatial Ecology Miscellaneous Methods
Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) <doi:10.1111/ecog.02671> for details.
Maintained by Olivier Broennimann. Last updated 1 months ago.
4.1 match 32 stars 9.35 score 418 scripts 1 dependentsamrei-stammann
alpaca:Fit GLM's with High-Dimensional k-Way Fixed Effects
Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arXiv:2004.12655>.
Maintained by Amrei Stammann. Last updated 6 months ago.
5.5 match 45 stars 7.01 score 105 scriptsstopsack
risks:Estimate Risk Ratios and Risk Differences using Regression
Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, <doi:10.1093/aje/kwi188>), and others.
Maintained by Konrad Stopsack. Last updated 11 months ago.
binomialbiostatisticsepidemiologyregression-models
7.7 match 5 stars 4.95 score 12 scriptsflorianhartig
BayesianTools:General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics
General-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.
Maintained by Florian Hartig. Last updated 1 years ago.
bayesecological-modelsmcmcoptimizationsmcsystems-biologycpp
3.8 match 122 stars 10.17 score 580 scripts 5 dependentsstephaneguerrier
pempi:Proportion Estimation with Marginal Proxy Information
A system contains easy-to-use tools for the conditional estimation of the prevalence of an emerging or rare infectious diseases using the methods proposed in Guerrier et al. (2023) <arXiv:2012.10745>.
Maintained by Stéphane Guerrier. Last updated 1 years ago.
covidprevalencerare-infectious-diseasesstatistics
8.7 match 4.30 score 9 scriptsdmmelamed
catregs:Post-Estimation Functions for Generalized Linear Mixed Models
Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).
Maintained by David Melamed. Last updated 8 months ago.
10.9 match 3.40 score 28 scriptscrisvarin
gcmr:Gaussian Copula Marginal Regression
Likelihood inference in Gaussian copula marginal regression models.
Maintained by Cristiano Varin. Last updated 3 years ago.
20.3 match 3 stars 1.82 score 22 scriptsgforge
forestplot:Advanced Forest Plot Using 'grid' Graphics
Allows the creation of forest plots with advanced features, such as multiple confidence intervals per row, customizable fonts for individual text elements, and flexible confidence interval drawing. It also supports mixing text with mathematical expressions. The package extends the application of forest plots beyond traditional meta-analyses, offering a more general version of the original 'rmeta' package’s forestplot() function. It relies heavily on the 'grid' package for rendering the plots.
Maintained by Max Gordon. Last updated 4 months ago.
3.1 match 43 stars 11.47 score 716 scripts 21 dependentsarunabhacodes
MPGE:A Two-Step Approach to Testing Overall Effect of Gene-Environment Interaction for Multiple Phenotypes
Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.
Maintained by Arunabha Majumdar. Last updated 4 years ago.
9.7 match 1 stars 3.70 score 1 scriptsjkropko
coxed:Duration-Based Quantities of Interest for the Cox Proportional Hazards Model
Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model as described in Kropko and Harden (2017) <doi:10.1017/S000712341700045X> and Harden and Kropko (2018) <doi:10.1017/psrm.2018.19>.
Maintained by "Kropko, Jonathan". Last updated 4 years ago.
5.9 match 25 stars 6.00 score 132 scripts 1 dependentsgbradburd
BEDASSLE:Quantifies Effects of Geo/Eco Distance on Genetic Differentiation
Provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy.
Maintained by Gideon Bradburd. Last updated 1 years ago.
10.9 match 2 stars 3.26 score 30 scripts 1 dependentshyu-ub
BayesNetBP:Bayesian Network Belief Propagation
Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <https://www.jmlr.org/papers/v6/cowell05a.html>. For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks <doi:10.18637/jss.v094.i03>. The optional 'cyjShiny' package for running the Shiny app is available at <https://github.com/cytoscape/cyjShiny>. Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.
Maintained by Han Yu. Last updated 2 years ago.
bayesian-networksconditional-gaussiannetwork-inferenceprobabilistic-graphical-models
8.9 match 19 stars 3.98 score 3 scriptsopenpharma
graphicalMCP:Graphical Multiple Comparison Procedures
Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.
Maintained by Dong Xi. Last updated 4 months ago.
4.8 match 17 stars 7.35 score 18 scriptsdmphillippo
multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data
Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.
Maintained by David M. Phillippo. Last updated 2 days ago.
3.9 match 35 stars 9.11 score 163 scriptsradiant-rstats
radiant.data:Data Menu for Radiant: Business Analytics using R and Shiny
The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.
Maintained by Vincent Nijs. Last updated 5 months ago.
4.3 match 54 stars 8.30 score 146 scripts 6 dependentslidiamandre
ReturnCurves:Estimation of Return Curves
Estimates the p-probability return curve proposed by Murphy-Barltrop et al. (2023) <doi:10.1002/env.2797>. Implements pointwise and smooth estimation of the angular dependence function introduced by Wadsworth and Tawn (2013) <doi:10.3150/12-BEJ471>.
Maintained by Lídia André. Last updated 1 months ago.
6.9 match 1 stars 5.06 score 19 scriptstsrobinson
cjbart:Heterogeneous Effects Analysis of Conjoint Experiments
A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.
Maintained by Thomas Robinson. Last updated 2 years ago.
7.4 match 9 stars 4.65 score 4 scriptsalemermartinez
rmargint:Robust Marginal Integration Procedures
Three robust marginal integration procedures for additive models based on local polynomial kernel smoothers. As a preliminary estimator of the multivariate function for the marginal integration procedure, a first approach uses local constant M-estimators, a second one uses local polynomials of order 1 over all the components of covariates, and the third one uses M-estimators based on local polynomials but only in the direction of interest. For this last approach, estimators of the derivatives of the additive functions can be obtained. All three procedures can compute predictions for points outside the training set if desired. See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.
Maintained by Alejandra Martinez. Last updated 1 years ago.
12.7 match 2.70 scorealanarnholt
BSDA:Basic Statistics and Data Analysis
Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.
Maintained by Alan T. Arnholt. Last updated 2 years ago.
3.8 match 7 stars 9.11 score 1.3k scripts 6 dependentscsblatvia
surveyplanning:Survey Planning Tools
Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.
Maintained by Juris Breidaks. Last updated 4 years ago.
7.5 match 8 stars 4.53 score 14 scripts 1 dependentsbbolker
prediction:Tidy, Type-Safe 'prediction()' Methods
A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing.
Maintained by Ben Bolker. Last updated 3 months ago.
4.6 match 7.34 score 127 scripts 2 dependentsdanlwarren
ENMTools:Analysis of Niche Evolution using Niche and Distribution Models
Constructing niche models and analyzing patterns of niche evolution. Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography. Warren, D.L., R.E. Glor, and M. Turelli (2008) <doi:10.1111/j.1558-5646.2008.00482.x> Glor, R.E., and D.L. Warren (2011) <doi:10.1111/j.1558-5646.2010.01177.x> Warren, D.L., R.E. Glor, and M. Turelli (2010) <doi:10.1111/j.1600-0587.2009.06142.x> Cardillo, M., and D.L. Warren (2016) <doi:10.1111/geb.12455> D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) <doi:10.1111/ecog.03900>.
Maintained by Dan Warren. Last updated 2 months ago.
4.9 match 105 stars 6.91 score 126 scriptsmqnjqrid
drpop:Efficient and Doubly Robust Population Size Estimation
Estimation of the total population size from capture-recapture data efficiently and with low bias implementing the methods from Das M, Kennedy EH, and Jewell NP (2021) <arXiv:2104.14091>. The estimator is doubly robust against errors in the estimation of the intermediate nuisance parameters. Users can choose from the flexible estimation models provided in the package, or use any other preferred model.
Maintained by Manjari Das. Last updated 3 years ago.
9.9 match 5 stars 3.40 score 2 scriptsmatteo21q
dani:Design and Analysis of Non-Inferiority Trials
Provides tools to help with the design and analysis of non-inferiority trials. These include functions for doing sample size calculations and for analysing non-inferiority trials, using a variety of outcome types and population-level sumamry measures. It also features functions to make trials more resilient by using the concept of non-inferiority frontiers, as described in Quartagno et al. (2019) <arXiv:1905.00241>. Finally it includes function to design and analyse MAMS-ROCI (aka DURATIONS) trials.
Maintained by Matteo Quartagno. Last updated 7 months ago.
6.3 match 2 stars 5.33 score 27 scriptsycphs
openxlsx:Read, Write and Edit xlsx Files
Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect' packages with the added benefit of removing the dependency on Java.
Maintained by Jan Marvin Garbuszus. Last updated 2 months ago.
1.8 match 232 stars 18.98 score 20k scripts 270 dependentszpneal
incidentally:Generates Incidence Matrices and Bipartite Graphs
Functions to generate incidence matrices and bipartite graphs that have (1) a fixed fill rate, (2) given marginal sums, (3) marginal sums that follow given distributions, or (4) represent bill sponsorships in the US Congress <doi:10.31219/osf.io/ectms>. It can also generate an incidence matrix from an adjacency matrix, or bipartite graph from a unipartite graph, via a social process mirroring team, group, or organization formation <doi:10.48550/arXiv.2204.13670>.
Maintained by Zachary Neal. Last updated 2 years ago.
6.8 match 7 stars 4.89 score 11 scriptslcbc-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 Øystein Sørensen. Last updated 6 months ago.
generalized-additive-modelshierarchical-modelsitem-response-theorylatent-variable-modelsstructural-equation-modelscpp
4.4 match 29 stars 7.33 score 41 scriptshadley
reshape2:Flexibly Reshape Data: A Reboot of the Reshape Package
Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast').
Maintained by Hadley Wickham. Last updated 4 years ago.
1.9 match 210 stars 17.19 score 94k scripts 2.0k dependentshanzifei
gcKrig:Analysis of Geostatistical Count Data using Gaussian Copulas
Provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations. Description of the method is available from: Han and DeOliveira (2018) <doi:10.18637/jss.v087.i13>.
Maintained by Zifei Han. Last updated 3 years ago.
28.1 match 1 stars 1.15 score 14 scriptselipousson
papersize:Sizing Plots and Files for Paper
A set of convenience functions extending grid, ggplot2, and patchwork to help you size plots and files for printing to paper, postcards, playing cards, and other physical media.
Maintained by Eli Pousson. Last updated 5 months ago.
9.8 match 4 stars 3.26 score 3 scripts 1 dependentsdavidgohel
officer:Manipulation of Microsoft Word and PowerPoint Documents
Access and manipulate 'Microsoft Word', 'RTF' and 'Microsoft PowerPoint' documents from R. The package focuses on tabular and graphical reporting from R; it also provides two functions that let users get document content into data objects. A set of functions lets add and remove images, tables and paragraphs of text in new or existing documents. The package does not require any installation of Microsoft products to be able to write Microsoft files.
Maintained by David Gohel. Last updated 1 months ago.
ms-office-documentspowerpointword
2.0 match 630 stars 15.79 score 4.1k scripts 137 dependentskassambara
ggpubr:'ggplot2' Based Publication Ready Plots
The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.
Maintained by Alboukadel Kassambara. Last updated 2 years ago.
1.9 match 1.2k stars 16.68 score 65k scripts 409 dependentsemanuelsommer
portvine:Vine Based (Un)Conditional Portfolio Risk Measure Estimation
Following Sommer (2022) <https://mediatum.ub.tum.de/1658240> portfolio level risk estimates (e.g. Value at Risk, Expected Shortfall) are estimated by modeling each asset univariately by an ARMA-GARCH model and then their cross dependence via a Vine Copula model in a rolling window fashion. One can even condition on variables/time series at certain quantile levels to stress test the risk measure estimates.
Maintained by Emanuel Sommer. Last updated 1 years ago.
expected-shortfallgarch-modelsvalue-at-riskvine-copulascpp
6.1 match 22 stars 5.04 score 6 scriptsriccardo-df
ocf:Ordered Correlation Forest
Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025) <doi:10.1080/07474938.2024.2429596>.
Maintained by Riccardo Di Francesco. Last updated 16 days ago.
7.7 match 3.95 score 5 scripts 1 dependentsquanteda
quanteda:Quantitative Analysis of Textual Data
A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
Maintained by Kenneth Benoit. Last updated 2 months ago.
corpusnatural-language-processingquantedatext-analyticsonetbbcpp
1.8 match 851 stars 16.68 score 5.4k scripts 51 dependentsalexanderrobitzsch
CDM:Cognitive Diagnosis Modeling
Functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. This package enables the estimation of the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>), the multiple group (polytomous) GDINA model (de la Torre, 2011, <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> or Robitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>) for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as Ravand and Robitzsch (2015).
Maintained by Alexander Robitzsch. Last updated 9 months ago.
cognitive-diagnostic-modelsitem-response-theorycpp
3.4 match 22 stars 8.76 score 138 scripts 28 dependentsbetsybersson
fabPrediction:Compute FAB (Frequentist and Bayes) Conformal Prediction Intervals
Computes and plots prediction intervals for numerical data or prediction sets for categorical data using prior information. Empirical Bayes procedures to estimate the prior information from multi-group data are included. See, e.g.,Bersson and Hoff (2022) <arXiv:2204.08122> "Optimal Conformal Prediction for Small Areas".
Maintained by Elizabeth Bersson. Last updated 12 months ago.
6.9 match 4.30 score 2 scriptsdaniel-jg
BeviMed:Bayesian Evaluation of Variant Involvement in Mendelian Disease
A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.
Maintained by Daniel Greene. Last updated 10 months ago.
8.7 match 1 stars 3.41 score 17 scriptsxiangzhou09
localIV:Estimation of Marginal Treatment Effects using Local Instrumental Variables
In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such as the average treatment effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection equation and an outcome equation, the function mte() estimates the MTE via the semiparametric local instrumental variables method or the normal selection model. The function mte_at() evaluates MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at() evaluates MTE projected onto the estimated propensity score. The function ace() estimates population-level average causal effects such as ATE, ATT, or the marginal policy relevant treatment effect.
Maintained by Xiang Zhou. Last updated 5 years ago.
8.7 match 5 stars 3.40 score 6 scriptsropensci
beautier:'BEAUti' from R
'BEAST2' (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAUti 2' (which is part of 'BEAST2') is a GUI tool that allows users to specify the many possible setups and generates the XML file 'BEAST2' needs to run. This package provides a way to create 'BEAST2' input files without active user input, but using R function calls instead.
Maintained by Richèl J.C. Bilderbeek. Last updated 22 days ago.
bayesianbeastbeast2beautiphylogenetic-inferencephylogenetics
3.4 match 13 stars 8.76 score 198 scripts 5 dependentsrstudio
tfprobability:Interface to 'TensorFlow Probability'
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
Maintained by Tomasz Kalinowski. Last updated 3 years ago.
3.4 match 54 stars 8.63 score 221 scripts 3 dependentsbioc
TRONCO:TRONCO, an R package for TRanslational ONCOlogy
The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC).
Maintained by Luca De Sano. Last updated 5 months ago.
biomedicalinformaticsbayesiangraphandnetworksomaticmutationnetworkinferencenetworkclusteringdataimportsinglecellimmunooncologyalgorithmscancer-inferencetumors
4.5 match 30 stars 6.50 score 38 scriptsrsetienne
DAISIE:Dynamical Assembly of Islands by Speciation, Immigration and Extinction
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. (2015) <doi:10.1111/ele.12461>.
Maintained by Rampal S. Etienne. Last updated 1 months ago.
3.4 match 9 stars 8.59 score 55 scripts 1 dependentstesselle
nexus:Sourcing Archaeological Materials by Chemical Composition
Exploration and analysis of compositional data in the framework of Aitchison (1986, ISBN: 978-94-010-8324-9). This package provides tools for chemical fingerprinting and source tracking of ancient materials.
Maintained by Nicolas Frerebeau. Last updated 12 days ago.
archaeologyarchaeological-sciencearchaeometrycompositional-dataprovenance-studies
5.6 match 5.21 score 26 scripts 1 dependentseasystats
bayestestR:Understand and Describe Bayesian Models and Posterior Distributions
Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.
Maintained by Dominique Makowski. Last updated 6 hours ago.
bayes-factorsbayesfactorbayesianbayesian-frameworkcredible-intervaleasystatshacktoberfesthdimapposterior-distributionsrope
1.7 match 579 stars 16.84 score 2.2k scripts 82 dependentssvetlanaeden
survSpearman:Nonparametric Spearman's Correlation for Survival Data
Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.
Maintained by Svetlana Eden. Last updated 2 years ago.
7.8 match 3.70 score 4 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.
1.9 match 100 stars 15.36 score 1.4k scripts 36 dependentsrje42
contingency:Discrete Multivariate Probability Distributions
Provides an object class for dealing with many multivariate probability distributions at once, useful for simulation.
Maintained by Robin Evans. Last updated 6 months ago.
7.2 match 4.00 score 5 scriptsr-forge
mvtnorm:Multivariate Normal and t Distributions
Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package.
Maintained by Torsten Hothorn. Last updated 17 days ago.
1.8 match 15.84 score 13k scripts 2.6k dependentscran
ICSNP:Tools for Multivariate Nonparametrics
Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.
Maintained by Klaus Nordhausen. Last updated 1 years ago.
7.3 match 3.84 score 14 dependentsmuschellij2
neurobase:'Neuroconductor' Base Package with Helper Functions for 'nifti' Objects
Base package for 'Neuroconductor', which includes many helper functions that interact with objects of class 'nifti', implemented by package 'oro.nifti', for reading/writing and also other manipulation functions.
Maintained by John Muschelli. Last updated 1 months ago.
3.3 match 5 stars 8.49 score 486 scripts 7 dependentsajbass
lit:Latent Interaction Testing for Genome-Wide Studies
Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies.
Maintained by Andrew Bass. Last updated 2 years ago.
6.8 match 2 stars 4.15 score 14 scriptsteunbrand
ggh4x:Hacks for 'ggplot2'
A 'ggplot2' extension that does a variety of little helpful things. The package extends 'ggplot2' facets through customisation, by setting individual scales per panel, resizing panels and providing nested facets. Also allows multiple colour and fill scales per plot. Also hosts a smaller collection of stats, geoms and axis guides.
Maintained by Teun van den Brand. Last updated 3 months ago.
2.0 match 616 stars 13.98 score 4.4k scripts 20 dependentsgshs-ornl
revengc:Reverse Engineering Summarized Data
Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses). The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers decoupled and/or censored count data with two main functions. The cnbinom.pars() function estimates the average and dispersion parameter of a censored univariate frequency table. The rec() function reverse engineers summarized data into an uncensored bivariate table of probabilities.
Maintained by Samantha Duchscherer. Last updated 6 years ago.
8.1 match 5 stars 3.44 score 11 scriptsmbaldassaro
sampler:Sample Design, Drawing & Data Analysis Using Data Frames
Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.
Maintained by Michael Baldassaro. Last updated 4 years ago.
5.7 match 7 stars 4.91 score 117 scriptsdipterix
filearray:File-Backed Array for Out-of-Memory Computation
Stores large arrays in files to avoid occupying large memories. Implemented with super fast gigabyte-level multi-threaded reading/writing via 'OpenMP'. Supports multiple non-character data types (double, float, complex, integer, logical, and raw).
Maintained by Zhengjia Wang. Last updated 4 months ago.
arraybig-datamemory-mapout-of-memoryoutofmemorycpp
4.3 match 17 stars 6.49 score 10 scripts 3 dependentsekstroem
MESS:Miscellaneous Esoteric Statistical Scripts
A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book. See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall.
Maintained by Claus Thorn Ekstrøm. Last updated 29 days ago.
biostatisticspower-analysisstatistical-analysisstatistical-methodsstatistical-modelsopenblascpp
3.5 match 4 stars 7.76 score 328 scripts 13 dependentskassambara
rstatix:Pipe-Friendly Framework for Basic Statistical Tests
Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix. Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 'Cramer V' for the association between categorical variables. The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.
Maintained by Alboukadel Kassambara. Last updated 2 years ago.
1.8 match 456 stars 15.16 score 11k scripts 420 dependentsstatistikat
VIM:Visualization and Imputation of Missing Values
New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
Maintained by Matthias Templ. Last updated 7 months ago.
hotdeckimputation-methodsmodel-predictionsvisualizationcpp
1.9 match 85 stars 14.44 score 2.6k scripts 19 dependentslaijiangshan
glmm.hp:Hierarchical Partitioning of Marginal R2 for Generalized Mixed-Effect Models
Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.
Maintained by Jiangshan Lai. Last updated 3 months ago.
5.0 match 7 stars 5.40 score 11 scriptsadeverse
ade4:Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences
Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.
Maintained by Aurélie Siberchicot. Last updated 12 days ago.
1.8 match 39 stars 14.96 score 2.2k scripts 256 dependentslaplacesdemonr
LaplacesDemon:Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Maintained by Henrik Singmann. Last updated 12 months ago.
2.0 match 93 stars 13.45 score 1.8k scripts 60 dependentsstan-dev
posterior:Tools for Working with Posterior Distributions
Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.
Maintained by Paul-Christian Bürkner. Last updated 10 days ago.
1.7 match 168 stars 16.13 score 3.3k scripts 342 dependentsnteetor
cascadess:A Style Pronoun for 'htmltools' Tags
Apply styles to tag elements directly and with the .style pronoun. Using the pronoun, styles are created within the context of a tag element. Change borders, backgrounds, text, margins, layouts, and more.
Maintained by Nathan Teetor. Last updated 5 months ago.
5.5 match 19 stars 4.82 score 4 scriptsnathansam
BASSLINE:Bayesian Survival Analysis Using Shape Mixtures of Log-Normal Distributions
Mixtures of life distributions provide a convenient framework for survival analysis: particularly when standard models such as the Weibull are unable to capture some features from the data. These mixtures can also account for unobserved heterogeneity or outlying observations. BASSLINE uses shape mixtures of log-normal distributions and has particular applicability to data with fat tails.
Maintained by Nathan Constantine-Cooke. Last updated 3 years ago.
9.9 match 2.70 scoredankelley
oce:Analysis of Oceanographic Data
Supports the analysis of Oceanographic data, including 'ADCP' measurements, measurements made with 'argo' floats, 'CTD' measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.
Maintained by Dan Kelley. Last updated 1 days ago.
1.7 match 146 stars 15.42 score 4.2k scripts 18 dependentsmariushofert
qrmtools:Tools for Quantitative Risk Management
Functions and data sets for reproducing selected results from the book "Quantitative Risk Management: Concepts, Techniques and Tools". Furthermore, new developments and auxiliary functions for Quantitative Risk Management practice.
Maintained by Marius Hofert. Last updated 1 years ago.
6.4 match 2 stars 4.15 score 237 scriptscran
tensorA:Advanced Tensor Arithmetic with Named Indices
Provides convenience functions for advanced linear algebra with tensors and computation with data sets of tensors on a higher level abstraction. It includes Einstein and Riemann summing conventions, dragging, co- and contravariate indices, parallel computations on sequences of tensors.
Maintained by K. Gerald van den Boogaart. Last updated 1 years ago.
4.5 match 5.83 score 399 dependentsbioc
baySeq:Empirical Bayesian analysis of patterns of differential expression in count data
This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.
Maintained by Samuel Granjeaud. Last updated 5 months ago.
sequencingdifferentialexpressionmultiplecomparisonsagebayesiancoverage
3.4 match 7.75 score 79 scripts 3 dependentsjames-thorson-noaa
dsem:Fit Dynamic Structural Equation Models
Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms."
Maintained by James Thorson. Last updated 5 days ago.
3.8 match 11 stars 6.90 score 24 scriptsanikoszabo
CorrBin:Nonparametrics with Clustered Binary and Multinomial Data
Implements non-parametric analyses for clustered binary and multinomial data. The elements of the cluster are assumed exchangeable, and identical joint distribution (also known as marginal compatibility, or reproducibility) is assumed for clusters of different sizes. A trend test based on stochastic ordering is implemented. Szabo A, George EO. (2010) <doi:10.1093/biomet/asp077>; George EO, Cheon K, Yuan Y, Szabo A (2016) <doi:10.1093/biomet/asw009>.
Maintained by Aniko Szabo. Last updated 7 months ago.
7.6 match 3.45 score 28 scripts