Showing 200 of total 4658 results (show query)
spatstat
spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family
Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.
Maintained by Adrian Baddeley. Last updated 7 days ago.
analysis-of-variancecluster-processconfidence-intervalscox-processdeterminantal-point-processesgibbs-processinfluenceleveragemodel-diagnosticsneyman-scottparameter-estimationpoisson-processspatial-analysisspatial-modellingspatial-point-processesstatistical-inference
163.0 match 5 stars 9.09 score 6 scripts 46 dependentsgamlss-dev
gamlss.dist:Distributions for Generalized Additive Models for Location Scale and Shape
A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.
Maintained by Mikis Stasinopoulos. Last updated 21 days ago.
119.9 match 4 stars 10.50 score 346 scripts 71 dependentsbiodiverse
unmarked:Models for Data from Unmarked Animals
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Maintained by Ken Kellner. Last updated 1 days ago.
89.4 match 4 stars 13.03 score 652 scripts 12 dependentsrkoenker
quantreg:Quantile Regression
Estimation and inference methods for models for conditional quantile functions: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press, <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, CRC Press, <doi:10.1201/9781315120256>.
Maintained by Roger Koenker. Last updated 6 days ago.
82.1 match 18 stars 13.93 score 2.6k scripts 1.5k dependentspadpadpadpad
rTPC:Fitting and Analysing Thermal Performance Curves
Helps to fit thermal performance curves (TPCs). 'rTPC' contains 26 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.
Maintained by Daniel Padfield. Last updated 24 days ago.
105.1 match 25 stars 9.07 score 267 scriptslbbe-software
fitdistrplus:Help to Fit of a Parametric Distribution to Non-Censored or Censored Data
Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation.
Maintained by Aurélie Siberchicot. Last updated 12 days ago.
58.4 match 54 stars 16.15 score 4.5k scripts 153 dependentslme4
lme4:Linear Mixed-Effects Models using 'Eigen' and S4
Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
Maintained by Ben Bolker. Last updated 3 days ago.
32.0 match 647 stars 20.69 score 35k scripts 1.5k dependentsvitomuggeo
segmented:Regression Models with Break-Points / Change-Points Estimation (with Possibly Random Effects)
Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>). Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, <doi:10.1177/1471082X13504721>). Estimation of piecewise-constant relationships and changepoints (mean-shift models) is discussed in Fasola et al. (2018, <doi:10.1007/s00180-017-0740-4>).
Maintained by Vito M. R. Muggeo. Last updated 16 days ago.
63.9 match 9 stars 10.03 score 1.2k scripts 203 dependentstherneau
survival:Survival Analysis
Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.
Maintained by Terry M Therneau. Last updated 3 months ago.
30.9 match 400 stars 20.43 score 29k scripts 3.9k dependentscran
nlme:Linear and Nonlinear Mixed Effects Models
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Maintained by R Core Team. Last updated 2 months ago.
45.8 match 6 stars 13.00 score 13k scripts 8.7k dependentsalexkowa
EnvStats:Package for Environmental Statistics, Including US EPA Guidance
Graphical and statistical analyses of environmental data, with focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. Major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. Numerous built-in data sets from regulatory guidance documents and environmental statistics literature. Includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, <doi:10.1007/978-1-4614-8456-1>).
Maintained by Alexander Kowarik. Last updated 17 days ago.
43.9 match 26 stars 12.80 score 2.4k scripts 46 dependentsmartin3141
spant:MR Spectroscopy Analysis Tools
Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.
Maintained by Martin Wilson. Last updated 30 days ago.
brainmrimrsmrshubspectroscopyfortran
61.6 match 25 stars 8.52 score 81 scriptscovaruber
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
39.5 match 43 stars 12.70 score 300 scripts 9 dependentsyrosseel
lavaan:Latent Variable Analysis
Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.
Maintained by Yves Rosseel. Last updated 3 hours ago.
factor-analysisgrowth-curve-modelslatent-variablesmissing-datamultilevel-modelsmultivariate-analysispath-analysispsychometricsstatistical-modelingstructural-equation-modeling
29.5 match 453 stars 16.83 score 8.4k scripts 216 dependentsbioc
limma:Linear Models for Microarray and Omics Data
Data analysis, linear models and differential expression for omics data.
Maintained by Gordon Smyth. Last updated 5 days ago.
exonarraygeneexpressiontranscriptionalternativesplicingdifferentialexpressiondifferentialsplicinggenesetenrichmentdataimportbayesianclusteringregressiontimecoursemicroarraymicrornaarraymrnamicroarrayonechannelproprietaryplatformstwochannelsequencingrnaseqbatcheffectmultiplecomparisonnormalizationpreprocessingqualitycontrolbiomedicalinformaticscellbiologycheminformaticsepigeneticsfunctionalgenomicsgeneticsimmunooncologymetabolomicsproteomicssystemsbiologytranscriptomics
35.2 match 13.81 score 16k scripts 585 dependentstxm676
sars:Fit and Compare Species-Area Relationship Models Using Multimodel Inference
Implements the basic elements of the multi-model inference paradigm for up to twenty species-area relationship models (SAR), using simple R list-objects and functions, as in Triantis et al. 2012 <DOI:10.1111/j.1365-2699.2011.02652.x>. The package is scalable and users can easily create their own model and data objects. Additional SAR related functions are provided.
Maintained by Thomas J. Matthews. Last updated 11 days ago.
70.6 match 9 stars 6.67 score 95 scriptsgavinsimpson
gratia:Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Maintained by Gavin L. Simpson. Last updated 10 hours ago.
distributional-regressiongamgammgeneralized-additive-mixed-modelsgeneralized-additive-modelsggplot2glmlmmgcvpenalized-splinerandom-effectssmoothingsplines
33.7 match 217 stars 12.99 score 1.6k scripts 2 dependentsstatnet
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.
28.3 match 100 stars 15.36 score 1.4k scripts 36 dependentsmurrayefford
secr:Spatially Explicit Capture-Recapture
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Maintained by Murray Efford. Last updated 3 days ago.
42.0 match 3 stars 10.18 score 410 scripts 5 dependentsjranke
mkin:Kinetic Evaluation of Chemical Degradation Data
Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using automatically generated C functions. Non-constant errors can be taken into account using variance by variable or two-component error models <doi:10.3390/environments6120124>. Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end <doi:10.3390/environments8080071>. Please note that no warranty is implied for correctness of results or fitness for a particular purpose.
Maintained by Johannes Ranke. Last updated 1 months ago.
degradationfocus-kineticskinetic-modelskineticsodeode-model
50.9 match 11 stars 8.18 score 78 scripts 1 dependentsgamlss-dev
gamlss:Generalized Additive Models for Location Scale and Shape
Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Maintained by Mikis Stasinopoulos. Last updated 4 months ago.
35.9 match 16 stars 11.23 score 2.0k scripts 49 dependentscran
mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.
Maintained by Simon Wood. Last updated 1 years ago.
31.3 match 32 stars 12.71 score 17k scripts 7.8k dependentsr-forge
robustbase:Basic Robust Statistics
"Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Maintained by Martin Maechler. Last updated 4 months ago.
27.7 match 13.33 score 1.7k scripts 480 dependentsbiodiverse
spAbundance:Univariate and Multivariate Spatial Modeling of Species Abundance
Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.
Maintained by Jeffrey Doser. Last updated 17 days ago.
59.7 match 17 stars 6.15 score 43 scripts 1 dependentspln-team
PLNmodels:Poisson Lognormal Models
The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.
Maintained by Julien Chiquet. Last updated 4 days ago.
count-datamultivariate-analysisnetwork-inferencepcapoisson-lognormal-modelopenblascpp
36.6 match 56 stars 9.50 score 226 scriptsaphalo
ggpmisc:Miscellaneous Extensions to 'ggplot2'
Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Statistics: locate and tag peaks and valleys; label plot with the equation of a fitted polynomial or other types of models; labels with P-value, R^2 or adjusted R^2 or information criteria for fitted models; label with ANOVA table for fitted models; label with summary for fitted models. Model fit classes for which suitable methods are provided by package 'broom' and 'broom.mixed' are supported. Scales and stats to build volcano and quadrant plots based on outcomes, fold changes, p-values and false discovery rates.
Maintained by Pedro J. Aphalo. Last updated 4 months ago.
data-analysisdatavizggplot2-annotationsggplot2-statsstatistics
25.2 match 105 stars 13.32 score 4.4k scripts 14 dependentstrevorhastie
glmnet:Lasso and Elastic-Net Regularized Generalized Linear Models
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.
Maintained by Trevor Hastie. Last updated 2 years ago.
22.1 match 82 stars 15.15 score 22k scripts 736 dependentsalbgarre
biogrowth:Modelling of Population Growth
Modelling of population growth under static and dynamic environmental conditions. Includes functions for model fitting and making prediction under isothermal and dynamic conditions. The methods (algorithms & models) are based on predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)).
Maintained by Alberto Garre. Last updated 2 days ago.
49.3 match 5 stars 6.71 score 44 scriptsigraph
igraph:Network Analysis and Visualization
Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.
Maintained by Kirill Müller. Last updated 10 hours ago.
complex-networksgraph-algorithmsgraph-theorymathematicsnetwork-analysisnetwork-graphfortranlibxml2glpkopenblascpp
15.3 match 582 stars 21.11 score 31k scripts 1.9k dependentsdistancedevelopment
mrds:Mark-Recapture Distance Sampling
Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.
Maintained by Laura Marshall. Last updated 2 months ago.
39.9 match 4 stars 8.05 score 78 scripts 7 dependentseagerai
fastai:Interface to 'fastai'
The 'fastai' <https://docs.fast.ai/index.html> library simplifies training fast and accurate neural networks using modern best practices. It is based on research in to deep learning best practices undertaken at 'fast.ai', including 'out of the box' support for vision, text, tabular, audio, time series, and collaborative filtering models.
Maintained by Turgut Abdullayev. Last updated 11 months ago.
audiocollaborative-filteringdarknetdarknet-image-classificationfastaimedicalobject-detectiontabulartextvision
33.6 match 118 stars 9.40 score 76 scriptsr-spatial
gstat:Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation
Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions; supports sf and stars.
Maintained by Edzer Pebesma. Last updated 10 days ago.
21.3 match 197 stars 14.78 score 4.8k scripts 57 dependentsnicholasjclark
mvgam:Multivariate (Dynamic) Generalized Additive Models
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.
Maintained by Nicholas J Clark. Last updated 18 hours ago.
bayesian-statisticsdynamic-factor-modelsecological-modellingforecastinggaussian-processgeneralised-additive-modelsgeneralized-additive-modelsjoint-species-distribution-modellingmultilevel-modelsmultivariate-timeseriesstantime-series-analysistimeseriesvector-autoregressionvectorautoregressioncpp
31.9 match 139 stars 9.85 score 117 scriptsusepa
spmodel:Spatial Statistical Modeling and Prediction
Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
Maintained by Michael Dumelle. Last updated 4 days ago.
40.8 match 15 stars 7.66 score 112 scripts 3 dependentsdrizopoulos
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.
32.3 match 30 stars 9.59 score 1.0k scripts 27 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.
23.2 match 57 stars 13.31 score 632 scripts 43 dependentstidymodels
parsnip:A Common API to Modeling and Analysis Functions
A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', 'H2O', etc).
Maintained by Max Kuhn. Last updated 4 days ago.
18.3 match 612 stars 16.37 score 3.4k scripts 69 dependentsbiodiverse
ubms:Bayesian Models for Data from Unmarked Animals using 'Stan'
Fit Bayesian hierarchical models of animal abundance and occurrence via the 'rstan' package, the R interface to the 'Stan' C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Maintained by Ken Kellner. Last updated 18 days ago.
distance-samplinghierarchical-modelsn-mixture-modeloccupancystanopenblascpp
37.8 match 35 stars 7.88 score 73 scriptsrsquaredacademy
olsrr:Tools for Building OLS Regression Models
Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.
Maintained by Aravind Hebbali. Last updated 4 months ago.
collinearity-diagnosticslinear-modelsregressionstepwise-regression
24.2 match 103 stars 12.19 score 1.4k scripts 4 dependentsmjskay
tidybayes:Tidy Data and 'Geoms' for Bayesian Models
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands.
Maintained by Matthew Kay. Last updated 6 months ago.
bayesian-data-analysisbrmsggplot2jagsstantidy-datavisualization
20.0 match 733 stars 14.72 score 7.3k scripts 20 dependentsdwarton
mvabund:Statistical Methods for Analysing Multivariate Abundance Data
A set of tools for displaying, modeling and analysing multivariate abundance data in community ecology. See 'mvabund-package.Rd' for details of overall package organization. The package is implemented with the Gnu Scientific Library (<http://www.gnu.org/software/gsl/>) and 'Rcpp' (<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.
Maintained by David Warton. Last updated 1 years ago.
28.7 match 10 stars 10.13 score 680 scripts 5 dependentsglmmtmb
glmmTMB:Generalized Linear Mixed Models using Template Model Builder
Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.
Maintained by Mollie Brooks. Last updated 11 days ago.
17.1 match 312 stars 16.77 score 3.7k scripts 24 dependentsf-rousset
spaMM:Mixed-Effect Models, with or without Spatial Random Effects
Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
Maintained by François Rousset. Last updated 9 months ago.
57.4 match 4.94 score 208 scripts 5 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
17.9 match 393 stars 15.68 score 5.0k scripts 13 dependentsjmsigner
amt:Animal Movement Tools
Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.
Maintained by Johannes Signer. Last updated 4 months ago.
26.3 match 41 stars 10.54 score 418 scriptscanmod
macpan2:Fast and Flexible Compartmental Modelling
Fast and flexible compartmental modelling with Template Model Builder.
Maintained by Steve Walker. Last updated 2 days ago.
compartmental-modelsepidemiologyforecastingmixed-effectsmodel-fittingoptimizationsimulationsimulation-modelingcpp
31.0 match 4 stars 8.89 score 246 scripts 1 dependentsbrockk
trialr:Clinical Trial Designs in 'rstan'
A collection of clinical trial designs and methods, implemented in 'rstan' and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch.
Maintained by Kristian Brock. Last updated 1 years ago.
31.4 match 41 stars 8.55 score 106 scripts 3 dependentstidyverts
fable:Forecasting Models for Tidy Time Series
Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.
Maintained by Mitchell OHara-Wild. Last updated 4 months ago.
19.8 match 565 stars 13.52 score 2.1k scripts 6 dependentsbxc147
Epi:Statistical Analysis in Epidemiology
Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.
Maintained by Bendix Carstensen. Last updated 2 months ago.
27.6 match 4 stars 9.65 score 708 scripts 11 dependentslrberge
fixest:Fast Fixed-Effects Estimations
Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://github.com/lrberge/fixest/blob/master/_DOCS/FENmlm_paper.pdf>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.
Maintained by Laurent Berge. Last updated 7 months ago.
18.1 match 387 stars 14.69 score 3.8k scripts 25 dependentsbioc
variancePartition:Quantify and interpret drivers of variation in multilevel gene expression experiments
Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures.
Maintained by Gabriel E. Hoffman. Last updated 2 months ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
22.7 match 7 stars 11.69 score 1.1k scripts 3 dependentsopen-aims
bayesnec:A Bayesian No-Effect- Concentration (NEC) Algorithm
Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017)<doi:10.18637/jss.v080.i01>; Burkner (2018)<doi:10.32614/RJ-2018-017>; Carpenter 'et al.' (2017)<doi:10.18637/jss.v076.i01> to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECx' values, but more particularly 'NEC' (see Fox (2010)<doi:10.1016/j.ecoenv.2009.09.012>), 'NSEC' (see Fisher and Fox (2023)<doi:10.1002/etc.5610>), and 'N(S)EC (see Fisher et al. 2023<doi:10.1002/ieam.4809>). A full description of this package can be found in Fisher 'et al.' (2024)<doi:10.18637/jss.v110.i05>. This package expands and supersedes an original version implemented in 'R2jags' (see Su and Yajima (2020)<https://CRAN.R-project.org/package=R2jags>; Fisher et al. (2020)<doi:10.5281/ZENODO.3966864>).
Maintained by Rebecca Fisher. Last updated 7 months ago.
bayesian-inferenceconcentration-responseecotoxicologyno-effect-concentrationnon-linear-decaythreshold-derivationtoxicology
31.7 match 12 stars 8.11 score 360 scriptsrefunders
refund:Regression with Functional Data
Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.
Maintained by Julia Wrobel. Last updated 6 months ago.
24.4 match 41 stars 10.25 score 472 scripts 16 dependentslbelzile
mev:Modelling of Extreme Values
Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
Maintained by Leo Belzile. Last updated 5 months ago.
extreme-value-statisticslikelihood-functionsmax-stablesimulationthreshold-selectionopenblascppopenmp
30.1 match 13 stars 8.23 score 94 scripts 4 dependentsalexpghayes
distributions3:Probability Distributions as S3 Objects
Tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.
Maintained by Alex Hayes. Last updated 6 months ago.
21.8 match 102 stars 11.35 score 118 scripts 7 dependentscran
MASS:Support Functions and Datasets for Venables and Ripley's MASS
Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).
Maintained by Brian Ripley. Last updated 16 days ago.
22.7 match 19 stars 10.53 score 11k dependentsnicwir
QurvE:Robust and User-Friendly Analysis of Growth and Fluorescence Curves
High-throughput analysis of growth curves and fluorescence data using three methods: linear regression, growth model fitting, and smooth spline fit. Analysis of dose-response relationships via smoothing splines or dose-response models. Complete data analysis workflows can be executed in a single step via user-friendly wrapper functions. The results of these workflows are summarized in detailed reports as well as intuitively navigable 'R' data containers. A 'shiny' application provides access to all features without requiring any programming knowledge. The package is described in further detail in Wirth et al. (2023) <doi:10.1038/s41596-023-00850-7>.
Maintained by Nicolas T. Wirth. Last updated 1 years ago.
38.7 match 25 stars 6.00 score 7 scriptsjclavel
mvMORPH:Multivariate Comparative Tools for Fitting Evolutionary Models to Morphometric Data
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.
Maintained by Julien Clavel. Last updated 1 months ago.
24.4 match 17 stars 9.46 score 189 scripts 3 dependentsasgr
hyper.fit:N-Dimensional Hyperplane Fitting with Errors
High level functions for hyperplane fitting (hyper.fit()) and visualising (hyper.plot2d() / hyper.plot3d()). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
Maintained by Aaron Robotham. Last updated 9 months ago.
76.3 match 7 stars 3.02 score 15 scriptsmlcollyer
RRPP:Linear Model Evaluation with Randomized Residuals in a Permutation Procedure
Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.
Maintained by Michael Collyer. Last updated 26 days ago.
23.4 match 4 stars 9.84 score 173 scripts 7 dependentskhliland
pls:Partial Least Squares and Principal Component Regression
Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
Maintained by Kristian Hovde Liland. Last updated 2 months ago.
17.0 match 36 stars 13.50 score 3.2k scripts 85 dependentsbpfaff
QRM:Provides R-Language Code to Examine Quantitative Risk Management Concepts
Provides functions/methods to accompany the book Quantitative Risk Management: Concepts, Techniques and Tools by Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.
Maintained by Bernhard Pfaff. Last updated 5 years ago.
50.3 match 4.53 score 181 scripts 5 dependentscomeetie
greed:Clustering and Model Selection with the Integrated Classification Likelihood
An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).
Maintained by Etienne Côme. Last updated 2 years ago.
37.7 match 14 stars 5.94 score 41 scriptsmoviedo5
fda.usc:Functional Data Analysis and Utilities for Statistical Computing
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.
Maintained by Manuel Oviedo de la Fuente. Last updated 4 months ago.
functional-data-analysisfortran
22.8 match 12 stars 9.72 score 560 scripts 22 dependentsalexiosg
rugarch:Univariate GARCH Models
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Maintained by Alexios Galanos. Last updated 3 months ago.
18.3 match 26 stars 12.13 score 1.3k scripts 15 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.
21.1 match 31 stars 10.42 score 289 scripts 44 dependentsusepa
SSN2:Spatial Modeling on Stream Networks
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.
Maintained by Michael Dumelle. Last updated 7 months ago.
33.1 match 18 stars 6.59 score 36 scripts 2 dependentsveronica0206
nlpsem:Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework
Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) <arXiv:2302.03237v2>.
Maintained by Jin Liu. Last updated 4 months ago.
31.3 match 145 stars 6.91 score 16 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 3 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
13.0 match 1.3k stars 16.61 score 13k scripts 34 dependentsbethatkinson
rpart:Recursive Partitioning and Regression Trees
Recursive partitioning for classification, regression and survival trees. An implementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Maintained by Beth Atkinson. Last updated 8 months ago.
13.0 match 52 stars 16.59 score 18k scripts 1.6k dependentsstan-dev
loo:Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models
Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.
Maintained by Jonah Gabry. Last updated 3 days ago.
bayesbayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticscross-validationinformation-criterionmodel-comparisonstan
12.1 match 152 stars 17.30 score 2.6k scripts 297 dependentssquidlobster
castor:Efficient Phylogenetics on Large Trees
Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.
Maintained by Stilianos Louca. Last updated 4 months ago.
36.1 match 2 stars 5.75 score 450 scripts 9 dependentsalexanderrobitzsch
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
20.6 match 23 stars 10.01 score 280 scripts 22 dependentsdanforthcenter
pcvr:Plant Phenotyping and Bayesian Statistics
Analyse common types of plant phenotyping data, provide a simplified interface to longitudinal growth modeling and select Bayesian statistics, and streamline use of 'PlantCV' output. Several Bayesian methods and reporting guidelines for Bayesian methods are described in Kruschke (2018) <doi:10.1177/2515245918771304>, Kruschke (2013) <doi:10.1037/a0029146>, and Kruschke (2021) <doi:10.1038/s41562-021-01177-7>.
Maintained by Josh Sumner. Last updated 5 days ago.
29.3 match 4 stars 6.99 score 39 scriptscran
drc:Analysis of Dose-Response Curves
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
Maintained by Christian Ritz. Last updated 9 years ago.
24.4 match 8 stars 8.39 score 1.4k scripts 28 dependentsklvoje
evoTS:Analyses of Evolutionary Time-Series
Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the 'paleoTS' package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.
Maintained by Kjetil Lysne Voje. Last updated 9 months ago.
47.0 match 1 stars 4.26 score 184 scriptshwborchers
pracma:Practical Numerical Math Functions
Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.
Maintained by Hans W. Borchers. Last updated 1 years ago.
16.0 match 29 stars 12.34 score 6.6k scripts 931 dependentsnlmixr2
nlmixr2est:Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines
Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Maintained by Matthew Fidler. Last updated 26 days ago.
23.6 match 9 stars 8.26 score 26 scripts 9 dependentscran
FIT:Transcriptomic Dynamics Models in Field Conditions
Provides functionality for constructing statistical models of transcriptomic dynamics in field conditions. It further offers the function to predict expression of a gene given the attributes of samples and meteorological data. Nagano, A. J., Sato, Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and Izawa, T. (2012). <doi:10.1016/j.cell.2012.10.048>. Iwayama, K., Aisaka, Y., Kutsuna, N., and Nagano, A. J. (2017). <doi:10.1093/bioinformatics/btx049>.
Maintained by Koji Iwayama. Last updated 6 years ago.
64.4 match 3.00 scorekjellpk
fit.models:Compare Fitted Models
The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002) 'Robust Library User's Guide' <http://robust.r-forge.r-project.org/Robust.pdf>). The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally.
Maintained by Kjell Konis. Last updated 5 years ago.
51.5 match 3.74 score 45 scripts 10 dependentsmbinois
hetGP:Heteroskedastic Gaussian Process Modeling and Design under Replication
Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Maintained by Mickael Binois. Last updated 6 months ago.
39.3 match 5 stars 4.89 score 260 scripts 2 dependentsvegandevs
vegan:Community Ecology Package
Ordination methods, diversity analysis and other functions for community and vegetation ecologists.
Maintained by Jari Oksanen. Last updated 16 days ago.
ecological-modellingecologyordinationfortranopenblas
9.8 match 472 stars 19.41 score 15k scripts 440 dependentsmelissagwolf
dynamic:DFI Cutoffs for Latent Variable Models
Returns dynamic fit index (DFI) cutoffs for latent variable models that are tailored to the user's model statement, model type, and sample size. This is the counterpart of the Shiny Application, <https://dynamicfit.app>.
Maintained by Melissa G. Wolf. Last updated 2 months ago.
26.4 match 16 stars 7.13 score 139 scriptsr-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.
15.6 match 11.83 score 1.2k scripts 86 dependentsquantsulting
ghyp:Generalized Hyperbolic Distribution and Its Special Cases
Detailed functionality for working with the univariate and multivariate Generalized Hyperbolic distribution and its special cases (Hyperbolic (hyp), Normal Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t and Gaussian distribution). Especially, it contains fitting procedures, an AIC-based model selection routine, and functions for the computation of density, quantile, probability, random variates, expected shortfall and some portfolio optimization and plotting routines as well as the likelihood ratio test. In addition, it contains the Generalized Inverse Gaussian distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. Quantitative risk management: Concepts, techniques and tools. Princeton University Press, Princeton (2005).
Maintained by Marc Weibel. Last updated 7 months ago.
33.0 match 5.58 score 90 scripts 8 dependentsjfwambaugh
invivoPKfit:Fits Toxicokinetic Models to In Vivo PK Data Sets
Takes in vivo toxicokinetic concentration-time data and fits parameters of 1-compartment and 2-compartment models for each chemical. These methods are described in detail in "Informatics for Toxicokinetics" (submitted).
Maintained by John Wambaugh. Last updated 2 months ago.
70.3 match 2.60 score 4 scriptsoakleyj
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.
20.5 match 19 stars 8.90 score 73 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
30.3 match 8 stars 6.00 score 21 scriptsstephenslab
fastTopics:Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data
Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.
Maintained by Peter Carbonetto. Last updated 17 days ago.
21.5 match 79 stars 8.38 score 678 scripts 1 dependentswillwerscheid
flashier:Empirical Bayes Matrix Factorization
Methods for matrix factorization based on Wang and Stephens (2021) <https://jmlr.org/papers/v22/20-589.html>.
Maintained by Jason Willwerscheid. Last updated 2 months ago.
21.6 match 11 stars 8.32 score 266 scriptsgrimbough
FITfileR:Read FIT files using only native R code
The 'FIT' (Flexible and Interoperable Data Transfer) protocol is designed specifically for sharing data from fitness and health devices. This package allows reading 'FIT' files in pure R without any dependence on external software or SDKs.
Maintained by Mike Smith. Last updated 2 months ago.
32.0 match 56 stars 5.59 score 46 scriptsclbustos
dominanceanalysis:Dominance Analysis
Dominance analysis is a method that allows to compare the relative importance of predictors in multiple regression models: ordinary least squares, generalized linear models, hierarchical linear models, beta regression and dynamic linear models. The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129> for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>, Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>, respectively.
Maintained by Claudio Bustos Navarrete. Last updated 1 years ago.
31.0 match 25 stars 5.75 score 45 scriptsfloschuberth
cSEM:Composite-Based Structural Equation Modeling
Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or Bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.).
Maintained by Florian Schuberth. Last updated 16 days ago.
19.5 match 28 stars 9.11 score 56 scripts 2 dependentsdwarton
ecostats:Code and Data Accompanying the Eco-Stats Text (Warton 2022)
Functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises.
Maintained by David Warton. Last updated 1 years ago.
26.6 match 8 stars 6.58 score 53 scriptsfishfollower
stockassessment:State-Space Assessment Model
Fitting SAM...
Maintained by Anders Nielsen. Last updated 13 days ago.
22.5 match 49 stars 7.76 score 324 scripts 2 dependentsusepa
tcplfit2:A Concentration-Response Modeling Utility
The tcplfit2 R package performs basic concentration-response curve fitting. The original tcplFit() function in the tcpl R package performed basic concentration-response curvefitting to 3 models. With tcplfit2, the core tcpl concentration-response functionality has been expanded to process diverse high-throughput screen (HTS) data generated at the US Environmental Protection Agency, including targeted ToxCast, high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). tcplfit2 can be used independently to support analysis for diverse chemical screening efforts.
Maintained by Jason Brown. Last updated 12 days ago.
24.4 match 6 stars 7.11 score 24 scripts 1 dependentsamvillegas
StMoMo:Stochastic Mortality Modelling
Implementation of the family of generalised age-period-cohort stochastic mortality models. This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.2307/2290201> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations.
Maintained by Andres Villegas. Last updated 5 years ago.
23.5 match 24 stars 7.38 score 84 scripts 2 dependentstopepo
caret:Classification and Regression Training
Misc functions for training and plotting classification and regression models.
Maintained by Max Kuhn. Last updated 3 months ago.
9.0 match 1.6k stars 19.24 score 61k scripts 303 dependentswilkelab
sicegar:Analysis of Single-Cell Viral Growth Curves
Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>), and the package name stands for "SIngle CEll Growth Analysis in R".
Maintained by Claus O. Wilke. Last updated 4 years ago.
26.1 match 9 stars 6.57 score 41 scriptsjsakaluk
dySEM:Dyadic Structural Equation Modeling
Scripting of structural equation models via 'lavaan' for Dyadic Data Analysis, and helper functions for supplemental calculations, tabling, and model visualization. Current models supported include Dyadic Confirmatory Factor Analysis, the Actor–Partner Interdependence Model (observed and latent), the Common Fate Model (observed and latent), Mutual Influence Model (latent), and the Bifactor Dyadic Model (latent).
Maintained by John Sakaluk. Last updated 24 days ago.
27.6 match 6 stars 6.16 score 10 scriptsatsa-es
MARSS:Multivariate Autoregressive State-Space Modeling
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>.
Maintained by Elizabeth Eli Holmes. Last updated 1 years ago.
multivariate-timeseriesstate-space-modelsstatisticstime-series
16.4 match 52 stars 10.34 score 596 scripts 3 dependentsfbartos
RoBMA:Robust Bayesian Meta-Analyses
A framework for estimating ensembles of meta-analytic and meta-regression models (assuming either presence or absence of the effect, heterogeneity, publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to combine the competing meta-analytic models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual components (e.g., effect vs. no effect; Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022, <doi:10.1037/met0000405>). Users can define a wide range of prior distributions for + the effect size, heterogeneity, publication bias (including selection models and PET-PEESE), and moderator components. The package provides convenient functions for summary, visualizations, and fit diagnostics.
Maintained by František Bartoš. Last updated 1 months ago.
meta-analysismodel-averagingpublication-biasjagsopenblascpp
24.3 match 9 stars 6.97 score 53 scriptspilaboratory
sads:Maximum Likelihood Models for Species Abundance Distributions
Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.
Maintained by Paulo I. Prado. Last updated 1 years ago.
19.4 match 23 stars 8.66 score 244 scripts 3 dependentstreynkens
ReIns:Functions from "Reinsurance: Actuarial and Statistical Aspects"
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.
Maintained by Tom Reynkens. Last updated 4 months ago.
extremesreinsurancerisk-analysiscpp
26.6 match 22 stars 6.31 score 186 scriptscran
sn:The Skew-Normal and Related Distributions Such as the Skew-t and the SUN
Build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t and the SUN families. For the skew-normal and the skew-t distributions, statistical methods are provided for data fitting and model diagnostics, in the univariate and the multivariate case.
Maintained by Adelchi Azzalini. Last updated 2 years ago.
22.6 match 3 stars 7.44 score 92 dependentscran
rriskDistributions:Fitting Distributions to Given Data or Known Quantiles
Collection of functions for fitting distributions to given data or by known quantiles. Two main functions fit.perc() and fit.cont() provide users a GUI that allows to choose a most appropriate distribution without any knowledge of the R syntax. Note, this package is a part of the 'rrisk' project.
Maintained by Matthias Greiner. Last updated 8 years ago.
57.1 match 2.94 score 1 dependentsvalentint
robust:Port of the S+ "Robust Library"
Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis.
Maintained by Valentin Todorov. Last updated 7 months ago.
22.2 match 7.52 score 572 scripts 8 dependentsmfasiolo
mgcViz:Visualisations for Generalized Additive Models
Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
Maintained by Matteo Fasiolo. Last updated 7 months ago.
17.8 match 77 stars 9.38 score 1000 scriptsalexanderrobitzsch
TAM:Test Analysis Modules
Includes marginal maximum likelihood estimation and joint maximum likelihood estimation for unidimensional and multidimensional item response models. The package functionality covers the Rasch model, 2PL model, 3PL model, generalized partial credit model, multi-faceted Rasch model, nominal item response model, structured latent class model, mixture distribution IRT models, and located latent class models. Latent regression models and plausible value imputation are also supported. For details see Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>, Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>, Formann, 1982 <doi:10.1002/bimj.4710240209>, Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
Maintained by Alexander Robitzsch. Last updated 6 months ago.
item-response-theoryopenblascpp
18.5 match 16 stars 8.93 score 258 scripts 25 dependentsfriendly
vcdExtra:'vcd' Extensions and Additions
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer.
Maintained by Michael Friendly. Last updated 5 months ago.
categorical-data-visualizationgeneralized-linear-modelsmosaic-plots
15.9 match 24 stars 10.34 score 472 scripts 3 dependentsrsparapa
BART:Bayesian Additive Regression Trees
Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.
Maintained by Rodney Sparapani. Last updated 9 months ago.
20.6 match 14 stars 7.96 score 474 scripts 10 dependentsbcgov
ssdtools:Species Sensitivity Distributions
Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.
Maintained by Joe Thorley. Last updated 24 days ago.
ecotoxicologyenvspecies-sensitivity-distributioncpp
16.0 match 31 stars 10.23 score 111 scripts 4 dependentsdenisrustand
INLAjoint:Multivariate Joint Modeling for Longitudinal and Time-to-Event Outcomes with 'INLA'
Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the 'INLA' package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).
Maintained by Denis Rustand. Last updated 24 days ago.
22.2 match 19 stars 7.36 score 40 scriptscran
gss:General Smoothing Splines
A comprehensive package for structural multivariate function estimation using smoothing splines.
Maintained by Chong Gu. Last updated 5 months ago.
25.4 match 3 stars 6.40 score 137 dependentsianjonsen
aniMotum:Fit Continuous-Time State-Space and Latent Variable Models for Quality Control of Argos Satellite (and Other) Telemetry Data and for Estimating Changes in Animal Movement
Fits continuous-time random walk, correlated random walk and move persistence state-space models for location estimation and behavioural inference from animal tracking data ('Argos', processed light-level 'geolocation', 'GPS'). Template Model Builder ('TMB') is used for fast random-effects estimation. The 'Argos' data can be: (older) least squares-based locations; (newer) Kalman filter-based locations with error ellipse information; or a mixture of both. The models estimate two sets of location states corresponding to: 1) each observation, which are (usually) irregularly timed; and 2) user-specified time intervals (regular or irregular). A track re-routing function is provided to adjust location estimates for known movement barriers. Track simulation functions are provided. Latent variable models are also provided to estimate move persistence from track data not requiring state-space model filtering.
Maintained by Ian Jonsen. Last updated 10 days ago.
animal-movementanimal-trackingrandom-effects-modelstate-space-modelstmbcpp
22.6 match 39 stars 7.16 score 59 scriptsgiampmarra
GJRM:Generalised Joint Regression Modelling
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Maintained by Giampiero Marra. Last updated 5 months ago.
39.3 match 4 stars 4.10 score 67 scripts 5 dependentsjsilve24
fido:Bayesian Multinomial Logistic Normal Regression
Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'.
Maintained by Justin Silverman. Last updated 18 days ago.
19.4 match 20 stars 8.31 score 103 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.
13.4 match 43 stars 12.04 score 458 scripts 38 dependentslcbc-uio
galamm:Generalized Additive Latent and Mixed Models
Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).
Maintained by Øystein Sørensen. Last updated 6 months ago.
generalized-additive-modelshierarchical-modelsitem-response-theorylatent-variable-modelsstructural-equation-modelscpp
21.8 match 29 stars 7.33 score 41 scriptscausal-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
20.7 match 25 stars 7.72 score 29 scriptsphilips-software
latrend:A Framework for Clustering Longitudinal Data
A framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The 'akmedoids' package is available from <https://github.com/MAnalytics/akmedoids>.
Maintained by Niek Den Teuling. Last updated 2 months ago.
cluster-analysisclustering-evaluationclustering-methodsdata-sciencelongitudinal-clusteringlongitudinal-datamixture-modelstime-series-analysis
23.4 match 30 stars 6.77 score 26 scriptsopenpharma
crmPack:Object-Oriented Implementation of CRM Designs
Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.
Maintained by Daniel Sabanes Bove. Last updated 2 months ago.
20.2 match 21 stars 7.79 score 208 scriptsfate-ewi
bayesdfa:Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.
Maintained by Eric J. Ward. Last updated 5 months ago.
18.9 match 28 stars 8.28 score 101 scriptstpetzoldt
growthrates:Estimate Growth Rates from Experimental Data
A collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.
Maintained by Thomas Petzoldt. Last updated 1 years ago.
20.6 match 27 stars 7.52 score 102 scriptsr-forge
GeneralizedHyperbolic:The Generalized Hyperbolic Distribution
Functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, normal inverse Gaussian distribution and generalized inverse Gaussian distribution, including fitting of these distributions to data. Linear models with hyperbolic errors may be fitted using hyperblmFit.
Maintained by David Scott. Last updated 3 months ago.
18.5 match 1 stars 8.39 score 124 scripts 27 dependentsbayesiandemography
bage:Bayesian Estimation and Forecasting of Age-Specific Rates
Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on 'Template Model Builder'.
Maintained by John Bryant. Last updated 2 months ago.
21.2 match 3 stars 7.30 score 39 scriptsjaredhuling
personalized:Estimation and Validation Methods for Subgroup Identification and Personalized Medicine
Provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>. This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) <doi:10.18637/jss.v098.i05>.
Maintained by Jared Huling. Last updated 3 years ago.
causal-inferenceheterogeneity-of-treatment-effectindividualized-treatment-rulespersonalized-medicineprecision-medicinesubgroup-identificationtreatment-effectstreatment-scoring
20.9 match 32 stars 7.38 score 125 scripts 1 dependentsrsquaredacademy
blorr:Tools for Developing Binary Logistic Regression Models
Tools designed to make it easier for beginner and intermediate users to build and validate binary logistic regression models. Includes bivariate analysis, comprehensive regression output, model fit statistics, variable selection procedures, model validation techniques and a 'shiny' app for interactive model building.
Maintained by Aravind Hebbali. Last updated 4 months ago.
logistic-regression-modelsregressioncpp
21.3 match 17 stars 7.13 score 144 scripts 1 dependentsgoranbrostrom
eha:Event History Analysis
Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2022) <doi:10.1201/9780429503764>.
Maintained by Göran Broström. Last updated 9 months ago.
15.4 match 7 stars 9.76 score 308 scripts 10 dependentsr-lum
Luminescence:Comprehensive Luminescence Dating Data Analysis
A collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.
Maintained by Sebastian Kreutzer. Last updated 1 days ago.
bayesian-statisticsdata-sciencegeochronologyluminescenceluminescence-datingopen-scienceoslplottingradiofluorescencetlxsygcpp
13.9 match 15 stars 10.77 score 178 scripts 8 dependentscran
evmix:Extreme Value Mixture Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation
The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code.
Maintained by Carl Scarrott. Last updated 6 years ago.
52.5 match 2 stars 2.85 score 7 dependentscran
apc:Age-Period-Cohort Analysis
Functions for age-period-cohort analysis. Aggregate data can be organised in matrices indexed by age-cohort, age-period or cohort-period. The data can include dose and response or just doses. The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors. Individual-level data should have a row for each individual and columns for each of age, period, and cohort. The statistical model for repeated cross-section is a generalized linear model. The statistical model for panel data is ordinary least squares. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) <DOI:10.1093/biomet/asn026> is used. Thus, the analysis does not rely on ad hoc identification.
Maintained by Bent Nielsen. Last updated 4 years ago.
33.3 match 4.49 score 49 scriptsmarc-girondot
embryogrowth:Tools to Analyze the Thermal Reaction Norm of Embryo Growth
Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.
Maintained by Marc Girondot. Last updated 7 months ago.
62.2 match 1 stars 2.40 score 252 scriptstheoreticalecology
sjSDM:Scalable Joint Species Distribution Modeling
A scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., <doi:10.1111/oik.08618>. The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes. The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>, scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via 'PyTorch' and 'reticulate', which can be run on CPUs or GPUs.
Maintained by Maximilian Pichler. Last updated 24 days ago.
deep-learninggpu-accelerationmachine-learningspecies-distribution-modellingspecies-interactions
19.5 match 69 stars 7.64 score 70 scriptsjaredhuling
fastglm:Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen'
Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard against convergence issues.
Maintained by Jared Huling. Last updated 3 years ago.
17.6 match 57 stars 8.47 score 59 scripts 13 dependentsbioc
PharmacoGx:Analysis of Large-Scale Pharmacogenomic Data
Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line.
Maintained by Benjamin Haibe-Kains. Last updated 2 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationdatasetspharmacogenomicpharmacogxcpp
13.0 match 68 stars 11.39 score 442 scripts 3 dependentsdatacloning
dclone:Data Cloning and MCMC Tools for Maximum Likelihood Methods
Low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov chain Monte Carlo methods as described in Solymos 2010 <doi:10.32614/RJ-2010-011>. Sequential and parallel MCMC support for 'JAGS', 'WinBUGS', 'OpenBUGS', and 'Stan'.
Maintained by Peter Solymos. Last updated 6 months ago.
21.4 match 7 stars 6.91 score 215 scripts 4 dependentsstephenslab
ebnm:Solve the Empirical Bayes Normal Means Problem
Provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). A detailed introduction to the package is provided by Willwerscheid and Stephens (2023) <arXiv:2110.00152>.
Maintained by Peter Carbonetto. Last updated 9 months ago.
17.9 match 12 stars 8.22 score 145 scripts 1 dependentsstephenslab
mashr:Multivariate Adaptive Shrinkage
Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.
Maintained by Peter Carbonetto. Last updated 4 months ago.
13.3 match 91 stars 11.04 score 624 scripts 3 dependentsspatstat
spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family
Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.
Maintained by Adrian Baddeley. Last updated 2 months ago.
density-estimationheat-equationkernel-density-estimationnetwork-analysispoint-processesspatial-data-analysisstatistical-analysisstatistical-inferencestatistical-models
15.2 match 6 stars 9.64 score 35 scripts 43 dependentsericgilleland
ismev:An Introduction to Statistical Modeling of Extreme Values
Functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values' by Stuart Coles. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes.
Maintained by Eric Gilleland. Last updated 7 years ago.
28.2 match 1 stars 5.19 score 326 scripts 13 dependentszhuwang46
mpath:Regularized Linear Models
Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2020) <arXiv:2010.02848>.
Maintained by Zhu Wang. Last updated 3 years ago.
21.9 match 1 stars 6.67 score 131 scripts 4 dependentsjenniniku
gllvm:Generalized Linear Latent Variable Models
Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).
Maintained by Jenni Niku. Last updated 10 hours ago.
13.8 match 52 stars 10.53 score 176 scripts 1 dependentssimsem
semTools:Useful Tools for Structural Equation Modeling
Provides miscellaneous tools for structural equation modeling, many of which extend the 'lavaan' package. For example, latent interactions can be estimated using product indicators (Lin et al., 2010, <doi:10.1080/10705511.2010.488999>) and simple effects probed; analytical power analyses can be conducted (Jak et al., 2021, <doi:10.3758/s13428-020-01479-0>); and scale reliability can be estimated based on estimated factor-model parameters.
Maintained by Terrence D. Jorgensen. Last updated 3 days ago.
10.5 match 79 stars 13.74 score 1.1k scripts 31 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.
11.9 match 138 stars 12.15 score 113 scripts 4 dependentsthongphamthe
PAFit:Generative Mechanism Estimation in Temporal Complex Networks
Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.
Maintained by Thong Pham. Last updated 12 months ago.
complex-networksfit-get-richergeneral-preferential-attachmentminorize-maximizationpreferential-attachmentrich-get-richerscale-freetemporal-networkscppopenmp
22.3 match 17 stars 6.47 score 70 scriptscran
paleoTS:Analyze Paleontological Time-Series
Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).
Maintained by Gene Hunt. Last updated 6 months ago.
32.1 match 1 stars 4.48 score 2 dependentsbaddstats
spatstat.local:Extension to 'spatstat' for Local Composite Likelihood
Extension to the 'spatstat' package, enabling the user to fit point process models to point pattern data by local composite likelihood ('geographically weighted regression').
Maintained by Adrian Baddeley. Last updated 8 months ago.
spatial-analysisspatial-dataspatstat
30.8 match 4.66 score 23 scriptsfchamroukhi
samurais:Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')
Provides a variety of original and flexible user-friendly statistical latent variable models and unsupervised learning algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes. 'samurais' is built upon the following packages, each of them is an autonomous time-series segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references. These models are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?&tab=repositories&q=time-series&type=public&language=matlab>.
Maintained by Florian Lecocq. Last updated 5 years ago.
artificial-intelligencechange-point-detectiondata-sciencedynamic-programmingem-algorithmhidden-markov-modelshidden-process-regressionhuman-activity-recognitionlatent-variable-modelsmodel-selectionmultivariate-timeseriesnewton-raphsonpiecewise-regressionstatistical-inferencestatistical-learningtime-series-analysistime-series-clusteringopenblascpp
23.2 match 12 stars 6.18 score 28 scriptsecmerkle
blavaan:Bayesian Latent Variable Analysis
Fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al. (2021) <doi:10.18637/jss.v100.i06>.
Maintained by Edgar Merkle. Last updated 4 days ago.
bayesian-statisticsfactor-analysisgrowth-curve-modelslatent-variablesmissing-datamultilevel-modelsmultivariate-analysispath-analysispsychometricsstatistical-modelingstructural-equation-modelingcpp
13.1 match 92 stars 10.84 score 183 scripts 3 dependentsr4ss
r4ss:R Code for Stock Synthesis
A collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of '{r4ss}' is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.23.1, from December 2024). Support for 3.24 models is only through the core functions for reading output and plotting.
Maintained by Ian G. Taylor. Last updated 5 days ago.
fisheriesfisheries-stock-assessmentstock-synthesis
12.5 match 43 stars 11.38 score 1.0k scripts 2 dependentssujit-sahu
bmstdr:Bayesian Modeling of Spatio-Temporal Data with R
Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.
Maintained by Sujit K. Sahu. Last updated 1 years ago.
bayesianmodellingspatio-temporal-datacpp
28.5 match 15 stars 4.95 score 12 scriptsvbaliga
gaussplotR:Fit, Predict and Plot 2D Gaussians
Functions to fit two-dimensional Gaussian functions, predict values from fits, and produce plots of predicted data via either 'ggplot2' or base R plotting.
Maintained by Vikram B. Baliga. Last updated 4 years ago.
2d-gaussiangaussiangaussian-fitgaussian-interpolationgaussian-plotgaussian-volumeplotting
27.6 match 4 stars 5.10 score 21 scriptsalexanderrobitzsch
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
16.0 match 22 stars 8.76 score 138 scripts 28 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.
19.9 match 45 stars 7.01 score 105 scriptspbreheny
biglasso:Extending Lasso Model Fitting to Big Data
Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing the user to analyze big data with limited RAM <doi:10.32614/RJ-2021-001>.
Maintained by Patrick Breheny. Last updated 11 days ago.
bigdatalassoout-of-coreparallel-computingcppopenmp
14.1 match 113 stars 9.84 score 74 scripts 1 dependentsfitzlab-al
gdm:Generalized Dissimilarity Modeling
A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) <doi:10.1111/geb.13459> Ferrier S, Manion G, Elith J, Richardson K (2007) <doi:10.1111/j.1472-4642.2007.00341.x>.
Maintained by Matt Fitzpatrick. Last updated 2 months ago.
17.1 match 35 stars 8.12 score 145 scriptstidymodels
infer:Tidy Statistical Inference
The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.
Maintained by Simon Couch. Last updated 6 months ago.
8.9 match 734 stars 15.69 score 3.5k scripts 17 dependentsusepa
tcpl:ToxCast Data Analysis Pipeline
The ToxCast Data Analysis Pipeline ('tcpl') is an R package that manages, curve-fits, plots, and stores ToxCast data to populate its linked MySQL database, 'invitrodb'. The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but 'tcpl' can be used to support diverse chemical screening efforts.
Maintained by Jason Brown. Last updated 3 days ago.
14.7 match 36 stars 9.41 score 90 scriptsstan-dev
rstan:R Interface to Stan
User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Maintained by Ben Goodrich. Last updated 1 days ago.
bayesian-data-analysisbayesian-inferencebayesian-statisticsmcmcstancpp
7.3 match 1.1k stars 18.67 score 14k scripts 279 dependentssparklyr
sparklyr:R Interface to Apache Spark
R interface to Apache Spark, a fast and general engine for big data processing, see <https://spark.apache.org/>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
Maintained by Edgar Ruiz. Last updated 10 days ago.
apache-sparkdistributeddplyridelivymachine-learningremote-clusterssparksparklyr
9.0 match 959 stars 15.16 score 4.0k scripts 21 dependentswviechtb
metafor:Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Maintained by Wolfgang Viechtbauer. Last updated 1 days ago.
meta-analysismixed-effectsmultilevel-modelsmultivariate
8.4 match 246 stars 16.30 score 4.9k scripts 92 dependentsjqveenstra
arfima:Fractional ARIMA (and Other Long Memory) Time Series Modeling
Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used.
Maintained by JQ Veenstra. Last updated 1 years ago.
25.6 match 14 stars 5.31 score 81 scripts 1 dependentsr-lib
generics:Common S3 Generics not Provided by Base R Methods Related to Model Fitting
In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics.
Maintained by Hadley Wickham. Last updated 1 years ago.
9.7 match 61 stars 14.00 score 131 scripts 9.8k dependentscitoverse
cito:Building and Training Neural Networks
The 'cito' package provides a user-friendly interface for training and interpreting deep neural networks (DNN). 'cito' simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems. DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, 'cito' has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. 'cito' optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, 'cito' is computationally efficient because it is based on the deep learning framework 'torch'. The 'torch' package is native to R, so no Python installation or other API is required for this package.
Maintained by Maximilian Pichler. Last updated 2 months ago.
machine-learningneural-network
14.9 match 42 stars 9.07 score 129 scripts 1 dependentswudongjie
em:Generic EM Algorithm
A generic function for running the Expectation-Maximization (EM) algorithm within a maximum likelihood framework, based on Dempster, Laird, and Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> is implemented. It can be applied after a model fitting using R's existing functions and packages. The research leading to the software described here has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 851293).
Maintained by Dongjie Wu. Last updated 2 years ago.
27.0 match 8 stars 4.98 score 24 scriptsinsightsengineering
tern:Create Common TLGs Used in Clinical Trials
Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.
Maintained by Joe Zhu. Last updated 2 months ago.
clinical-trialsgraphslistingsnestoutputstables
10.6 match 79 stars 12.62 score 186 scripts 9 dependentsoliviergimenez
R2ucare:Goodness-of-Fit Tests for Capture-Recapture Models
Performs goodness-of-fit tests for capture-recapture models as described by Gimenez et al. (2018) <doi:10.1111/2041-210X.13014>. Also contains several functions to process capture-recapture data.
Maintained by Olivier Gimenez. Last updated 3 years ago.
arnason-schwarz-modelscapture-recapture-modelscormack-jolly-sebergoodness-of-fit
21.9 match 6 stars 6.10 score 104 scriptsashesitr
reservr:Fit Distributions and Neural Networks to Censored and Truncated Data
Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) <doi:10.1007/s13385-022-00314-4>.
Maintained by Alexander Rosenstock. Last updated 9 months ago.
24.9 match 5 stars 5.35 score 9 scriptsgiopogg
webSDM:Including Known Interactions in Species Distribution Models
A collection of tools to fit and work with trophic Species Distribution Models. Trophic Species Distribution Models combine knowledge of trophic interactions with Bayesian structural equation models that model each species as a function of its prey (or predators) and environmental conditions. It exploits the topological ordering of the known trophic interaction network to predict species distribution in space and/or time, where the prey (or predator) distribution is unavailable. The method implemented by the package is described in Poggiato, Andréoletti, Pollock and Thuiller (2022) <doi:10.22541/au.166853394.45823739/v1>.
Maintained by Giovanni Poggiato. Last updated 9 months ago.
22.9 match 17 stars 5.71 score 9 scriptsum-kevinhe
pprof:Modeling, Standardization and Testing for Provider Profiling
Implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.
Maintained by Xiaohan Liu. Last updated 5 days ago.
32.0 match 4.08 score 3 scriptsrobjhyndman
forecast:Forecasting Functions for Time Series and Linear Models
Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
Maintained by Rob Hyndman. Last updated 7 months ago.
forecastforecastingopenblascpp
7.0 match 1.1k stars 18.63 score 16k scripts 239 dependentstrevorhastie
gam:Generalized Additive Models
Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).
Maintained by Trevor Hastie. Last updated 6 months ago.
13.7 match 4 stars 9.50 score 2.2k scripts 61 dependentsbyoungman
evgam:Generalised Additive Extreme Value Models
Methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. For details of distributions see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>. Details of how evgam works and various examples are given in Youngman, B.D. (2022) <doi:10.18637/jss.v103.i03>.
Maintained by Ben Youngman. Last updated 3 days ago.
15.4 match 6 stars 8.43 score 82 scripts 12 dependentsphiala
ecodist:Dissimilarity-Based Functions for Ecological Analysis
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.
Maintained by Sarah Goslee. Last updated 1 years ago.
13.1 match 9 stars 9.84 score 566 scripts 9 dependentsbioc
autonomics:Unified Statistical Modeling of Omics Data
This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.
Maintained by Aditya Bhagwat. Last updated 2 months ago.
softwaredataimportpreprocessingdimensionreductionprincipalcomponentregressiondifferentialexpressiongenesetenrichmenttranscriptomicstranscriptiongeneexpressionrnaseqmicroarrayproteomicsmetabolomicsmassspectrometry
21.6 match 5.95 score 5 scriptsglotaran
TIMP:Fitting Separable Nonlinear Models in Spectroscopy and Microscopy
A problem solving environment (PSE) for fitting separable nonlinear models to measurements arising in physics and chemistry experiments, as described by Mullen & van Stokkum (2007) <doi:10.18637/jss.v018.i03> for its use in fitting time resolved spectroscopy data, and as described by Laptenok et al. (2007) <doi:10.18637/jss.v018.i08> for its use in fitting Fluorescence Lifetime Imaging Microscopy (FLIM) data, in the study of Förster Resonance Energy Transfer (FRET). `TIMP` also serves as the computation backend for the `GloTarAn` software, a graphical user interface for the package, as described in Snellenburg et al. (2012) <doi:10.18637/jss.v049.i03>.
Maintained by Joris Snellenburg. Last updated 2 years ago.
26.6 match 3 stars 4.80 score 14 scripts 1 dependentspbreheny
grpreg:Regularization Paths for Regression Models with Grouped Covariates
Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. For more information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>, Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015) <doi:10.1111/biom.12300>, or visit the package homepage <https://pbreheny.github.io/grpreg/>.
Maintained by Patrick Breheny. Last updated 17 days ago.
11.2 match 34 stars 11.38 score 192 scripts 34 dependentssthomas522
hmclearn:Fit Statistical Models Using Hamiltonian Monte Carlo
Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models. All of the code is written in R. Theoretical references are listed below:. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" <arXiv:1701.02434>, Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>, Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.
Maintained by Samuel Thomas. Last updated 4 years ago.
22.6 match 11 stars 5.64 score 16 scriptsmarco-geraci
Qtools:Utilities for Quantiles
Functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, directional quantile classification, and quantile ratio regression. A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Maintained by Marco Geraci. Last updated 1 years ago.
30.9 match 4.10 score 33 scripts 2 dependentscdriveraus
ctsem:Continuous Time Structural Equation Modelling
Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . Stan based functions are not available on 32 bit Windows systems at present. <https://cdriver.netlify.app/> contains some tutorial blog posts.
Maintained by Charles Driver. Last updated 11 days ago.
stochastic-differential-equationstime-seriescpp
13.2 match 42 stars 9.58 score 366 scripts 1 dependents