Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 5270 packages in 0.02 seconds

nlme — by R-core, a month ago

Linear and Nonlinear Mixed Effects Models

Fit and compare Gaussian linear and nonlinear mixed-effects models.

lme4 — by Ben Bolker, 2 months ago

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".

MCMCglmm — by Jarrod Hadfield, 14 days ago

MCMC Generalised Linear Mixed Models

Fits Multi-response Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques.

broom.mixed — by Ben Bolker, 8 months ago

Tidying Methods for Mixed Models

Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.

gamm4 — by Simon Wood, 10 months ago

Generalized Additive Mixed Models using 'mgcv' and 'lme4'

Estimate generalized additive mixed models via a version of function gamm() from 'mgcv', using 'lme4' for estimation.

pbkrtest — by Søren Højsgaard, a month ago

Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models

Test in mixed effects models. Attention is on mixed effects models as implemented in the 'lme4' package. For linear mixed models, this package implements (1) a parametric bootstrap test, (2) a Kenward-Roger-typ modification of F-tests for linear mixed effects models and (3) a Satterthwaite-type modification of F-tests for linear mixed effects models. The package also implements a parametric bootstrap test for generalized linear mixed models. The facilities of the package are documented in the paper by Halehoh and Højsgaard, (2012, ). Please see 'citation("pbkrtest")' for citation details.

glmmTMB — by Mollie Brooks, 7 months ago

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.

mgcv — by Simon Wood, 5 months ago

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) for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.

GLMMadaptive — by Dimitris Rizopoulos, 3 months ago

Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) .

sommer — by Giovanny Covarrubias-Pazaran, 15 days ago

Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects and unknown variance-covariance structures (i.e. heterogeneous and unstructured variance models) (Covarrubias-Pazaran, 2016 ; Maier et al., 2015 ). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) and dense known covariance structures for levels of random effects. Spatial models can also be fitted using i.e. the two-dimensional spline functionality available in sommer.