Showing 12 of total 12 results (show query)
paul-buerkner
brms:Bayesian Regression Models using 'Stan'
Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bürkner. Last updated 4 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
1.3k stars 16.64 score 13k scripts 35 dependents
alexpghayes
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 7 months ago.
102 stars 11.35 score 118 scripts 7 dependents
pbs-assess
sdmTMB:Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'
Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.
Maintained by Sean C. Anderson. Last updated 2 days ago.
ecologyglmmspatial-analysisspecies-distribution-modellingtmbcpp
205 stars 11.04 score 848 scripts 1 dependents
r-forge
distr:Object Oriented Implementation of Distributions
S4-classes and methods for distributions.
Maintained by Peter Ruckdeschel. Last updated 2 months ago.
8.77 score 327 scripts 32 dependents
gavinsimpson
coenocliner:Coenocline Simulation
Simulate species occurrence and abundances (counts) along gradients.
Maintained by Gavin L. Simpson. Last updated 4 years ago.
12 stars 6.03 score 15 scripts 1 dependents
f-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 10 months ago.
4.94 score 208 scripts 5 dependents
loicym
multibreakeR:Tests for a Structural Change in Multivariate Time Series
Flexible implementation of a structural change point detection algorithm for multivariate time series. It authorizes inclusion of trends, exogenous variables, and break test on the intercept or on the full vector autoregression system. Bai, Lumsdaine, and Stock (1998) <doi:10.1111/1467-937X.00051>.
Maintained by Loic Marechal. Last updated 2 years ago.
3.70 score 3 scripts
stla
boodist:Some Distributions from the 'Boost' Library and More
Make some distributions from the 'C++' library 'Boost' available in 'R'. In addition, the normal-inverse Gaussian distribution and the generalized inverse Gaussian distribution are provided. The distributions are represented by 'R6' classes. The method to sample from the generalized inverse Gaussian distribution is the one given in "Random variate generation for the generalized inverse Gaussian distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.
Maintained by Stéphane Laurent. Last updated 1 years ago.
3.04 score 22 scripts
jahmadkhan
DELTD:Kernel Density Estimation using Lifetime Distributions
A collection of asymmetrical kernels belong to lifetime distributions for kernel density estimation is presented. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000a,b), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>.
Maintained by Javaria Ahmad Khan. Last updated 3 years ago.
1.48 score 1 dependents
bmahieuoniris
MBAnalysis:Multiblock Exploratory and Predictive Data Analysis
Exploratory and predictive methods for the analysis of several blocks of variables measured on the same individuals.
Maintained by Benjamin Mahieu. Last updated 1 years ago.
1.00 score 1 scripts
cran
OPDOE:Optimal Design of Experiments
Several function related to Experimental Design are implemented here, see "Optimal Experimental Design with R" by Rasch D. et. al (ISBN 9781439816974).
Maintained by Albrecht Gebhardt. Last updated 7 years ago.
1.00 score