Package: ggdmc 0.2.8.1

ggdmc: Cognitive Models

The package provides tools to fit the LBA, DDM, PM and 2-D diffusion models, using the population-based Markov Chain Monte Carlo.

Authors:Yi-Shin Lin [aut, cre], Andrew Heathcote [aut]

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ggdmc.pdf |ggdmc.html✨
ggdmc/json (API)

# Install 'ggdmc' in R:

install.packages('ggdmc', repos = c('https://yxlin.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yxlin/ggdmc/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

49 exports 19 stars 2.06 score 41 dependencies 24 scripts 271 downloads

Last updated 1 months agofrom:de30bc2b1e. Checks:OK: 1 WARNING: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 12 2024
R-4.5-win-x86_64	WARNING	Sep 12 2024
R-4.5-linux-x86_64	WARNING	Sep 12 2024
R-4.4-win-x86_64	WARNING	Sep 12 2024
R-4.4-mac-x86_64	WARNING	Sep 12 2024
R-4.4-mac-aarch64	WARNING	Sep 12 2024
R-4.3-win-x86_64	WARNING	Sep 12 2024
R-4.3-mac-x86_64	WARNING	Sep 12 2024
R-4.3-mac-aarch64	WARNING	Sep 12 2024

Exports:ac autocorr BuildDMI BuildModel BuildPrior check_pvec dbeta_lu dcauchy_l dcircle dcircle300 dconstant deviance_model dgamma_l DIC dlnorm_l dtnorm dvonmises effectiveSize gelman get_os GetNsim GetParameterMatrix GetPNames iseffective isflat ismixed isstuck likelihood logLik PickStuck plot print ptnorm pvonmises r1d random rcircle rcircle_process rlba_norm rprior rtnorm run rvonmises simulate StartNewsamples summary TableParameters trial_loglik_hier unstick_one

Dependencies:abind backports checkmate cli colorspace data.table distributional fansi farver generics ggplot2 glue gtable isoband labeling lattice lifecycle loo magrittr MASS Matrix matrixStats mgcv munsell nlme numDeriv pillar pkgconfig posterior R6 RColorBrewer Rcpp RcppArmadillo rlang scales tensorA tibble utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculate the autocorrelation of a vector	ac
Autocorrelation Plot	autocorr
Bind data and models	BuildDMI
Create a model object	BuildModel
Specifying Prior Distributions	BuildPrior
Does a model object specify a correct p.vector	check_pvec
Convergence Diagnosis	CheckConverged isflat isflat,list-method isflat,posterior-method ismixed ismixed,posterior-method isstuck isstuck,hyper-method isstuck,list-method isstuck,posterior-method PickStuck PickStuck,hyper-method PickStuck,list-method PickStuck,posterior-method
A modified dbeta function	dbeta_lu
A modified dcauchy functions	dcauchy_l
Two-dimension Diffusion Model	dcircle dcircle300 r1d rcircle rcircle_process
A pseudo constant function to get constant densities	dconstant
Calculate the statistics of model complexity	deviance_model
A modified dgamma function	dgamma_l
Deviance Information Criteria	DIC DIC,hyper-method DIC,list-method DIC,posterior-method
A modified dlnorm functions	dlnorm_l
An S4 class of the Data-model Instance	dmi-class
Truncated Normal Distribution	dtnorm ptnorm rtnorm
Effective Sample Size	effectiveSize effectiveSize,hyper-method effectiveSize,list-method effectiveSize,posterior-method
Potential scale reduction factor	gelman gelman,hyper-method gelman,list-method gelman,posterior-method
Retrieve information of operating system	get_os
Get a n-cell matrix	GetNsim
Constructs a ns x npar matrix,	GetParameterMatrix
Extract parameter names from a model object	GetPNames
Cognitive Multilevel Models	ggdmc-package ggdmc
An S4 class to represent an object storing posterior samples at the participant and hyper level	hyper-class
Model checking functions	iseffective
Calculate log likelihoods	likelihood
Extract Posterior Log-Likelihood	logLik logLik,hyper-method logLik,list-method logLik,posterior-method
An S4 class of the process model.	model-class
The Parameter Names in a Prior Object	names,prior-method
ggdmc Plotting Methods	plot plot,hyper-method plot,list-method plot,posterior-method plot,prior-method
An S4 class to represent an object storing posterior samples at the participant level. Posterior samples storing both the participant and the hyper lever are represented by an S4 class hyper	posterior-class
ggdmc Printing Methods	print print,model-method print,prior-method
An S4 class to represent an object storing prior distributions	prior-class
Random number generation	random
Generate Random Deviates of the LBA Distribution	rlba_norm
Generate Random Numbers	rprior rprior,prior-method
Generate random deviates from a von Mises distribution	dvonmises pvonmises rvonmises
Simulate Choice Responses	simulate,model-method
Start new model fits	run StartNewsamples
ggdmc Summary Methods	summary summary,hyper-method summary,list-method summary,posterior-method
Table response and parameter	TableParameters
Extract trial log likelihoods	trial_loglik_hier
Unstick posterios samples (One subject)	unstick_one