Package: lddmm 0.4.2

Giorgio Paulon

lddmm: Longitudinal Drift-Diffusion Mixed Models (LDDMM)

Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <doi:10.1080/01621459.2020.1801448>.

Authors:Giorgio Paulon [aut, cre], Abhra Sarkar [aut, ctb]

lddmm_0.4.2.tar.gz
lddmm_0.4.2.tar.gz(r-4.5-noble)lddmm_0.4.2.tar.gz(r-4.4-noble)
lddmm_0.4.2.tgz(r-4.4-emscripten)lddmm_0.4.2.tgz(r-4.3-emscripten)
lddmm.pdf |lddmm.html
lddmm/json (API)

# Install 'lddmm' in R:
install.packages('lddmm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • data - Example dataset

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 4 scripts 186 downloads 13 exports 45 dependencies

Last updated 11 months agofrom:1f9ee65044. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-linux-x86_64OKNov 13 2024

Exports:B_basiscompute_WAICdataextract_post_drawsextract_post_meanH_ballLDDMMlog_likelihoodlog_likelihood_indP_smooth1plot_accuracyplot_post_parsplot_RT

Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtablegtoolsisobandlabelingLaplacesDemonlatex2explatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrpurrrR6RColorBrewerRcppRcppArmadilloRcppProgressreshape2rgenrlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

minimal_example

Rendered fromminimal_example.Rmdusingknitr::rmarkdownon Nov 13 2024.

Last update: 2024-01-18
Started: 2021-11-08