|
r-rdsg
|
public |
A set of handy functions for Notre Dame Data Science Group
|
2025-03-25 |
|
mrbayes
|
public |
Bayesian Inference of Phylogeny
|
2025-03-25 |
|
qqman
|
public |
Draws Manhattan plot and QQ plot using plink assoc output.
|
2025-03-25 |
|
r-tda
|
public |
Tools for the statistical analysis of persistent homology and for density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries 'GUDHI' <http://gudhi.gforge.inria.fr/>, 'Dionysus' <http://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128> for analyzing the statistical significance of persistent homology features.
|
2025-03-25 |
|
nbresuse
|
public |
Simple Jupyter extension to show how much resources (RAM) your notebook is using
|
2025-03-25 |
|
r-pma
|
public |
Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in the following papers: (1) Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data. Statistical Applications in Genetics and Molecular Biology 8(1): Article 28.
|
2025-03-25 |
|
r-rafalib
|
public |
A series of shortcuts for routine tasks originally developed by Rafael A. Irizarry to facilitate data exploration.
|
2025-03-25 |
|
r-cloudml
|
public |
Interface to the Google Cloud Machine Learning Platform <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine learning models.
|
2025-03-25 |
|
hg-git
|
public |
Push and pull from git repositories using mercurial.
|
2025-03-25 |
|
r-hawkes
|
public |
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
|
2025-03-25 |
|
jupyterlab-slurm
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-latex
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab_katex-extension
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-flake8
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-git
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab_geojson-extension
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-github
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab_fasta-extension
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-toc
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-vim
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-variableinspector
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-hub
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab_html
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab-google-drive
|
public |
No Summary
|
2025-03-25 |
|
jupyterlab_bokeh
|
public |
No Summary
|
2025-03-25 |
