|
r-tidytree
|
public |
Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.
|
2025-09-23 |
|
r-listarrays
|
public |
A toolbox for R arrays. Flexibly split, bind, reshape, modify, subset and name arrays.
|
2025-09-23 |
|
r-iheatmapr
|
public |
Make complex, interactive heatmaps. 'iheatmapr' includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.
|
2025-09-23 |
|
r-robustrankaggreg
|
public |
Methods for aggregating ranked lists, especially lists of genes. It implements the Robust Rank Aggregation (Kolde et. al in preparation) and some other simple algorithms for the task. RRA method uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking.
|
2025-09-23 |
|
fedfred
|
public |
A feature-rich python package for interacting with the Federal Reserve Bank of St. Louis Economic Database (FRED)
|
2025-09-23 |
|
cherry-picker
|
public |
Backport CPython changes from main to maintenance branches
|
2025-09-23 |
|
r-purrrlyr
|
public |
Some functions at the intersection of 'dplyr' and 'purrr' that formerly lived in 'purrr'.
|
2025-09-23 |
|
r-primme
|
public |
R interface to PRIMME, a C library for computing a few eigenvalues and their corresponding eigenvectors of a real symmetric or complex Hermitian matrix. It can also compute singular values and vectors of a square or rectangular matrix. It can find largest, smallest, or interior singular/eigenvalues and can use preconditioning to accelerate convergence.
|
2025-09-23 |
|
r-rocit
|
public |
Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.
|
2025-09-22 |
|
r-naniar
|
public |
Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data.
|
2025-09-22 |
|
r-mcl
|
public |
Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.
|
2025-09-22 |
|
r-sparktf
|
public |
A 'sparklyr' extension that enables reading and writing 'TensorFlow' TFRecord files via 'Apache Spark'.
|
2025-09-22 |
|
r-fastverse
|
public |
Easy installation, loading and management, of a complementary set of high-performance packages for statistical computing and data manipulation. The core 'fastverse' consists of 6 packages: 'data.table', 'collapse', 'matrixStats', 'kit', 'magrittr' and 'fst', that jointly only depend on 'Rcpp'. These packages are attached and harmonized through the 'fastverse'. In addition, the 'fastverse' can be freely and permanently extended with additional packages, both globally or for individual projects. Entirely separate package verses can also be created. Selected fast and low-dependency packages are suggested for various topics such as time series, dates and times, strings, spatial data, statistics and data serialization (see GitHub / website).
|
2025-09-22 |
|
r-timeroc
|
public |
Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed.
|
2025-09-22 |
|
r-ascii
|
public |
Coerce R object to asciidoc, txt2tags, restructuredText, org, textile or pandoc syntax. Package comes with a set of drivers for Sweave.
|
2025-09-22 |
|
r-mclustcomp
|
public |
Given a set of data points, a clustering is defined as a disjoint partition where each pair of sets in a partition has no overlapping elements. This package provides 25 methods that play a role somewhat similar to distance or metric that measures similarity of two clusterings - or partitions. For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.
|
2025-09-22 |
|
r-purrrogress
|
public |
Provides functions to easily add progress bars to apply calls.
|
2025-09-22 |
|
r-ggrasp
|
public |
Given a group of genomes and their relationship with each other, the package clusters the genomes and selects the most representative members of each cluster. Additional data can be provided to the prioritize certain genomes. The results can be printed out as a list or a new phylogeny with graphs of the trees and distance distributions also available. For detailed introduction see: Thomas H Clarke, Lauren M Brinkac, Granger Sutton, and Derrick E Fouts (2018), GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models, Bioinformatics, bty300, <doi:10.1093/bioinformatics/bty300>.
|
2025-09-22 |
|
r-argumentcheck
|
public |
The typical process of checking arguments in functions is iterative. In this process, an error may be returned and the user may fix it only to receive another error on a different argument. 'ArgumentCheck' facilitates a more helpful way to perform argument checks allowing the programmer to run all of the checks and then return all of the errors and warnings in a single message.
|
2025-09-22 |
|
r-shinyeffects
|
public |
Add fancy CSS effects to your 'shinydashboards' or 'shiny' apps. 100% compatible with 'shinydashboardPlus' and 'bs4Dash'.
|
2025-09-22 |
|
r-docxtractr
|
public |
'Microsoft Word' 'docx' files provide an 'XML' structure that is fairly straightforward to navigate, especially when it applies to 'Word' tables and comments. Tools are provided to determine table count/structure, comment count and also to extract/clean tables and comments from 'Microsoft Word' 'docx' documents. There is also nascent support for '.doc' and '.pptx' files.
|
2025-09-22 |
|
mkdocs-macros-plugin
|
public |
Unleash the power of MkDocs with macros and variables
|
2025-09-22 |
|
aiohttp-client-cache
|
public |
Persistent cache for aiohttp requests
|
2025-09-22 |
|
pyiceberg
|
public |
Apache Iceberg is an open table format for huge analytic datasets
|
2025-09-22 |
|
r-logicreg
|
public |
Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) <DOI:10.1002/gepi.20042>.
|
2025-09-22 |
