|
r-waterfalls
|
public |
A not uncommon task for quants is to create 'waterfall charts'. There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme.
|
2025-09-24 |
|
numexpr
|
public |
Fast numerical expression evaluator for NumPy.
|
2025-09-24 |
|
r-marmap
|
public |
Import xyz data from the NOAA (National Oceanic and Atmospheric Administration, <https://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans, <https://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
|
2025-09-24 |
|
r-tpea
|
public |
We described a novel Topology-based pathway enrichment analysis, which integrated the global position of the nodes and the topological property of the pathways in Kyoto Encyclopedia of Genes and Genomes Database. We also provide some functions to obtain the latest information about pathways to finish pathway enrichment analysis using this method.
|
2025-09-24 |
|
r-graddescent
|
public |
An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. The variants of gradient descent algorithm are : Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load. Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically. Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average. Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning. Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning. Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning. Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning. RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability. Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning. Stochastic Variance Reduce Gradient (SVRG), which is an optimization SGD-based algorithm to accelerates the process toward converging by reducing the gradient. Semi Stochastic Gradient Descent (SSGD),which is a SGD-based algorithm that combine GD and SGD to accelerates the process toward converging by choosing one of the gradients at a time. Stochastic Recursive Gradient Algorithm (SARAH), which is an optimization algorithm similarly SVRG to accelerates the process toward converging by accumulated stochastic information. Stochastic Recursive Gradient Algorithm+ (SARAHPlus), which is a SARAH practical variant algorithm to accelerates the process toward converging provides a possibility of earlier termination.
|
2025-09-24 |
|
r-tfdatasets
|
public |
Interface to 'TensorFlow' Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See <https://www.tensorflow.org/programmers_guide/datasets> for additional details.
|
2025-09-24 |
|
r-pkgnet
|
public |
Tools from the domain of graph theory can be used to quantify the complexity and vulnerability to failure of a software package. That is the guiding philosophy of this package. 'pkgnet' provides tools to analyze the dependencies between functions in an R package and between its imported packages.
|
2025-09-24 |
|
aws-cdk.cloud-assembly-schema
|
public |
Cloud Assembly Schema
|
2025-09-24 |
|
r-piecewisesem
|
public |
Implements piecewise structural equation modeling from a single list of structural equations, with new methods for non-linear, latent, and composite variables, standardized coefficients, query-based prediction and indirect effects. See <http://jslefche.github.io/piecewiseSEM/> for more.
|
2025-09-24 |
|
r-subspace
|
public |
An interface to 'OpenSubspace', an open source framework for evaluation and exploration of subspace clustering algorithms in WEKA (see <http://dme.rwth-aachen.de/de/opensubspace> for more information). Also performs visualization.
|
2025-09-24 |
|
r-scorecard
|
public |
The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.
|
2025-09-24 |
|
r-tfisher
|
public |
We provide the cumulative distribution function (CDF), quantile, and statistical power calculator for a collection of thresholding Fisher's p-value combination methods, including Fisher's p-value combination method, truncated product method and, in particular, soft-thresholding Fisher's p-value combination method which is proven to be optimal in some context of signal detection. The p-value calculator for the omnibus version of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
|
2025-09-24 |
|
filepattern
|
public |
Python library to store and filter files with systematic naming conventions using simplified regular expression-like patterns
|
2025-09-24 |
|
r-tarchetypes
|
public |
Function-oriented Make-like declarative workflows for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible workflows concisely and compactly. The methods in this package were influenced by the 'drake' R package by Will Landau (2018) <doi:10.21105/joss.00550>.
|
2025-09-24 |
|
r-lsmeans
|
public |
Obtain least-squares means for linear, generalized linear, and mixed models. Compute contrasts or linear functions of least-squares means, and comparisons of slopes. Plots and compact letter displays. Least-squares means were proposed in Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers", Tech Report ARS-20-8, USDA National Agricultural Library, and discussed further in Searle, Speed, and Milliken (1980) "Population marginal means in the linear model: An alternative to least squares means", The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>. NOTE: lsmeans now relies primarily on code in the 'emmeans' package. 'lsmeans' will be archived in the near future.
|
2025-09-24 |
|
r-networktoolbox
|
public |
Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
|
2025-09-24 |
|
openai
|
public |
Python client library for the OpenAI API
|
2025-09-24 |
|
r-diversity
|
public |
Allows the calculation of both genetic diversity partition statistics, genetic differentiation statistics, and locus informativeness for ancestry assignment. It also provides users with various option to calculate bootstrapped 95\% confidence intervals both across loci, for pairwise population comparisons, and to plot these results interactively. Parallel computing capabilities and pairwise results without bootstrapping are provided. Also calculates F-statistics from Weir and Cockerham (1984). Various plotting features are provided, as well as Chi-square tests of genetic heterogeneity. Functionality for the calculation of various diversity parameters is possible for RAD-seq derived SNP data sets containing thousands of marker loci. A shiny application for the development of microsatellite multiplexes is also available.
|
2025-09-24 |
|
stglib
|
public |
Process data from a variety of oceanographic instrumentation
|
2025-09-24 |
|
vercel-cli
|
public |
Vercel's frontend cloud gives developers frameworks, workflows, and infrastructure to build a faster, more personalized web.
|
2025-09-24 |
|
r-coxme
|
public |
This package fits Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Rippatti and Palgren, Biometrics 2002.
|
2025-09-24 |
|
hepunits
|
public |
Units and constants in the HEP system of units
|
2025-09-24 |
|
sourmash-minimal
|
public |
Compute and compare MinHash signatures for DNA data sets.
|
2025-09-24 |
|
r-policytree
|
public |
Learn optimal policies via doubly robust empirical welfare maximization over trees. This package implements the multi-action doubly robust approach of Zhou, Athey and Wager (2018) <arXiv:1810.04778> in the case where we want to learn policies that belong to the class of depth k decision trees.
|
2025-09-24 |
|
r-dnapath
|
public |
Integrates pathway information into the differential network analysis of two gene expression datasets as described in Grimes, Potter, and Datta (2019) <doi:10.1038/s41598-019-41918-3>. Provides summary functions to break down the results at the pathway, gene, or individual connection level. The differential networks for each pathway of interest can be plotted, and the visualization will highlight any differentially expressed genes and all of the gene-gene associations that are significantly differentially connected.
|
2025-09-24 |
