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r-shinipsum
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public |
Prototype your 'shiny' apps quickly with these Lorem-Ipsum helper functions. Generate random elements for 'shiny' outputs that can be used as placeholder in your application.
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2025-09-24 |
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r-spscomps
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public |
The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.
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2025-09-24 |
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r-howmany
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public |
When testing multiple hypotheses simultaneously, this package provides functionality to calculate a lower bound for the number of correct rejections (as a function of the number of rejected hypotheses), which holds simultaneously -with high probability- for all possible number of rejections. As a special case, a lower bound for the total number of false null hypotheses can be inferred. Dependent test statistics can be handled for multiple tests of associations. For independent test statistics, it is sufficient to provide a list of p-values.
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2025-09-24 |
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r-canopy
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public |
A statistical framework and computational procedure for identifying the sub-populations within a tumor, determining the mutation profiles of each subpopulation, and inferring the tumor's phylogenetic history. The input are variant allele frequencies (VAFs) of somatic single nucleotide alterations (SNAs) along with allele-specific coverage ratios between the tumor and matched normal sample for somatic copy number alterations (CNAs). These quantities can be directly taken from the output of existing software. Canopy provides a general mathematical framework for pooling data across samples and sites to infer the underlying parameters. For SNAs that fall within CNA regions, Canopy infers their temporal ordering and resolves their phase. When there are multiple evolutionary configurations consistent with the data, Canopy outputs all configurations along with their confidence assessment.
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2025-09-24 |
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r-mikropml
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public |
User-Friendly R Package for Supervised Machine Learning Pipelines
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2025-09-24 |
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r-arules
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public |
Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. See Christian Borgelt (2012) <doi:10.1002/widm.1074>.
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2025-09-24 |
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r-fftrees
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public |
Create, visualize, and test fast-and-frugal decision trees (FFTs). FFTs are very simple decision trees for binary classification problems. FFTs can be preferable to more complex algorithms because they are easy to communicate, require very little information, and are robust against overfitting.
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2025-09-24 |
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cashocs
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public |
Computational Adjoint-Based Shape Optimization and Optimal Control Software
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2025-09-24 |
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metadata-crawler
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public |
Crawl, extract and push climate metadata for indexing.
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2025-09-24 |
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onnxscript
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public |
Naturally author ONNX functions and models using a subset of Python
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2025-09-24 |
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git-crypt
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public |
Transparent file encryption in git
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2025-09-24 |
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galaxy-tool-util
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public |
Galaxy Tool and Tool Dependency Utilities
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2025-09-24 |
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galaxy-tool-util-models
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public |
Pydantic models for Galaxy tools
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2025-09-24 |
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r-rstackdeque
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public |
Provides fast, persistent (side-effect-free) stack, queue and deque (double-ended-queue) data structures. While deques include a superset of functionality provided by queues, in these implementations queues are more efficient in some specialized situations. See the documentation for rstack, rdeque, and rpqueue for details.
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2025-09-24 |
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r-cooccur
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public |
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.
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2025-09-24 |
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google-api-python-client
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public |
Google API Client Library for Python
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2025-09-24 |
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r-tripack
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public |
A constrained two-dimensional Delaunay triangulation package providing both triangulation and generation of voronoi mosaics of irregular spaced data.
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2025-09-24 |
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r-tkrgl
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public |
TK widget tools for the rgl package have been moved to rgl.
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2025-09-24 |
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r-rflptools
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public |
Provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).
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2025-09-24 |
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r-spatstat.linnet
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public |
This is a subset of the 'spatstat' package, containing its functionality for spatial data on a linear network.
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2025-09-24 |
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r-mertools
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public |
Provides methods for extracting results from mixed-effect model objects fit with the 'lme4' package. Allows construction of prediction intervals efficiently from large scale linear and generalized linear mixed-effects models.
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2025-09-24 |
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r-dagwood
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public |
DAGs With Omitted Objects Displayed (DAGWOOD) is a framework to help reveal key hidden assumptions in a causal DAG. This package provides an implementation of the DAGWOOD algorithm. Further description can be found in Haber et al (2022) <DOI:10.1016/j.annepidem.2022.01.001>.
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2025-09-24 |
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r-spiralize
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public |
It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.
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2025-09-24 |
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r-tmle
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public |
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
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2025-09-24 |
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r-autoplotly
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public |
Functionalities to automatically generate interactive visualizations for popular statistical results supported by 'ggfortify', such as time series, PCA, clustering and survival analysis, with 'plotly.js' <https://plot.ly/> and 'ggplot2' style. The generated visualizations can also be easily extended using 'ggplot2' and 'plotly' syntax while staying interactive.
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2025-09-24 |
