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openjpeg
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An open-source JPEG 2000 codec written in C
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2025-09-21 |
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r-srs
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Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.
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2025-09-21 |
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r-poissonreg
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Bindings for Poisson regression models for use with the 'parsnip' package. Models include simple generalized linear models, Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber, and Jackman (2008) <doi:10.18637/jss.v027.i08>).
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2025-09-21 |
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r-doc2vec
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Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms, namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at <arXiv:1405.4053>. The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering. It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps these document embeddings to a lower-dimensional space using the 'Uniform Manifold Approximation and Projection' (UMAP) clustering algorithm and finds dense areas in that space using a 'Hierarchical Density-Based Clustering' technique (HDBSCAN). These dense areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can be found which are representative of the topic. More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at <arXiv:2008.09470>.
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2025-09-21 |
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r-maldirppa
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Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data.
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2025-09-21 |
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r-mbsts
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Multivariate time series regression using dynamic linear models fit by MCMC. See Qiu, Jammalamadaka and Ning (2018) <http://www.jmlr.org/papers/volume19/18-009/18-009.pdf>.
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2025-09-21 |
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r-ggchangepoint
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R provides fantastic tools for changepoint analysis, but plots generated by the tools do not have the 'ggplot2' style. This tool, however, combines 'changepoint', 'changepoint.np' and 'ecp' together, and uses 'ggplot2' to visualize changepoints.
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2025-09-21 |
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r-dirichletprocess
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Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.
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2025-09-21 |
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r-bsvars
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Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>.
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2025-09-21 |
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r-wordcloud2
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A fast visualization tool for creating wordcloud by using 'wordcloud2.js'. 'wordcloud2.js' is a JavaScript library to create wordle presentation on 2D canvas or HTML <https://timdream.org/wordcloud2.js/>.
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2025-09-21 |
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r-rlabkey
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The 'LabKey' client library for R makes it easy for R users to load live data from a 'LabKey' Server, <http://www.labkey.com/>, into the R environment for analysis, provided users have permissions to read the data. It also enables R users to insert, update, and delete records stored on a 'LabKey' Server, provided they have appropriate permissions to do so.
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2025-09-21 |
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r-checkhelper
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Deal with packages 'check' outputs and reduce the risk of rejection by 'CRAN' by following policies.
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2025-09-21 |
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r-didimputation
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Estimates Two-way Fixed Effects difference-in-differences/event-study models using the imputation-based approach proposed by Borusyak, Jaravel, and Spiess (2021).
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2025-09-21 |
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r-denoiser
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Estimate a low rank matrix from noisy data using singular values thresholding and shrinking functions. Impute missing values with matrix completion. The method is described in <arXiv:1602.01206>.
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2025-09-21 |
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r-reffreeewas
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Reference-free method for conducting EWAS while deconvoluting DNA methylation arising as mixtures of cell types. The older method (Houseman et al., 2014,<doi:10.1093/bioinformatics/btu029>) is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption. The newer method (Houseman et al., 2016, <doi:10.1186/s12859-016-1140-4>) is similar to non-negative matrix factorization, with additional constraints and additional utilities.
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2025-09-21 |
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r-fracdiff
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Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); including inference and basic methods. Some alternative algorithms to estimate "H".
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2025-09-21 |
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r-ddrtree
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Provides an implementation of the framework of reversed graph embedding (RGE) which
projects data into a reduced dimensional space while constructs a principal tree
which passes through the middle of the data simultaneously. DDRTree shows superiority
to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic
structure of the single cell genomics data. In general, it could be used to reconstruct
the temporal progression as well as bifurcation structure of any datatype.
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2025-09-21 |
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r-uroot
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Seasonal unit roots and seasonal stability tests. P-values based on response surface regressions are available for both tests. P-values based on bootstrap are available for seasonal unit root tests.
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2025-09-21 |
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r-wdi
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Search, extract and format data from the World Bank's World Development Indicators.
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2025-09-21 |
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open-clip-torch
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OpenCLIP
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2025-09-21 |
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r-cgdsr
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Provides a basic set of R functions for querying the Cancer Genomics Data Server (CGDS), hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC) at <www.cbioportal.org>.
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2025-09-21 |
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r-zoib
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Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
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2025-09-21 |
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r-thankr
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Find out who maintains the packages you use in your current session or in your package library and maybe say 'thank you'.
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2025-09-21 |
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r-osd
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Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.
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2025-09-21 |
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r-base64
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Compatibility wrapper to replace the orphaned package by Romain Francois. New applications should use the 'openssl' or 'base64enc' package instead.
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2025-09-21 |
