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    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-ediblecity 0.2.2
Propagated dependencies: r-stars@0.6-8 r-sf@1.0-23 r-rlang@1.1.6 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/icra/ediblecity
Licenses: Expat
Build system: r
Synopsis: Modeling Urban Agriculture at City Scale
Description:

The purpose of this package is to estimate the potential of urban agriculture to contribute to addressing several urban challenges at the city-scale. Within this aim, we selected 8 indicators directly related to one or several urban challenges. Also, a function is provided to compute new scenarios of urban agriculture. Methods are described by Pueyo-Ros, Comas & Corominas (2023) <doi:10.12688/openreseurope.16054.1>.

r-ecohydmod 1.0.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=Ecohydmod
Licenses: GPL 2
Build system: r
Synopsis: Ecohydrological Modelling
Description:

Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.

r-emissv 0.666.0.0
Propagated dependencies: r-units@1.0-0 r-sf@1.0-23 r-raster@3.6-32 r-ncdf4@1.24 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://atmoschem.github.io/EmissV/
Licenses: Expat
Build system: r
Synopsis: Tools for Create Emissions for Air Quality Models
Description:

Processing tools to create emissions for use in numerical air quality models. Emissions can be calculated both using emission factors and activity data (Schuch et al 2018) <doi:10.21105/joss.00662> or using pollutant inventories (Schuch et al., 2018) <doi:10.30564/jasr.v1i1.347>. Functions to process individual point emissions, line emissions and area emissions of pollutants are available as well as methods to incorporate alternative data for Spatial distribution of emissions such as satellite images (Gavidia-Calderon et. al, 2018) <doi:10.1016/j.atmosenv.2018.09.026> or openstreetmap data (Andrade et al, 2015) <doi:10.3389/fenvs.2015.00009>.

r-ebcobart 1.1.2
Propagated dependencies: r-univariateml@1.5.0 r-posterior@1.6.1 r-loo@2.8.0 r-extradistr@1.10.0 r-dbarts@0.9-32
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/JeroenGoedhart/EBcoBART
Licenses: GPL 3+
Build system: r
Synopsis: Co-Data Learning for Bayesian Additive Regression Trees
Description:

Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the dbarts R package. See Goedhart and others (2023) <doi:10.1002/sim.70004> for details.

r-elmr 1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ELMR
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Extreme Machine Learning (ELM)
Description:

Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.

r-emgcr 0.2.0
Propagated dependencies: r-tibble@3.3.0 r-survival@3.8-3 r-knitr@1.50 r-ggplot2@4.0.1 r-formula@1.2-5 r-flexsurv@2.3.2 r-actuar@3.3-6
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EMGCR
Licenses: GPL 3
Build system: r
Synopsis: Fit a Mixture Cure Rate Model with Custom Link Function
Description:

This package provides tools to fit Mixture Cure Rate models via the Expectation-Maximization (EM) algorithm, allowing for flexible link functions in the cure component and various survival distributions in the latency part. The package supports user-specified link functions, includes methods for parameter estimation and model diagnostics, and provides residual analysis tailored for cure models. The classical theory methods used are described in Berkson, J. and Gage, R. P. (1952) <doi:10.2307/2281318>, Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) <https://www.jstor.org/stable/2984875>, Bazán, J., Torres-Avilés, F., Suzuki, A. and Louzada, F. (2017)<doi:10.1002/asmb.2215>.

r-editrules 2.9.6
Propagated dependencies: r-lpsolveapi@5.5.2.0-17.14 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/data-cleaning/editrules
Licenses: GPL 3
Build system: r
Synopsis: Parsing, Applying, and Manipulating Data Cleaning Rules
Description:

Please note: active development has moved to packages validate and errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the igraph package.

r-ernm 1.0.4
Propagated dependencies: r-trust@0.1-8 r-tidyr@1.3.1 r-rlang@1.1.6 r-rcpp@1.1.0 r-network@1.19.0 r-moments@0.14.1 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ernm
Licenses: LGPL 2.1
Build system: r
Synopsis: Exponential-Family Random Network Models
Description:

Estimation of fully and partially observed Exponential-Family Random Network Models (ERNM). Exponential-family Random Graph Models (ERGM) and Gibbs Fields are special cases of ERNMs and can also be estimated with the package. Please cite Fellows and Handcock (2012), "Exponential-family Random Network Models" available at <doi:10.48550/arXiv.1208.0121>.

r-efdr 1.3
Propagated dependencies: r-waveslim@1.8.5 r-tidyr@1.3.1 r-sp@2.2-0 r-matrix@1.7-4 r-gstat@2.1-4 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-copula@1.1-7
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/andrewzm/EFDR/
Licenses: GPL 2+
Build system: r
Synopsis: Wavelet-Based Enhanced FDR for Detecting Signals from Complete or Incomplete Spatially Aggregated Data
Description:

Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies in an image. The image is first transformed into the wavelet domain in order to decorrelate any noise components, following which the coefficients at each resolution are standardised. Statistical tests (in a multiple hypothesis testing setting) are then carried out to find the anomalies. The power of EFDR exceeds that of standard FDR, which would carry out tests on every wavelet coefficient: EFDR choose which wavelets to test based on a criterion described in Shen et al. (2002). The package also provides elementary tools to interpolate spatially irregular data onto a grid of the required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N. Nonparametric hypothesis testing for a spatial signal. Journal of the American Statistical Association 97.460 (2002): 1122-1140.

r-earlyr 0.0.5
Propagated dependencies: r-ggplot2@4.0.1 r-epitrix@0.4.1 r-epiestim@2.2-5 r-distcrete@1.0.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://www.repidemicsconsortium.org/earlyR/
Licenses: Expat
Build system: r
Synopsis: Estimation of Transmissibility in the Early Stages of a Disease Outbreak
Description:

This package implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) <doi:10.1093/aje/kwt133>.

r-easyclimate 0.2.2
Propagated dependencies: r-terra@1.8-86 r-rcurl@1.98-1.17 r-r-utils@2.13.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/VeruGHub/easyclimate
Licenses: GPL 3+
Build system: r
Synopsis: Easy Access to High-Resolution Daily Climate Data for Europe
Description:

Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for points and polygons within Europe.

r-esreg 0.6.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-quantreg@6.1 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=esreg
Licenses: GPL 3
Build system: r
Synopsis: Joint Quantile and Expected Shortfall Regression
Description:

Simultaneous modeling of the quantile and the expected shortfall of a response variable given a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

r-einet 0.1.0
Propagated dependencies: r-shiny@1.11.1 r-magrittr@2.0.4 r-igraph@2.2.1 r-entropy@1.3.2 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/travisbyrum/einet
Licenses: Expat
Build system: r
Synopsis: Effective Information and Causal Emergence
Description:

This package provides methods and utilities for causal emergence. Used to explore and compute various information theory metrics for networks, such as effective information, effectiveness and causal emergence.

r-ebm 0.1.0
Propagated dependencies: r-reticulate@1.44.1 r-lattice@0.22-7 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/bgreenwell/ebm
Licenses: Expat
Build system: r
Synopsis: Explainable Boosting Machines
Description:

An interface to the Python InterpretML framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) <doi:10.48550/arXiv.1909.09223> for details. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable.

r-eben 5.2
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EBEN
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Empirical Bayesian Elastic Net
Description:

This package provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models. As a special case of the EBglmnet package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.

r-epe4md 0.1.4
Propagated dependencies: r-zoo@1.8-14 r-tsibble@1.2.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-scales@1.4.0 r-readxl@1.4.5 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-jrvfinance@1.4.3 r-janitor@2.2.1 r-ggplot2@4.0.1 r-future@1.68.0 r-furrr@0.3.1 r-forcats@1.0.1 r-feasts@0.5.0 r-fabletools@0.6.1 r-dplyr@1.1.4 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://epe-gov-br.github.io/epe4md/
Licenses: GPL 3+
Build system: r
Synopsis: EPE's 4MD Model to Forecast the Adoption of Distributed Generation
Description:

EPE's (Empresa de Pesquisa Energética) 4MD (Modelo de Mercado da Micro e Minigeração Distribuà da - Micro and Mini Distributed Generation Market Model) model to forecast the adoption of Distributed Generation. Given the user's assumptions, it is possible to estimate how many consumer units will have distributed generation in Brazil over the next 10 years, for example. In addition, it is possible to estimate the installed capacity, the amount of investments that will be made in the country and the monthly energy contribution of this type of generation. <https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-689/topico-639/NT_Metodologia_4MD_PDE_2032_VF.pdf>.

r-exnruleensemble 0.1.1
Propagated dependencies: r-fnn@1.1.4.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ExNRuleEnsemble
Licenses: GPL 3+
Build system: r
Synopsis: k Nearest Neibour Ensemble Based on Extended Neighbourhood Rule
Description:

The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) <doi:10.48550/arXiv.2205.15111>.

r-exampletestr 1.7.3
Propagated dependencies: r-withr@3.0.2 r-usethis@3.2.1 r-styler@1.11.0 r-stringr@1.6.0 r-strex@2.0.1 r-rstudioapi@0.17.1 r-roxygen2@7.3.3 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-fs@1.6.6 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://rorynolan.github.io/exampletestr/
Licenses: GPL 3
Build system: r
Synopsis: Help for Writing Unit Tests Based on Function Examples
Description:

Take the examples written in your documentation of functions and use them to create shells (skeletons which must be manually completed by the user) of test files to be tested with the testthat package. Sort of like python doctests for R.

r-evaltest 1.0.6
Propagated dependencies: r-shinydashboard@0.7.3 r-shiny@1.11.1 r-readxl@1.4.5 r-proc@1.19.0.1 r-openxlsx@4.2.8.1 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-dt@0.34.0 r-binom@1.1-1.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EvalTest
Licenses: Expat
Build system: r
Synopsis: Tools for Evaluating Diagnostic Test Performance
Description:

Evaluates diagnostic test performance using data from laboratory or diagnostic research. It includes functions to compute common performance indicators along with their confidence intervals, and offers an interactive shiny application for comprehensive analysis including ROC curve visualization and related metrics. It supports both binary and continuous test variables. It allows users to compute key performance indicators and visualize Receiver Operating Characteristic (ROC) curves, determine optimal cut-off thresholds, display confusion matrix, and export publication-ready plot. It aims to facilitate the application of statistical methods in diagnostic test evaluation by healthcare professionals. Methodological details and references for the computation of performance indicators are provided in the package vignette.

r-epimodel 2.5.0
Propagated dependencies: r-tibble@3.3.0 r-tergm@4.2.2 r-statnet-common@4.12.0 r-rlang@1.1.6 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-networklite@1.1.0 r-networkdynamic@0.11.5 r-network@1.19.0 r-lazyeval@0.2.2 r-ggplot2@4.0.1 r-foreach@1.5.2 r-ergm@4.12.0 r-dplyr@1.1.4 r-doparallel@1.0.17 r-desolve@1.40 r-collections@0.3.9 r-coda@0.19-4.1 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://www.epimodel.org/
Licenses: GPL 3
Build system: r
Synopsis: Mathematical Modeling of Infectious Disease Dynamics
Description:

This package provides tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).

r-envnj 0.1.3
Propagated dependencies: r-stringr@1.6.0 r-seqinr@4.2-36 r-philentropy@0.10.0 r-phangorn@2.12.1 r-bio3d@2.4-5 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EnvNJ
Licenses: GPL 2+
Build system: r
Synopsis: Whole Genome Phylogenies Using Sequence Environments
Description:

This package contains utilities for the analysis of protein sequences in a phylogenetic context. Allows the generation of phylogenetic trees base on protein sequences in an alignment-independent way. Two different methods have been implemented. One approach is based on the frequency analysis of n-grams, previously described in Stuart et al. (2002) <doi:10.1093/bioinformatics/18.1.100>. The other approach is based on the species-specific neighborhood preference around amino acids. Features include the conversion of a protein set into a vector reflecting these neighborhood preferences, pairwise distances (dissimilarity) between these vectors, and the generation of trees based on these distance matrices.

r-ecar 0.1.2
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/gpage2990/eCAR
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Eigenvalue CAR Models
Description:

Fits Leroux model in spectral domain to estimate causal spatial effect as detailed in Guan, Y; Page, G.L.; Reich, B.J.; Ventrucci, M.; Yang, S; (2020) <arXiv:2012.11767>. Both the parametric and semi-parametric models are available. The semi-parametric model relies on INLA'. The INLA package can be obtained from <https://www.r-inla.org/>.

r-explodelayout 0.1.3
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ExplodeLayout
Licenses: Expat
Build system: r
Synopsis: Calculate Exploded Coordinates Based on Original Node Coordinates and Node Clustering Membership
Description:

Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual â hairballâ that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be â explodedâ to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which â explodesâ the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl]<https://github.com/UTMB-DIVA-Lab/epl> that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) <https://pmc.ncbi.nlm.nih.gov/articles/PMC5543384/>). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks.

r-ebirdst 3.2023.1
Propagated dependencies: r-viridislite@0.4.2 r-terra@1.8-86 r-stringr@1.6.0 r-sf@1.0-23 r-scales@1.4.0 r-rlang@1.1.6 r-rcolorbrewer@1.1-3 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-dplyr@1.1.4 r-arrow@22.0.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://ebird.github.io/ebirdst/
Licenses: GPL 3
Build system: r
Synopsis: Access and Analyze eBird Status and Trends Data Products
Description:

This package provides tools for accessing and analyzing eBird Status and Trends Data Products (<https://science.ebird.org/en/status-and-trends>). eBird (<https://ebird.org/home>) is a global database of bird observations collected by member of the public. eBird Status and Trends uses these data to model global bird distributions, abundances, and population trends at a high spatial and temporal resolution.

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