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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-soilhypfit 0.1-8
Dependencies: mpfr@4.2.2 gmp@6.3.0
Propagated dependencies: r-soilhyp@0.1.7 r-snowfall@1.84-6.3 r-rmpfr@1.1-2 r-quadprog@1.5-8 r-nloptr@2.2.1 r-mgcv@1.9-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=soilhypfit
Licenses: GPL 2+ LGPL 3
Build system: r
Synopsis: Modelling of Soil Water Retention and Hydraulic Conductivity Data
Description:

This package provides functions for efficiently estimating properties of the Van Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil water retention and hydraulic conductivity data by multi-response parameter estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) "Parameter estimation from multi-response data" <doi:10.1002/aic.690380502>). Parameter estimation is simplified by exploiting the fact that residual and saturated water contents and saturated conductivity are conditionally linear parameters (Bates, D. M. and Watts, D. G. (1988) "Nonlinear Regression Analysis and Its Applications" <doi:10.1002/9780470316757>). Estimated parameters are optionally constrained by the evaporation characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020) "Physical Constraints for Improved Soil Hydraulic Parameter Estimation by Pedotransfer Functions" <doi:10.1029/2019WR025963>) to ensure that the estimated parameters are physically valid. Common S3 methods and further utility functions allow to process, explore and visualise estimation results.

r-shiny-ollama 0.1.1
Propagated dependencies: r-shiny@1.11.1 r-mockery@0.4.5 r-markdown@2.0 r-jsonlite@2.0.0 r-httr@1.4.7 r-bslib@0.9.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://www.indraneelchakraborty.com/shiny.ollama/
Licenses: FSDG-compatible
Build system: r
Synopsis: R 'shiny' Interface for Chatting with Large Language Models Offline on Local with 'ollama'
Description:

Chat with large language models on your machine without internet with complete privacy via ollama', powered by R shiny interface. For more information on ollama', visit <https://ollama.com>.

r-solar 0.47
Propagated dependencies: r-zoo@1.8-14 r-rcolorbrewer@1.1-3 r-latticeextra@0.6-31 r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://oscarperpinan.codeberg.page/solar/
Licenses: GPL 3
Build system: r
Synopsis: Radiation and Photovoltaic Systems
Description:

Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.

r-sahpm 1.0.1
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sahpm
Licenses: GPL 2
Build system: r
Synopsis: Variable Selection using Simulated Annealing
Description:

Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. Our stochastic search process SAHPM based on simulated annealing maximization method tries to find the highest posterior model by maximizing the model space with respect to the posterior probabilities of the models. This package currently contains the SAHPM method only for linear models. The codes for GLM will be added in future.

r-soilfunctionality 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SoilFunctionality
Licenses: GPL 3
Build system: r
Synopsis: Soil Functionality Measurement
Description:

Generally, soil functionality is characterized by its capability to sustain microbial activity, nutritional element supply, structural stability and aid for crop production. Since soil functions can be linked to 80% of ecosystem services, conservation of degraded land should strive to restore not only the capacity of soil to sustain flora but also ecosystem provisions. The primary ecosystem services of soil are carbon sequestration, food or biomass production, provision of microbial habitat, nutrient recycling. However, the actual magnitude of soil functions provided by agricultural land uses has never been quantified. Nutrient supply capacity (NSC) is a measure of nutrient dynamics in restored land uses. Carbon accumulation proficiency (CAP) is a measure of ecosystem carbon sequestration. Biological activity index (BAI) is the average of responses of all enzyme activities in treated land over control/reference land. The CAP parameter investigates how land uses may affect carbon flows, retention, and sequestration. The CAP provides a signal for C cycles, flows, and the systems relative operational supremacy.

r-segcorr 1.2
Propagated dependencies: r-jointseg@1.0.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SegCorr
Licenses: GPL 2
Build system: r
Synopsis: Detecting Correlated Genomic Regions
Description:

This package performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression.

r-samplesizecalculator 0.1.0
Propagated dependencies: r-shinythemes@1.2.0 r-shiny@1.11.1 r-dt@0.34.0 r-bslib@0.9.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SampleSizeCalculator
Licenses: GPL 2+
Build system: r
Synopsis: Sample Size Calculator under Complex Survey Design
Description:

It helps in determination of sample size for estimating population mean or proportion under simple random sampling with or without replacement and stratified random sampling without replacement. When prior information on the population coefficient of variation (CV) is unavailable, then a preliminary sample is drawn to estimate the CV which is used to compute the final sample size. If the final size exceeds the preliminary sample size, then additional units are drawn; otherwise, the preliminary sample size is considered as final sample size. For stratified random sampling without replacement design, it also calculates the sample size in each stratum under different allocation methods for estimation of population mean and proportion based upon the availability of prior information on sizes of the strata, standard deviations of the strata and costs of drawing a sampling unit in the strata.For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" <https://archive.org/details/samplingtechniqu0000coch_t4x6>.

r-studystrap 1.0.0
Propagated dependencies: r-tidyverse@2.0.0 r-tibble@3.3.0 r-pls@2.8-5 r-nnls@1.6 r-matrixcorrelation@0.10.1 r-dplyr@1.1.4 r-cca@1.2.2 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=studyStrap
Licenses: Expat
Build system: r
Synopsis: Study Strap and Multi-Study Learning Algorithms
Description:

This package implements multi-study learning algorithms such as merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap, the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights. Embedded within the caret framework, this package allows for a wide range of single-study learners (e.g., neural networks, lasso, random forests). The package offers over 20 default similarity measures and allows for specification of custom similarity measures for covariate-profile similarity weighting and an accept/reject step. This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019) <doi:10.1101/856385>.

r-serofoi 1.0.3
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-purrr@1.2.0 r-loo@2.8.0 r-glue@1.8.0 r-ggplot2@4.0.1 r-expm@1.0-0 r-dplyr@1.1.4 r-cowplot@1.2.0 r-config@0.3.2 r-checkmate@2.3.3 r-bh@1.87.0-1 r-bayesplot@1.14.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/epiverse-trace/serofoi
Licenses: Expat
Build system: r
Synopsis: Bayesian Estimation of the Force of Infection from Serological Data
Description:

Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions.

r-seqtarget 1.3.6
Propagated dependencies: r-survival@3.8-3 r-stringr@1.6.0 r-parallelly@1.45.1 r-knitr@1.50 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-future@1.68.0 r-fastglm@0.0.3 r-dorng@1.8.6.2 r-dofuture@1.1.2 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://causalinference.github.io/SEQTaRget/
Licenses: Expat
Build system: r
Synopsis: Sequential Trial Emulation
Description:

Implementation of sequential trial emulation for the analysis of observational databases. The SEQTaRget software accommodates time-varying treatments and confounders, as well as binary and failure time outcomes. SEQTaRget allows to compare both static and dynamic strategies, can be used to estimate observational analogs of intention-to-treat and per-protocol effects, and can adjust for potential selection bias induced by losses-to-follow-up. (Paper to come).

r-seasonal 1.10.0
Propagated dependencies: r-x13binary@1.1.61.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://www.seasonal.website
Licenses: GPL 3
Build system: r
Synopsis: R Interface to X-13-ARIMA-SEATS
Description:

Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the seasonalview package. Uses the X-13-binaries from the x13binary package.

r-stlelm 0.1.1
Propagated dependencies: r-nnfor@0.9.9 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=stlELM
Licenses: GPL 3
Build system: r
Synopsis: Hybrid Forecasting Model Based on STL Decomposition and ELM
Description:

Univariate time series forecasting with STL decomposition based Extreme Learning Machine hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

r-sopie 1.6
Propagated dependencies: r-circular@0.5-2 r-adgoftest@0.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SOPIE
Licenses: GPL 3
Build system: r
Synopsis: Non-Parametric Estimation of the Off-Pulse Interval of a Pulsar
Description:

This package provides functions to non-parametrically estimate the off-pulse interval of a source function originating from a pulsar. The technique is based on a sequential application of P-values obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.

r-spectrakit 0.1.2
Propagated dependencies: r-tibble@3.3.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-magick@2.9.0 r-glue@1.8.0 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spectrakit
Licenses: Expat
Build system: r
Synopsis: Spectral Data Handling and Visualization
Description:

This package provides functions to combine, normalize and visualize spectral data, perform principal component analysis (PCA), and assemble customizable image grids suitable for publication-quality scientific figures.

r-samplrdata 1.0.0
Propagated dependencies: r-rdpack@2.6.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/lucas-castillo/samplrData
Licenses: FSDG-compatible
Build system: r
Synopsis: Datasets from the SAMPLING Project
Description:

This package contains human behaviour datasets collected by the SAMPLING project (<https://sampling.warwick.ac.uk>).

r-shinylogs 0.2.1
Propagated dependencies: r-shiny@1.11.1 r-nanotime@0.3.12 r-jsonlite@2.0.0 r-htmltools@0.5.8.1 r-digest@0.6.39 r-data-table@1.17.8 r-bit64@4.6.0-1 r-anytime@0.3.12
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/dreamRs/shinylogs
Licenses: GPL 3
Build system: r
Synopsis: Record Everything that Happens in a 'Shiny' Application
Description:

Track and record the use of applications and the user's interactions with Shiny inputs. Allows to trace the inputs with which the user interacts, the outputs generated, as well as the errors displayed in the interface.

r-shinyitemanalysis 1.5.5
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-purrr@1.2.0 r-psych@2.5.6 r-nnet@7.3-20 r-mirt@1.45.1 r-lme4@1.1-37 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-difr@6.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://shinyitemanalysis.org/
Licenses: GPL 3
Build system: r
Synopsis: Test and Item Analysis via Shiny
Description:

Package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.

r-sketching 0.1.2
Propagated dependencies: r-rcpp@1.1.0 r-phangorn@2.12.1 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/sokbae/sketching/
Licenses: GPL 3
Build system: r
Synopsis: Sketching of Data via Random Subspace Embeddings
Description:

Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45รข 80.

r-swfscdas 0.6.4
Propagated dependencies: r-tidyr@1.3.1 r-swfscmisc@1.7 r-sf@1.0-23 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://swfsc.github.io/swfscDAS/
Licenses: FSDG-compatible
Build system: r
Synopsis: Processing DAS Data Files
Description:

Process and summarize DAS data files. These files are typically, but do not have to be DAS <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data produced by the Southwest Fisheries Science Center (SWFSC) program WinCruz'. This package standardizes and streamlines basic DAS data processing, and includes a PDF with the DAS data format requirements expected by the package.

r-statnet 2019.6
Propagated dependencies: r-tsna@0.3.6 r-tergm@4.2.2 r-statnet-common@4.12.0 r-sna@2.8 r-networkdynamic@0.11.5 r-network@1.19.0 r-ergm-count@4.1.3 r-ergm@4.12.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://statnet.org
Licenses: FSDG-compatible
Build system: r
Synopsis: Software Tools for the Statistical Analysis of Network Data
Description:

Statnet is a collection of packages for statistical network analysis that are designed to work together because they share common data representations and API design. They provide an integrated set of tools for the representation, visualization, analysis, and simulation of many different forms of network data. This package is designed to make it easy to install and load the key statnet packages in a single step. Learn more about statnet at <http://www.statnet.org>. Tutorials for many packages can be found at <https://github.com/statnet/Workshops/wiki>. For an introduction to functions in this package, type help(package='statnet').

r-simplegraph 1.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/gaborcsardi/simplegraph
Licenses: Expat
Build system: r
Synopsis: Simple Graph Data Types and Basic Algorithms
Description:

Simple classic graph algorithms for simple graph classes. Graphs may possess vertex and edge attributes. simplegraph has no dependencies and it is written entirely in R, so it is easy to install.

r-slouch 2.1.5
Propagated dependencies: r-memoise@2.0.1 r-crayon@1.5.3 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/kopperud/slouch
Licenses: GPL 2
Build system: r
Synopsis: Stochastic Linear Ornstein-Uhlenbeck Comparative Hypotheses
Description:

An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.

r-sensitivitycalibration 0.0.1
Propagated dependencies: r-stringi@1.8.7 r-splitstackshape@1.4.8 r-relaimpo@2.2-7 r-plotly@4.11.0 r-ggrepel@0.9.6 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sensitivityCalibration
Licenses: Expat
Build system: r
Synopsis: Calibrated Sensitivity Analysis for Matched Observational Studies
Description:

This package implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates. Zhang, B., Small, D. S. (2018). <arXiv:1812.00215>.

r-sensitivitymult 1.0.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sensitivitymult
Licenses: GPL 2
Build system: r
Synopsis: Sensitivity Analysis for Observational Studies with Multiple Outcomes
Description:

Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>. Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.

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