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

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 search send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-stepgwr 0.1.0
Propagated dependencies: r-qpdf@1.4.1 r-numbers@0.9-2 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=StepGWR
Licenses: GPL 2+
Build system: r
Synopsis: Hybrid Spatial Model for Prediction and Capturing Spatial Variation in the Data
Description:

It is a hybrid spatial model that combines the variable selection capabilities of stepwise regression methods with the predictive power of the Geographically Weighted Regression(GWR) model.The developed hybrid model follows a two-step approach where the stepwise variable selection method is applied first to identify the subset of predictors that have the most significant impact on the response variable, and then a GWR model is fitted using those selected variables for spatial prediction at test or unknown locations. For method details,see Leung, Y., Mei, C. L. and Zhang, W. X. (2000).<DOI:10.1068/a3162>.This hybrid spatial model aims to improve the accuracy and interpretability of GWR predictions by selecting a subset of relevant variables through a stepwise selection process.This approach is particularly useful for modeling spatially varying relationships and improving the accuracy of spatial predictions.

r-sivs 0.2.11
Propagated dependencies: r-varhandle@2.0.6 r-proc@1.19.0.1 r-glmnet@5.0 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mmahmoudian/sivs
Licenses: GPL 3
Build system: r
Synopsis: Stable Iterative Variable Selection
Description:

An iterative feature selection method that internally utilizes various Machine Learning methods that have embedded feature reduction in order to shrink down the feature space into a small and yet robust set.

r-splutil 2022.6.20
Propagated dependencies: r-magrittr@2.0.5 r-ggplot2@4.0.3 r-data-table@1.18.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://docs.sykdomspulsen.no/splutil/
Licenses: Expat
Build system: r
Synopsis: Utility Functions for Common Base-R Problems Relating to Lists
Description:

Utility functions that help with common base-R problems relating to lists. Lists in base-R are very flexible. This package provides functions to quickly and easily characterize types of lists. That is, to identify if all elements in a list are null, data.frames, lists, or fully named lists. Other functionality is provided for the handling of lists, such as the easy splitting of lists into equally sized groups, and the unnesting of data.frames within fully named lists.

r-sbicgraph 1.0.0
Propagated dependencies: r-network@1.20.0 r-mass@7.3-65 r-glmnet@5.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SBICgraph
Licenses: GPL 3
Build system: r
Synopsis: Structural Bayesian Information Criterion for Graphical Models
Description:

This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review). In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.

r-salso 0.3.78
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/dbdahl/salso
Licenses: Expat ASL 2.0
Build system: r
Synopsis: Search Algorithms and Loss Functions for Bayesian Clustering
Description:

The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.

r-slick 1.0.1
Propagated dependencies: r-tibble@3.3.1 r-shiny@1.13.0 r-scales@1.4.0 r-golem@0.5.1 r-ggrepel@0.9.8 r-ggplot2@4.0.3 r-dt@0.34.0 r-dplyr@1.2.1 r-cli@3.6.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://slick.bluematterscience.com/
Licenses: GPL 2
Build system: r
Synopsis: Interactive Visualization of MSE Results
Description:

This package provides a framework for visualizing and exploring results of a Management Strategy Evaluation (MSE). The publication quality figures and tables can be developed directly from the R console, or interactively explored with the Slick App. For more details, see the Slick website <https://slick.bluematterscience.com>.

r-socviz 1.2
Propagated dependencies: r-tibble@3.3.1 r-rlang@1.2.0 r-magrittr@2.0.5 r-fs@2.1.0 r-dplyr@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://kjhealy.github.io/socviz/
Licenses: Expat
Build system: r
Synopsis: Utility Functions and Data Sets for Data Visualization
Description:

Supporting materials for a course and book on data visualization. It contains utility functions for graphs and several sample data sets. See Healy (2019) <ISBN 978-0691181622>.

r-salesforcer 1.0.2
Propagated dependencies: r-zip@2.3.3 r-xml2@1.5.2 r-xml@3.99-0.23 r-vctrs@0.7.3 r-tibble@3.3.1 r-rlist@0.4.6.2 r-rlang@1.2.0 r-readr@2.2.0 r-purrr@1.2.2 r-mime@0.13 r-lubridate@1.9.5 r-lifecycle@1.0.5 r-jsonlite@2.0.0 r-httr@1.4.8 r-dplyr@1.2.1 r-data-table@1.18.4 r-curl@7.1.0 r-base64enc@0.1-6 r-anytime@0.3.13
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/StevenMMortimer/salesforcer
Licenses: Expat
Build system: r
Synopsis: An Implementation of 'Salesforce' APIs Using Tidy Principles
Description:

This package provides functions connecting to the Salesforce Platform APIs (REST, SOAP, Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) <https://trailhead.salesforce.com/content/learn/modules/api_basics/api_basics_overview>. "API" is an acronym for "application programming interface". Most all calls from these APIs are supported as they use CSV, XML or JSON data that can be parsed into R data structures. For more details please see the Salesforce API documentation and this package's website <https://stevenmmortimer.github.io/salesforcer/> for more information, documentation, and examples.

r-simstudy 0.9.2
Propagated dependencies: r-rcpp@1.1.1-1.1 r-pbv@0.5-47 r-mvnfast@0.2.8 r-glue@1.8.1 r-fastglm@0.1.0 r-data-table@1.18.4 r-backports@1.5.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/kgoldfeld/simstudy
Licenses: GPL 3
Build system: r
Synopsis: Simulation of Study Data
Description:

Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

r-sparqlr 0.1.0
Propagated dependencies: r-tidyr@1.3.2 r-tibble@3.3.1 r-stringr@1.6.0 r-rlang@1.2.0 r-purrr@1.2.2 r-httr2@1.2.2 r-dplyr@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/sib-swiss/sparqlr
Licenses: GPL 3
Build system: r
Synopsis: SPARQL Client for R
Description:

This package provides a client for running SPARQL queries directly from R. SPARQL (short for SPARQL Protocol and RDF Query Language) is a query language used to retrieve and manipulate data stored in RDF (Resource Description Framework) format.

r-srda 1.0.0
Propagated dependencies: r-mvtnorm@1.3-7 r-matrix@1.7-5 r-foreach@1.5.2 r-elasticnet@1.3 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sRDA
Licenses: Expat
Build system: r
Synopsis: Sparse Redundancy Analysis
Description:

Sparse redundancy analysis for high dimensional (biomedical) data. Directional multivariate analysis to express the maximum variance in the predicted data set by a linear combination of variables of the predictive data set. Implemented in a partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.

r-sensmediation 0.3.1
Propagated dependencies: r-mvtnorm@1.3-7 r-maxlik@1.5-2.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sensmediation
Licenses: GPL 2
Build system: r
Synopsis: Parametric Estimation and Sensitivity Analysis of Direct and Indirect Effects
Description:

We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022) <doi:10.1007/s10260-021-00611-4>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.

r-secrdesign 2.10.1
Propagated dependencies: r-sf@1.1-1 r-secr@5.4.3 r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1 r-kofnga@1.3 r-bh@1.90.0-1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://www.otago.ac.nz/density/
Licenses: GPL 2+
Build system: r
Synopsis: Sampling Design for Spatially Explicit Capture-Recapture
Description:

This package provides tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing.

r-spreadr 0.3.0
Propagated dependencies: r-rcpp@1.1.1-1.1 r-matrix@1.7-5 r-igraph@2.3.1 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://csqsiew.github.io/spreadr/
Licenses: GPL 3
Build system: r
Synopsis: Simulating Spreading Activation in a Network
Description:

The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in spreadr subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. <doi:10.3389/fpsyg.2011.00369> and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. <doi: 10.3758/s13428-018-1186-5>.

r-ssfa 1.2.3
Propagated dependencies: r-spdep@1.4-2 r-spatialreg@1.4-3 r-sp@2.2-1 r-maxlik@1.5-2.2 r-matrix@1.7-5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ssfa
Licenses: GPL 3
Build system: r
Synopsis: Spatial Stochastic Frontier Analysis
Description:

Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term.

r-spheredata 0.1.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/santosoph/spheredata
Licenses: FSDG-compatible
Build system: r
Synopsis: Students' Performance Dataset in Physics Education Research (SPHERE)
Description:

This package provides a multidimensional dataset of students performance assessment in high school physics. The SPHERE dataset was collected from 497 students in four public high schools specifically measuring their conceptual understanding, scientific ability, and attitude toward physics [see Santoso et al. (2024) <doi:10.17632/88d7m2fv7p.1>]. The data collection was conducted using some research based assessments established by the physics education research community. They include the Force Concept Inventory, the Force and Motion Conceptual Evaluation, the Rotational and Rolling Motion Conceptual Survey, the Fluid Mechanics Concept Inventory, the Mechanical Waves Conceptual Survey, the Thermal Concept Evaluation, the Survey of Thermodynamic Processes and First and Second Laws, the Scientific Abilities Assessment Rubrics, and the Colorado Learning Attitudes about Science Survey. Students attributes related to gender, age, socioeconomic status, domicile, literacy, physics identity, and test results administered using teachers developed items are also reported in this dataset.

r-somemtp 1.4.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=someMTP
Licenses: GPL 2+
Build system: r
Synopsis: Some Multiple Testing Procedures
Description:

It's a collection of functions for Multiplicity Correction and Multiple Testing.

r-syrup 0.1.4
Propagated dependencies: r-withr@3.0.2 r-vctrs@0.7.3 r-tibble@3.3.1 r-rlang@1.2.0 r-purrr@1.2.2 r-ps@1.9.3 r-dplyr@1.2.1 r-callr@3.7.6 r-bench@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/simonpcouch/syrup
Licenses: Expat
Build system: r
Synopsis: Measure Memory and CPU Usage for Parallel R Code
Description:

Measures memory and CPU usage of R code by regularly taking snapshots of calls to the system command ps'. The package provides an entry point (albeit coarse) to profile usage of system resources by R code run in parallel.

r-surveydown 1.3.0
Propagated dependencies: r-yaml@2.3.12 r-xml2@1.5.2 r-shinywidgets@0.9.1 r-shinyjs@2.1.1 r-shiny@1.13.0 r-rvest@1.0.5 r-rstudioapi@0.18.0 r-rpostgres@1.4.10 r-rmarkdown@2.31 r-quarto@1.5.1 r-pool@1.0.5 r-miniui@0.1.2 r-markdown@2.0 r-jsonlite@2.0.0 r-htmltools@0.5.9 r-fs@2.1.0 r-dotenv@1.0.3 r-dbi@1.3.0 r-cli@3.6.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://pkg.surveydown.org
Licenses: Expat
Build system: r
Synopsis: Markdown-Based Programmable Surveys Using 'Quarto' and 'shiny'
Description:

Generate programmable surveys using markdown and R code chunks. Surveys are composed of two files: a survey.qmd Quarto file defining the survey content (pages, questions, etc), and an app.R file defining a shiny app with global settings (libraries, database configuration, etc.) and server configuration options (e.g., conditional skipping / display, etc.). Survey data collected from respondents is stored in a PostgreSQL database. Features include controls for conditional skip logic (skip to a page based on an answer to a question), conditional display logic (display a question based on an answer to a question), a customizable progress bar, and a wide variety of question types, including multiple choice (single choice and multiple choices), select, text, numeric, multiple choice buttons, text area, and dates. Because the surveys render into a shiny app, designers can also leverage the reactive capabilities of shiny to create dynamic and interactive surveys.

r-scrappy 0.0.1
Propagated dependencies: r-xml2@1.5.2 r-rvest@1.0.5 r-magrittr@2.0.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/villegar/scrappy/
Licenses: Expat
Build system: r
Synopsis: Simple Web Scraper
Description:

This package provides a group of functions to scrape data from different websites, for academic purposes.

r-statelevelforest 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=StateLevelForest
Licenses: FSDG-compatible
Build system: r
Synopsis: Historical State-Level Forest Cover Data in the United States
Description:

This package provides a unique dataset of historical forest cover across all states in the United States, spanning from 1907 to 2017, along with 1630 as a reference year. This dataset is important for understanding environmental changes and land use trends over time. It includes functionality for easy access of the data.

r-surviv 0.1.3
Propagated dependencies: r-survival@3.8-6 r-rootsolve@1.8.2.4 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/tonyhbc/surviv
Licenses: Expat
Build system: r
Synopsis: Flexible Instrumental Variable Methods for Survival Analysis
Description:

Instrumental variable analysis methods for causal inference on survival data based on the Cox model allowing for various treatment and effect types, including orthogonality method-of-moments instrumental variable estimation for the Cox model, two-stage residual inclusion Cox estimation with frailty, sequential trial emulation, sequential Cox analyses, and sequential two-stage residual inclusion Cox analyses. Methodological background includes MacKenzie et al. (2014) <doi:10.1007/s10742-014-0117-x>, Martinez-Camblor et al. (2019) <doi:10.1093/biostatistics/kxx062>, Martinez-Camblor et al. (2019) <doi:10.1111/rssc.12341>, Gran et al. (2010) <doi:10.1002/sim.4048>, and Keogh et al. (2023) <doi:10.1002/sim.9718>.

r-simjoint 0.3.12
Propagated dependencies: r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SimJoint
Licenses: GPL 3
Build system: r
Synopsis: Simulate Joint Distribution
Description:

Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator.

r-stressstrength 1.0.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=StressStrength
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Computation and Estimation of Reliability of Stress-Strength Models
Description:

Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.

Total packages: 72465