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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-clusterbootstrap 2.0.0
Propagated dependencies: r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/mathijsdeen/ClusterBootstrap
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Analyze Clustered Data using the Cluster Bootstrap
Description:

This package provides functionality for the analysis of clustered data using the cluster bootstrap.

r-checkarg 0.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=checkarg
Licenses: GPL 2+
Build system: r
Synopsis: Check the Basic Validity of a (Function) Argument
Description:

Utility functions that allow checking the basic validity of a function argument or any other value, including generating an error and assigning a default in a single line of code. The main purpose of the package is to provide simple and easily readable argument checking to improve code robustness.

r-cvglasso 1.0
Propagated dependencies: r-glasso@1.11 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/MGallow/CVglasso
Licenses: GPL 2+
Build system: r
Synopsis: Lasso Penalized Precision Matrix Estimation
Description:

Estimates a lasso penalized precision matrix via the blockwise coordinate descent (BCD). This package is a simple wrapper around the popular glasso package that extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations. See Friedman et al (2008) <doi:10.1093/biostatistics/kxm045> for details regarding the estimation method.

r-condensr 1.0.0
Propagated dependencies: r-whisker@0.4.1 r-glue@1.8.0 r-fs@1.6.6 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: http://www.michaellydeamore.com/condensr/
Licenses: Expat
Build system: r
Synopsis: Academic Group Website Generator
Description:

Helps automate Quarto website creation for small academic groups. Builds a database-like structure of people, projects and publications, linking them together with a string-based ID system. Then, provides functions to automate production of clean markdown for these structures, and in-built CSS formatting using CSS flexbox.

r-covtools 0.5.6
Propagated dependencies: r-sht@0.1.9 r-shapes@1.2.8 r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pracma@2.4.6 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-geigen@2.3 r-foreach@1.5.2 r-expm@1.0-0 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/kisungyou/CovTools
Licenses: GPL 3+
Build system: r
Synopsis: Statistical Tools for Covariance Analysis
Description:

Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.

r-crwbmetareg 1.0
Propagated dependencies: r-sandwich@3.1-1 r-rfast2@0.1.5.6 r-lmtest@0.9-40
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=crwbmetareg
Licenses: GPL 2+
Build system: r
Synopsis: Cluster Robust Wild Bootstrap Meta Regression
Description:

In meta regression sometimes the studies have multiple effects that are correlated. For this reason cluster robust standard errors must be computed. However, since the clusters are unbalanced the wild bootstrap is suggested. See Oczkowski E. and Doucouliagos H. (2015). "Wine prices and quality ratings: a meta-regression analysis". American Journal of Agricultural Economics, 97(1): 103--121. <doi:10.1093/ajae/aau057> and Cameron A. C., Gelbach J. B. and Miller D. L. (2008). "Bootstrap-based improvements for inference with clustered errors". The Review of Economics and Statistics, 90(3): 414--427. <doi:10.1162/rest.90.3.414>.

r-csmpv 1.0.5
Propagated dependencies: r-xgboost@1.7.11.1 r-survminer@0.5.1 r-survival@3.8-3 r-scales@1.4.0 r-rms@8.1-0 r-matrix@1.7-4 r-hmisc@5.2-4 r-glmnet@4.1-10 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-forestmodel@0.6.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=csmpv
Licenses: Expat
Build system: r
Synopsis: Biomarker Confirmation, Selection, Modelling, Prediction, and Validation
Description:

There are diverse purposes such as biomarker confirmation, novel biomarker discovery, constructing predictive models, model-based prediction, and validation. It handles binary, continuous, and time-to-event outcomes at the sample or patient level. - Biomarker confirmation utilizes established functions like glm() from stats', coxph() from survival', surv_fit(), and ggsurvplot() from survminer'. - Biomarker discovery and variable selection are facilitated by three LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging the glmnet R package with additional steps. - Eight versatile modeling functions are offered, each designed for predictive models across various outcomes and data types. 1) LASSO2(), LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection using LASSO methods and construct predictive models based on selected variables. 2) XGBtraining() employs XGBoost for model building and is the only function not involving variable selection. 3) Functions like LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine LASSO-related variable selection with XGBoost for model construction. - All models support prediction and validation, requiring a testing dataset comparable to the training dataset. Additionally, the package introduces XGpred() for risk prediction based on survival data, with the XGpred_predict() function available for predicting risk groups in new datasets. The methodology is based on our new algorithms and various references: - Hastie et al. (1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al. (2021) <https://CRAN.R-project.org/package=survminer>, - Friedman et al. (2010) <doi:10.18637/jss.v033.i01>, - Simon et al. (2011) <doi:10.18637/jss.v039.i05>, - Harrell (2023) <https://CRAN.R-project.org/package=rms>, - Harrell (2023) <https://CRAN.R-project.org/package=Hmisc>, - Chen and Guestrin (2016) <doi:10.48550/arXiv.1603.02754>, - Aoki et al. (2023) <doi:10.1200/JCO.23.01115>.

r-carts 0.1.0
Propagated dependencies: r-targeted@0.7.1 r-survival@3.8-3 r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-r6@2.6.1 r-progressr@0.18.0 r-logger@0.4.1 r-lava@1.8.2 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://novonordisk-opensource.github.io/carts/
Licenses: FSDG-compatible
Build system: r
Synopsis: Simulation-Based Assessment of Covariate Adjustment in Randomized Trials
Description:

Monte Carlo simulation framework for different randomized clinical trial designs with a special emphasis on estimators based on covariate adjustment. The package implements regression-based covariate adjustment (Rosenblum & van der Laan (2010) <doi:10.2202/1557-4679.1138>) and a one-step estimator (Van Lancker et al (2024) <doi:10.48550/arXiv.2404.11150>) for trials with continuous, binary and count outcomes. The estimation of the minimum sample-size required to reach a specified statistical power for a given estimator uses bisection to find an initial rough estimate, followed by stochastic approximation (Robbins-Monro (1951) <doi:10.1214/aoms/1177729586>) to improve the estimate, and finally, a grid search to refine the estimate in the neighborhood of the current best solution.

r-cmstatrext 0.4.0
Propagated dependencies: r-testthat@3.3.0 r-rlang@1.1.6 r-rcpp@1.1.0 r-generics@0.1.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/cmstatr/cmstatrExt
Licenses: AGPL 3
Build system: r
Synopsis: More Statistical Methods for Composite Material Data
Description:

This package provides a companion package to cmstatr <https://cran.r-project.org/package=cmstatr>. cmstatr contains statistical methods that are published in the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4), while cmstatrExt contains statistical methods that are not included in that handbook.

r-chngpt 2024.11-15
Propagated dependencies: r-survival@3.8-3 r-rhpcblasctl@0.23-42 r-mass@7.3-65 r-lme4@1.1-37 r-kyotil@2024.11-01 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=chngpt
Licenses: GPL 2+
Build system: r
Synopsis: Estimation and Hypothesis Testing for Threshold Regression
Description:

Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) <DOI:10.1186/s12859-017-1863-x> and the package vignette.

r-countstar 1.0.2
Propagated dependencies: r-truncdist@1.0-2 r-truncatednormal@2.3 r-splines2@0.5.4 r-spikeslabgam@1.1-20 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-randomforest@4.7-1.2 r-matrix@1.7-4 r-kfas@1.6.0 r-gbm@2.2.2 r-fastgp@1.2 r-dbarts@0.9-32 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=countSTAR
Licenses: GPL 2+
Build system: r
Synopsis: Flexible Modeling of Count Data
Description:

For Bayesian and classical inference and prediction with count-valued data, Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable, and easy-to-use approach. STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package also includes several methods for modeling count time series data, namely via warped Dynamic Linear Models. For more details and background on these methodologies, see the works of Kowal and Canale (2020) <doi:10.1214/20-EJS1707>, Kowal and Wu (2022) <doi:10.1111/biom.13617>, King and Kowal (2022) <arXiv:2110.14790>, and Kowal and Wu (2023) <arXiv:2110.12316>.

r-cfilt 0.3.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CFilt
Licenses: GPL 3
Build system: r
Synopsis: Recommendation by Collaborative Filtering
Description:

This package provides methods and functions to implement a Recommendation System based on Collaborative Filtering Methodology. See Aggarwal (2016) <doi:10.1007/978-3-319-29659-3> for an overview.

r-cdmconnector 2.4.0
Propagated dependencies: r-withr@3.0.2 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-stringr@1.6.0 r-stringi@1.8.7 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-omopgenerics@1.3.7 r-lifecycle@1.0.4 r-jsonlite@2.0.0 r-glue@1.8.0 r-generics@0.1.4 r-dplyr@1.1.4 r-dbplyr@2.5.1 r-dbi@1.2.3 r-cli@3.6.5 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://darwin-eu.github.io/CDMConnector/
Licenses: FSDG-compatible
Build system: r
Synopsis: Connect to an OMOP Common Data Model
Description:

This package provides tools for working with observational health data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax. Common data model database table references are stored in a single compound object along with metadata.

r-calango 1.0.20
Propagated dependencies: r-rmarkdown@2.30 r-plotly@4.11.0 r-pkgdown@2.2.0 r-pbmcapply@1.5.1 r-nlme@3.1-168 r-knitr@1.50 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-heatmaply@1.6.0 r-ggplot2@4.0.1 r-dt@0.34.0 r-dendextend@1.19.1 r-biocmanager@1.30.27 r-assertthat@0.2.1 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://labpackages.github.io/CALANGO/
Licenses: GPL 2
Build system: r
Synopsis: Comparative Analysis with Annotation-Based Genomic Components
Description:

This package provides a first-principle, phylogeny-aware comparative genomics tool for investigating associations between terms used to annotate genomic components (e.g., Pfam IDs, Gene Ontology terms,) with quantitative or rank variables such as number of cell types, genome size, or density of specific genomic elements. See the project website for more information, documentation and examples, and <doi:10.1016/j.patter.2023.100728> for the full paper.

r-cry 0.5.2
Propagated dependencies: r-zoo@1.8-14 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://jfoadi.github.io/cry/
Licenses: GPL 2
Build system: r
Synopsis: Statistics for Structural Crystallography
Description:

Reading and writing of files in the most commonly used formats of structural crystallography. It includes functions to work with a variety of statistics used in this field and functions to perform basic crystallographic computing. References: D. G. Waterman, J. Foadi, G. Evans (2011) <doi:10.1107/S0108767311084303>.

r-cdlei 1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cdlei
Licenses: GPL 2
Build system: r
Synopsis: Cause-Deleted Life Expectancy Improvement Procedure
Description:

The concept of cause-deleted life expectancy improvement is statistic designed to quantify the increase in life expectancy if a certain cause of death is removed. See Adamic, P. (2015) (<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2689352>).

r-clr 0.1.2
Propagated dependencies: r-magrittr@2.0.4 r-lubridate@1.9.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=clr
Licenses: LGPL 2.0+
Build system: r
Synopsis: Curve Linear Regression via Dimension Reduction
Description:

This package provides a new methodology for linear regression with both curve response and curve regressors, which is described in Cho, Goude, Brossat and Yao (2013) <doi:10.1080/01621459.2012.722900> and (2015) <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several scalar linear regression problems.

r-camtrapdp 0.5.0
Propagated dependencies: r-uuid@1.2-1 r-stringr@1.6.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-memoise@2.0.1 r-frictionless@1.2.1 r-eml@2.0.7 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/inbo/camtrapdp
Licenses: Expat
Build system: r
Synopsis: Read and Manipulate Camera Trap Data Packages
Description:

Read and manipulate Camera Trap Data Packages ('Camtrap DP'). Camtrap DP (<https://camtrap-dp.tdwg.org>) is a data exchange format for camera trap data. With camtrapdp you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. camtraptor or camtrapR'.

r-ciplot 1.0
Propagated dependencies: r-multcomp@1.4-29 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/toshi-ara/CIplot
Licenses: GPL 2+
Build system: r
Synopsis: Functions to Plot Confidence Interval
Description:

Plot confidence interval from the objects of statistical tests such as t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest class), Tukey test [TukeyHSD()], Dunnett test [glht() in multcomp package], logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in userfriendlyscience package]. Users are able to set the styles of lines and points. This package contains the function to calculate odds ratios and their confidence intervals from the result of logistic regression.

r-configular 0.1.1
Propagated dependencies: r-stringr@1.6.0 r-magrittr@2.0.4 r-here@1.0.2 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://dataupsurge.github.io/configulaR/
Licenses: Expat
Build system: r
Synopsis: Manage Application Settings via '.env' or '.ini' Files
Description:

This package provides a simple way to manage application settings by loading configuration values from .env or .ini files. It supports default values, type casting, and environment variable overrides, enabling a clean separation of configuration from code. Ideal for managing credentials, API keys, and deployment-specific settings.

r-cancerscreening 1.1.1
Propagated dependencies: r-withr@3.0.2 r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-lubridate@1.9.4 r-khisr@1.0.6 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cancerscreening.damurka.com
Licenses: Expat
Build system: r
Synopsis: Streamline Access to Cancer Screening Data
Description:

Retrieve cancer screening data for cervical, breast and colorectal cancers from the Kenya Health Information System <https://hiskenya.org> in a consistent way.

r-con2aqi 0.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=con2aqi
Licenses: GPL 3
Build system: r
Synopsis: Calculate the AQI from Pollutant Concentration
Description:

To calculate the AQI (Air Quality Index) from pollutant concentration data. O3, PM2.5, PM10, CO, SO2, and NO2 are available currently. The method can be referenced at Environmental Protection Agency, United States as follows: EPA (2016) <https://www3.epa.gov/airnow/aqi-technical-assistance-document-may2016.pdf>.

r-cdfinv 0.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cdfinv
Licenses: Expat
Build system: r
Synopsis: Confidence Interval Estimation via CDF Inversion
Description:

Estimation of one- and two-sided confidence intervals via the numerical inversion of the cumulative distribution function of a statistic's sampling distribution. For more details, see section 9.2.3 of Casella and Berger (2002) <ISBN:0534243126>.

r-cohortsymmetry 0.2.4
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-patientprofiles@1.5.0 r-omopgenerics@1.3.7 r-here@1.0.2 r-drugutilisation@1.1.0 r-dplyr@1.1.4 r-codelistgenerator@4.0.2 r-cdmconnector@2.4.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://ohdsi.github.io/CohortSymmetry/
Licenses: FSDG-compatible
Build system: r
Synopsis: Sequence Symmetry Analysis Using the Observational Medical Outcomes Partnership Common Data Model
Description:

Calculating crude sequence ratio, adjusted sequence ratio and confidence intervals using data mapped to the Observational Medical Outcomes Partnership Common Data Model.

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Total results: 69112