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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-sgee 0.6-0
Propagated dependencies: r-mvtnorm@1.3-3 r-copula@1.1-6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sgee
Licenses: GPL 3+
Build system: r
Synopsis: Stagewise Generalized Estimating Equations
Description:

Stagewise techniques implemented with Generalized Estimating Equations to handle individual, group, bi-level, and interaction selection. Stagewise approaches start with an empty model and slowly build the model over several iterations, which yields a path of candidate models from which model selection can be performed. This slow brewing approach gives stagewise techniques a unique flexibility that allows simple incorporation of Generalized Estimating Equations; see Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017) <doi:10.1111/biom.12669> for details.

r-sendgridr 0.6.1
Propagated dependencies: r-usethis@3.2.1 r-magrittr@2.0.4 r-keyring@1.4.1 r-jsonlite@2.0.0 r-httr@1.4.7 r-emayili@0.9.3 r-cli@3.6.5 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mrchypark/sendgridr
Licenses: Expat
Build system: r
Synopsis: Mail Sender Using 'Sendgrid' Service
Description:

Send email using Sendgrid <https://sendgrid.com/> mail API(v3) <https://docs.sendgrid.com/api-reference/how-to-use-the-sendgrid-v3-api/authentication>.

r-secrfunc 1.0.0
Propagated dependencies: r-rcppparallel@5.1.11-1 r-rcppnumerical@0.6-0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-bh@1.87.0-1
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: Helper Functions for Package 'secr'
Description:

This package provides functions are provided for internal use by the spatial capture-recapture package secr (from version 5.4.0). The idea is to speed up the installation of secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from RcppNumerical and RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data.

r-siminf 10.1.0
Dependencies: gsl@2.8
Propagated dependencies: r-matrix@1.7-4 r-mass@7.3-65 r-digest@0.6.39
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/stewid/SimInf
Licenses: GPL 3
Build system: r
Synopsis: Framework for Data-Driven Stochastic Disease Spread Simulations
Description:

This package provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and OpenMP (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172> or the Particle Markov Chain Monte Carlo ('PMCMC') algorithm of Andrieu and others (2010) <doi:10.1111/j.1467-9868.2009.00736.x>.

r-spicefp 0.1.2
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-purrr@1.2.0 r-matrix@1.7-4 r-genlasso@1.6.1 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://cran.r-project.org/package=SpiceFP
Licenses: GPL 3
Build system: r
Synopsis: Sparse Method to Identify Joint Effects of Functional Predictors
Description:

This package provides a set of functions allowing to implement the SpiceFP approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates. The methods in this package are describing in Girault Gnanguenon Guesse, Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021) "An exploratory penalized regression to identify combined effects of functional variables -Application to agri-environmental issues" <https://hal.archives-ouvertes.fr/hal-03298977>.

r-surreal 0.0.2
Propagated dependencies: r-png@0.1-8 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/coatless-rpkg/surreal
Licenses: GPL 3+
Build system: r
Synopsis: Create Datasets with Hidden Images in Residual Plots
Description:

This package implements the "Residual (Sur)Realism" algorithm described by Stefanski (2007) <doi:10.1198/000313007X190079> to generate datasets that reveal hidden images or messages in their residual plots. It offers both predefined datasets and tools to embed custom text or images into residual structures. Allowing users to create intriguing visual demonstrations for teaching model diagnostics.

r-smosr 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-terra@1.8-86 r-rcurl@1.98-1.17 r-ncdf4@1.24 r-lubridate@1.9.4 r-fields@17.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/tshestakova/smosr
Licenses: GPL 3
Build system: r
Synopsis: Acquire and Explore BEC-SMOS L4 Soil Moisture Data in R
Description:

This package provides functions that automate accessing, downloading and exploring Soil Moisture and Ocean Salinity (SMOS) Level 4 (L4) data developed by Barcelona Expert Center (BEC). Particularly, it includes functions to search for, acquire, extract, and plot BEC-SMOS L4 soil moisture data downscaled to ~1 km spatial resolution. Note that SMOS is one of Earth Explorer Opportunity missions by the European Space Agency (ESA). More information about SMOS products can be found at <https://earth.esa.int/eogateway/missions/smos/data>.

r-splineplot 0.2.1
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/jinseob2kim/splineplot
Licenses: ASL 2.0
Build system: r
Synopsis: Visualization of Spline Effects in GAM and GLM Models
Description:

This package creates ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models) fitted with mgcv and spline effects from GLM (Generalized Linear Models). Supports interaction terms and provides hazard ratio plots with histograms for survival analysis. Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models.

r-statconfr 0.2.1
Propagated dependencies: r-rmisc@1.5.1 r-plyr@1.8.9 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/ManuelRausch/StatConfR
Licenses: GPL 3+
Build system: r
Synopsis: Models of Decision Confidence and Measures of Metacognition
Description:

This package provides fitting functions and other tools for decision confidence and metacognition researchers, including meta-d'/d', often considered to be the gold standard to measure metacognitive efficiency, and information-theoretic measures of metacognition. Also allows to fit and compare several static models of decision making and confidence.

r-swpcindex 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SwPcIndex
Licenses: GPL 2+
Build system: r
Synopsis: Computation of Survey Weighted PC Based Composite Index
Description:

An index is created using a mathematical model that transforms multi-dimensional variables into a single value. These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices. For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986) <DOI:10.1080/01621459.1986.10478336>, Singh, D., Basak, P., Kumar, R. and Ahmad, T. (2023) <DOI:10.3389/fams.2023.1274530>.

r-samc 4.1.0
Propagated dependencies: r-terra@1.8-86 r-rcppthread@2.2.0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-raster@3.6-32 r-matrix@1.7-4 r-circular@0.5-2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://andrewmarx.github.io/samc/
Licenses: AGPL 3+
Build system: r
Synopsis: Spatial Absorbing Markov Chains
Description:

This package implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains.

r-srlars 1.0.1
Propagated dependencies: r-glmnet@4.1-10 r-cellwise@2.5.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=srlars
Licenses: GPL 2+
Build system: r
Synopsis: Split Robust Least Angle Regression
Description:

This package provides functions to perform split robust least angle regression. The approach first uses the least angle regression algorithm to split the variables into the models of an ensemble and robust estimates of the correlation between predictors. An elastic net estimator is then applied to the selected predictors in each model using the imputed data from the detect deviating cell (DDC) method.

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-septest 0.0.1
Propagated dependencies: r-splancs@2.01-45 r-spatstat-model@3.5-0 r-spatstat-geom@3.6-1 r-spatstat-explore@3.6-0 r-scatterplot3d@0.3-44 r-reshape2@1.4.5 r-patchwork@1.3.2 r-mass@7.3-65 r-ggplot2@4.0.1 r-get@1.0-7 r-fields@17.1 r-dhsic@2.1 r-combinat@0.0-8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mghorbani01/SepTest
Licenses: GPL 3
Build system: r
Synopsis: Tests for First-Order Separability in Spatio-Temporal Point Processes
Description:

This package provides statistical tools for testing first-order separability in spatio-temporal point processes, that is, assessing whether the spatio-temporal intensity function can be expressed as the product of spatial and temporal components. The package implements several hypothesis tests, including exact and asymptotic methods for Poisson and non-Poisson processes. Methods include global envelope tests, chi-squared type statistics, and a novel Hilbert-Schmidt independence criterion (HSIC) test, all with both block and pure permutation procedures. Simulation studies and real world examples, including the 2001 UK foot and mouth disease outbreak data, illustrate the utility of the proposed methods. The package contains all simulation studies and applications presented in Ghorbani et al. (2021) <doi:10.1016/j.csda.2021.107245> and Ghorbani et al. (2025) <doi:10.1007/s11749-025-00972-y>.

r-spatfd 0.0.1
Propagated dependencies: r-tidyr@1.3.1 r-sp@2.2-0 r-sf@1.0-23 r-reshape@0.8.10 r-proxy@0.4-27 r-plotly@4.11.0 r-mass@7.3-65 r-gstat@2.1-4 r-ggplot2@4.0.1 r-geor@1.9-6 r-fda-usc@2.2.0 r-fda@6.3.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SpatFD
Licenses: GPL 3
Build system: r
Synopsis: Functional Geostatistics: Univariate and Multivariate Functional Spatial Prediction
Description:

Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. SpatFD is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s10260-015-0340-9>, Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s00477-016-1266-y>, Bohorquez M., Giraldo R. and Mateu J. (2021) <doi:10.1002/9781119387916>.

r-simriv 1.0.7
Propagated dependencies: r-terra@1.8-86 r-mco@1.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://www.r-project.org
Licenses: GPL 2+
Build system: r
Synopsis: Simulating Multistate Movements in River/Heterogeneous Landscapes
Description:

This package provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of Movement Ecology hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics.

r-shinytesters 0.1.0
Propagated dependencies: r-rlang@1.1.6 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://ashbaldry.github.io/shinytesters/
Licenses: GPL 3
Build system: r
Synopsis: Update 'Shiny' Inputs when using testServer()
Description:

Create mocked bindings to Shiny update functions within test function calls to automatically update input values. The mocked bindings simulate the communication between the server and UI components of a Shiny module in testServer().

r-shoredate 1.1.1
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23 r-ggspatial@1.1.10 r-ggridges@0.5.7 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://github.com/isakro/shoredate
Licenses: GPL 3+
Build system: r
Synopsis: Shoreline Dating Coastal Stone Age Sites
Description:

This package provides tools for shoreline dating coastal Stone Age sites. The implemented method was developed in Roalkvam (2023) <doi:10.1016/j.quascirev.2022.107880> for the Norwegian Skagerrak coast. Although it can be extended to other areas, this also forms the core area for application of the package. Shoreline dating is based on the present-day elevation of a site, a reconstruction of past relative sea-level change, and empirically derived estimates of the likely elevation of the sites above the contemporaneous sea-level when they were in use. The geographical and temporal coverage of the method thus follows from the availability of local geological reconstructions of shoreline displacement and the degree to which the settlements to be dated have been located on or close to the shoreline when they were in use. Methods for numerical treatment and visualisation of the dates are provided, along with basic tools for visualising and evaluating the location of sites.

r-stacking 0.2.1
Propagated dependencies: 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=stacking
Licenses: Expat
Build system: r
Synopsis: Building Predictive Models with Stacking
Description:

Building predictive models with stacking which is a type of ensemble learning. Learners can be specified from those implemented in caret'. For more information of the package, see Nukui and Onogi (2023) <doi:10.1101/2023.06.06.543970>.

r-skewmlrm 1.7
Propagated dependencies: r-mvtnorm@1.3-3 r-moments@0.14.1 r-matrixcalc@1.0-6 r-mass@7.3-65 r-foreach@1.5.2 r-doparallel@1.0.17 r-clustergeneration@1.3.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=skewMLRM
Licenses: GPL 2+
Build system: r
Synopsis: Estimation for Scale-Shape Mixtures of Skew-Normal Distributions
Description:

Provide data generation and estimation tools for the multivariate scale mixtures of normal presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the multivariate scale mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of normal presented in Louredo, Zeller and Ferreira (2021) <doi:10.1007/s13571-021-00257-y> and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020) <doi:10.1080/03610918.2020.1804582>.

r-shapley 0.5.1
Propagated dependencies: r-pander@0.6.6 r-h2o@3.44.0.3 r-ggplot2@4.0.1 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/haghish/shapley
Licenses: Expat
Build system: r
Synopsis: Weighted Mean SHAP and CI for Robust Feature Assessment in ML Grid
Description:

This R package introduces Weighted Mean SHapley Additive exPlanations (WMSHAP), an innovative method for calculating SHAP values for a grid of fine-tuned base-learner machine learning models as well as stacked ensembles, a method not previously available due to the common reliance on single best-performing models. By integrating the weighted mean SHAP values from individual base-learners comprising the ensemble or individual base-learners in a tuning grid search, the package weights SHAP contributions according to each model's performance, assessed by multiple either R squared (for both regression and classification models). alternatively, this software also offers weighting SHAP values based on the area under the precision-recall curve (AUCPR), the area under the curve (AUC), and F2 measures for binary classifiers. It further extends this framework to implement weighted confidence intervals for weighted mean SHAP values, offering a more comprehensive and robust feature importance evaluation over a grid of machine learning models, instead of solely computing SHAP values for the best model. This methodology is particularly beneficial for addressing the severe class imbalance (class rarity) problem by providing a transparent, generalized measure of feature importance that mitigates the risk of reporting SHAP values for an overfitted or biased model and maintains robustness under severe class imbalance, where there is no universal criteria of identifying the absolute best model. Furthermore, the package implements hypothesis testing to ascertain the statistical significance of SHAP values for individual features, as well as comparative significance testing of SHAP contributions between features. Additionally, it tackles a critical gap in feature selection literature by presenting criteria for the automatic feature selection of the most important features across a grid of models or stacked ensembles, eliminating the need for arbitrary determination of the number of top features to be extracted. This utility is invaluable for researchers analyzing feature significance, particularly within severely imbalanced outcomes where conventional methods fall short. Moreover, it is also expected to report democratic feature importance across a grid of models, resulting in a more comprehensive and generalizable feature selection. The package further implements a novel method for visualizing SHAP values both at subject level and feature level as well as a plot for feature selection based on the weighted mean SHAP ratios.

r-simcomp 3.6
Propagated dependencies: r-mvtnorm@1.3-3 r-multcomp@1.4-29 r-mratios@1.4.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SimComp
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Simultaneous Comparisons for Multiple Endpoints
Description:

Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).

r-sglg 0.2.6
Propagated dependencies: r-teachingsampling@4.1.1 r-survival@3.8-3 r-rcpp@1.1.0 r-progress@1.2.3 r-pracma@2.4.6 r-plotly@4.11.0 r-plot3d@1.4.2 r-moments@0.14.1 r-magrittr@2.0.4 r-gridextra@2.3 r-ggplot2@4.0.1 r-formula@1.2-5 r-adequacymodel@2.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sglg
Licenses: GPL 3
Build system: r
Synopsis: Fitting Semi-Parametric Generalized log-Gamma Regression Models
Description:

Set of tools to fit a linear multiple or semi-parametric regression models with the possibility of non-informative random right or left censoring. Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression or semi-parametric functions, whose non-parametric components may be approximated by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes the generalized extreme value and standard normal distributions as important special cases. Inference is based on likelihood, penalized likelihood and bootstrap methods. Lastly, some numerical and graphical devices for diagnostic of the fitted models are offered.

r-swcrtdesign 4.1
Propagated dependencies: r-lmertest@3.1-3 r-lme4@1.1-37 r-glmmtmb@1.1.13
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=swCRTdesign
Licenses: GPL 2
Build system: r
Synopsis: Stepped Wedge Cluster Randomized Trial (SW CRT) Design
Description:

This package provides a set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional or cohort sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191).

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