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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-snreg 1.2.0
Propagated dependencies: r-npsf@0.8.0 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://olegbadunenko.github.io/snreg/
Licenses: GPL 3
Build system: r
Synopsis: Regression with Skew-Normally Distributed Error Term
Description:

Models with skewâ normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <doi:10.1007/s11123-023-00680-5>. The package provides tools to estimate regression models with skewâ normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.

r-spectralmap 1.0
Propagated dependencies: r-scatterplot3d@0.3-44 r-fields@17.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SpectralMap
Licenses: GPL 2
Build system: r
Synopsis: Diffusion Map and Spectral Map
Description:

This package implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.

r-selectiongain 2.0.710
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=selectiongain
Licenses: GPL 2
Build system: r
Synopsis: Tool for Calculation and Optimization of the Expected Gain from Multi-Stage Selection
Description:

Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.

r-survidm 1.3.2
Propagated dependencies: r-tpmsm@1.2.14 r-survival@3.8-3 r-rcpp@1.1.0 r-plotly@4.11.0 r-np@0.60-18 r-kernsmooth@2.23-26 r-gridextra@2.3 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dorng@1.8.6.2 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=survidm
Licenses: GPL 3
Build system: r
Synopsis: Inference and Prediction in an Illness-Death Model
Description:

Newly developed methods for the estimation of several probabilities in an illness-death model. The package can be used to obtain nonparametric and semiparametric estimates for: transition probabilities, occupation probabilities, cumulative incidence function and the sojourn time distributions. Additionally, it is possible to fit proportional hazards regression models in each transition of the Illness-Death Model. Several auxiliary functions are also provided which can be used for marginal estimation of the survival functions.

r-saeme 1.3.1
Propagated dependencies: 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=saeME
Licenses: GPL 2
Build system: r
Synopsis: Small Area Estimation with Measurement Error
Description:

This package provides a set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.

r-staticryptr 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=staticryptR
Licenses: Expat
Build system: r
Synopsis: Encrypt HTML Files Using 'staticrypt'
Description:

This package provides a convenient interface to the staticrypt by Robin Moisson <https://github.com/robinmoisson/staticrypt>---'Node.js package for adding a password protection layer to static HTML pages. This package can be integrated into the post-render process of quarto documents to secure them with a password.

r-smcrm 0.0-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SMCRM
Licenses: GPL 3
Build system: r
Synopsis: Data Sets for Statistical Methods in Customer Relationship Management by Kumar and Petersen (2012)
Description:

Data Sets for Kumar and Petersen (2012). Statistical Methods in Customer Relationship Management, Wiley: New York.

r-semiartificial 2.4.1
Propagated dependencies: r-timedate@4051.111 r-statmatch@1.4.3 r-rsnns@0.4-18 r-robustbase@0.99-6 r-nnet@7.3-20 r-mcclust@1.0.1 r-mass@7.3-65 r-logspline@2.1.22 r-ks@1.15.1 r-fpc@2.2-13 r-flexclust@1.5.0 r-corelearn@1.57.3.1 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://lkm.fri.uni-lj.si/rmarko/software/
Licenses: GPL 3
Build system: r
Synopsis: Generator of Semi-Artificial Data
Description:

This package contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests.

r-stratest 1.1.8
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/fdvorak/stratEst
Licenses: GPL 3
Build system: r
Synopsis: Strategy Estimation
Description:

Variants of strategy estimation (Dal Bo & Frechette, 2011, <doi:10.1257/aer.101.1.411>), including the model with parameters for the choice probabilities of the strategies (Breitmoser, 2015, <doi:10.1257/aer.20130675>), and the model with individual level covariates for the selection of strategies by individuals (Dvorak & Fehrler, 2018, <doi:10.2139/ssrn.2986445>).

r-ssbtools 1.8.6
Propagated dependencies: r-matrix@1.7-4 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/statisticsnorway/ssb-ssbtools
Licenses: Expat
Build system: r
Synopsis: Algorithms and Tools for Tabular Statistics and Hierarchical Computations
Description:

Includes general data manipulation functions, algorithms for statistical disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and functions for hierarchical computations by sparse model matrices (Langsrud, 2023) <doi:10.32614/RJ-2023-088>.

r-sommd 0.1.2
Propagated dependencies: r-kohonen@3.0.12 r-igraph@2.2.1 r-cluster@2.1.8.1 r-bio3d@2.4-5 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SOMMD
Licenses: GPL 3
Build system: r
Synopsis: Self Organising Maps for the Analysis of Molecular Dynamics Data
Description:

Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) <doi:10.1021/acs.jctc.1c01163>. I/O functions for xtc format files were implemented using the xdrfile library available under open source license. The relevant information can be found in inst/COPYRIGHT.

r-susier 0.14.2
Propagated dependencies: r-reshape@0.8.10 r-mixsqp@0.3-54 r-matrixstats@1.5.0 r-matrix@1.7-4 r-ggplot2@4.0.1 r-crayon@1.5.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/stephenslab/susieR
Licenses: Modified BSD
Build system: r
Synopsis: Sum of Single Effects Linear Regression
Description:

This package implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables).

r-segmag 1.2.4
Propagated dependencies: r-rcpp@1.1.0 r-plyr@1.8.9
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=segmag
Licenses: GPL 3+
Build system: r
Synopsis: Determine Event Boundaries in Event Segmentation Experiments
Description:

This package contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results.

r-saqgetr 0.2.21
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/skgrange/saqgetr
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Import Air Quality Monitoring Data in a Fast and Easy Way
Description:

This package provides a collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. Air quality data are sourced from open and publicly accessible repositories and can be found in these locations: <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> and <https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm>. The web server space has been provided by Ricardo Energy & Environment.

r-streg 1.1
Propagated dependencies: r-tseries@0.10-58 r-numderiv@2016.8-1.1 r-mcmcpack@1.7-1 r-matlab@1.0.4.1 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=StReg
Licenses: GPL 2
Build system: r
Synopsis: Student's t Regression Models
Description:

It contains functions to estimate multivariate Student's t dynamic and static regression models for given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form. Poudyal, N., and Spanos, A. (2022) <doi:10.3390/econometrics10020017>. Spanos, A. (1994) <http://www.jstor.org/stable/3532870>.

r-scalablebayesm 0.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-bayesm@3.1-7
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scalablebayesm
Licenses: GPL 2+
Build system: r
Synopsis: Distributed Markov Chain Monte Carlo for Bayesian Inference in Marketing
Description:

Estimates unit-level and population-level parameters from a hierarchical model in marketing applications. The package includes: Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates. For more details, see Bumbaca, F. (Rico), Misra, S., & Rossi, P. E. (2020) <doi:10.1177/0022243720952410> "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018.

r-simitation 0.0.7
Propagated dependencies: 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=simitation
Licenses: GPL 3
Build system: r
Synopsis: Simplified Simulations
Description:

This package provides tools for generating and analyzing simulation studies. Users may easily specify all terms of a simulation study, often in a single line of code. Common univariate and bivariate methods, such as t tests, proportions tests, and chi squared tests, are integrated. Multivariate studies involving linear or logistic regression may also be specified with symbolic inputs. The simulation studies generate data for n observations in each of B experiments. Analyses of each experiment are integrated, and empirical results across the experiments are also provided.

r-ssaforecast 0.1.1
Propagated dependencies: r-rssa@1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SSAforecast
Licenses: GPL 3
Build system: r
Synopsis: SSA Based Decomposition and Forecasting
Description:

Singular spectrum analysis (SSA) decomposes a time series into interpretable components like trends, oscillations, and noise without strict distributional and structural assumptions. For method details see Golyandina N, Zhigljavsky A (2013). <doi:10.1007/978-3-642-34913-3>.

r-spcalda 1.0
Propagated dependencies: 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=SPCALDA
Licenses: GPL 2
Build system: r
Synopsis: New Reduced-Rank Linear Discriminant Analysis Method
Description:

This package provides a new reduced-rank LDA method which works for high dimensional multi-class data.

r-support-bws3 0.2-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=support.BWS3
Licenses: GPL 2+
Build system: r
Synopsis: Tools for Case 3 Best-Worst Scaling
Description:

This package provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.

r-svenssonm 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=svenssonm
Licenses: GPL 3
Build system: r
Synopsis: Svensson's Method
Description:

Obtain parameters of Svensson's Method, including percentage agreement, systematic change and individual change. Also, the contingency table can be generated. Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales which measures the level of change both from systematic and individual aspects. For the details, please refer to Svensson E. Analysis of systematic and random differences between paired ordinal categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.

r-slos 1.0.1
Propagated dependencies: r-ranger@0.17.0 r-mlmetrics@1.1.3 r-magrittr@2.0.4 r-httr@1.4.7 r-ggplot2@4.0.1 r-ems@1.3.11 r-dplyr@1.1.4 r-caretensemble@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SLOS
Licenses: Expat
Build system: r
Synopsis: ICU Length of Stay Prediction and Efficiency Evaluation
Description:

This package provides tools for predicting ICU length of stay and assessing ICU efficiency. It is based on the methodologies proposed by Peres et al. (2022, 2023), which utilize data-driven approaches for modeling and validation, offering insights into ICU performance and patient outcomes. References: Peres et al. (2022)<https://pubmed.ncbi.nlm.nih.gov/35988701/>, Peres et al. (2023)<https://pubmed.ncbi.nlm.nih.gov/37922007/>. More information: <https://github.com/igor-peres/ICU-Length-of-Stay-Prediction>.

r-simcdm 0.1.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://tmsalab.github.io/simcdm/
Licenses: GPL 2+
Build system: r
Synopsis: Simulate Cognitive Diagnostic Model ('CDM') Data
Description:

This package provides efficient R and C++ routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.

r-simdata 0.4.1
Propagated dependencies: r-mvtnorm@1.3-3 r-matrix@1.7-4 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://matherealize.github.io/simdata/
Licenses: GPL 3
Build system: r
Synopsis: Generate Simulated Datasets
Description:

Generate simulated datasets from an initial underlying distribution and apply transformations to obtain realistic data. Implements the NORTA (Normal-to-anything) approach from Cario and Nelson (1997) and other data generating mechanisms. Simple network visualization tools are provided to facilitate communicating the simulation setup.

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