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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-panelsur 0.1.0
Propagated dependencies: r-plm@2.6-7 r-matlib@1.0.1 r-mass@7.3-65 r-formula-tools@1.7.1 r-fastmatrix@0.6-6
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=panelSUR
Licenses: GPL 3+
Build system: r
Synopsis: Two-Way Error Component SUR Systems Estimation on Unbalanced Panel Data
Description:

Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression (SUR) systems on unbalanced panel in the one/two-way cases also taking into account the possibility of cross equation restrictions. Methodological details can be found in Biørn (2004) <doi:10.1016/j.jeconom.2003.10.023> and Platoni, Sckokai, Moro (2012) <doi:10.1080/07474938.2011.607098>.

r-prop-comb-rr 1.2
Propagated dependencies: r-rootsolve@1.8.2.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=prop.comb.RR
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Analyzing Combination of Proportions and Relative Risk
Description:

Carrying out inferences about any linear combination of proportions and the ratio of two proportions.

r-pnd-heter-cluster 0.1.0
Propagated dependencies: r-xgboost@1.7.11.1 r-tidyverse@2.0.0 r-superlearner@2.0-29 r-ranger@0.17.0 r-purrr@1.2.0 r-origami@1.0.7 r-nnet@7.3-20 r-mvtnorm@1.3-3 r-magrittr@2.0.4 r-glue@1.8.0 r-dplyr@1.1.4 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/xliu12/PND.heter
Licenses: GPL 2
Build system: r
Synopsis: Estimating the Cluster Specific Treatment Effects in Partially Nested Designs
Description:

This package implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.

r-perturbr 0.1.3
Propagated dependencies: r-igraph@2.2.1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=perturbR
Licenses: GPL 2
Build system: r
Synopsis: Random Perturbation of Count Matrices
Description:

The perturbR() function incrementally perturbs network edges (using the rewireR function)and compares the resulting community detection solutions from the rewired networks with the solution found for the original network. These comparisons aid in understanding the stability of the original solution. The package requires symmetric, weighted (specifically, count) matrices/networks.

r-ptwikiwords 0.0.3
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/dfalbel/ptwikiwords
Licenses: Expat
Build system: r
Synopsis: Words Used in Portuguese Wikipedia
Description:

This package contains a dataset of words used in 15.000 randomly extracted pages from the Portuguese Wikipedia (<https://pt.wikipedia.org/>).

r-phylocanvas 0.1.3
Propagated dependencies: r-phylobase@0.8.12 r-htmlwidgets@1.6.4 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/zachcp/phylocanvas
Licenses: Expat
Build system: r
Synopsis: Interactive Phylogenetic Trees Using the 'Phylocanvas' JavaScript Library
Description:

Create and customize interactive phylogenetic trees using the phylocanvas JavaScript library and the htmlwidgets package. These trees can be used directly from the R console, from RStudio', in Shiny apps, and in R Markdown documents. See <http://phylocanvas.org/> for more information on the phylocanvas library.

r-peip 2.2-5
Propagated dependencies: r-rseis@4.2-4 r-pracma@2.4.6 r-matrix@1.7-4 r-geigen@2.3 r-fields@17.1 r-bvls@1.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PEIP
Licenses: GPL 2+
Build system: r
Synopsis: Geophysical Inverse Theory and Optimization
Description:

Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, ART and SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included.

r-psyccleaning 0.1.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://jasonmoy28.github.io/psycCleaning/
Licenses: GPL 3+
Build system: r
Synopsis: Data Cleaning for Psychological Analyses
Description:

Useful for preparing and cleaning data. It includes functions to center data, reverse coding, dummy code and effect code data, and more.

r-profiler 0.3-5
Propagated dependencies: r-reshape@0.8.10 r-rcolorbrewer@1.1-3 r-lavaan@0.6-20 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=profileR
Licenses: GPL 2+
Build system: r
Synopsis: Profile Analysis of Multivariate Data in R
Description:

This package provides a suite of multivariate methods and data visualization tools to implement profile analysis and cross-validation techniques described in Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources. The package includes routines to perform criterion-related profile analysis, profile analysis via multidimensional scaling, moderated profile analysis, profile analysis by group, and a within-person factor model to derive score profiles.

r-pharmaversesdtmjnj 0.0.1
Propagated dependencies: r-pharmaversesdtm@1.4.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=pharmaversesdtmjnj
Licenses: ASL 2.0
Build system: r
Synopsis: J&J Innovative Medicine SDTM Test Data
Description:

This package provides a set of Study Data Tabulation Model (SDTM) datasets constructed by modifying the pharmaversesdtm package to meet J&J Innovative Medicine's standard data structure for Clinical and Statistical Programming.

r-polysat 1.7-7
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/lvclark/polysat/wiki
Licenses: GPL 2
Build system: r
Synopsis: Tools for Polyploid Microsatellite Analysis
Description:

This package provides a collection of tools to handle microsatellite data of any ploidy (and samples of mixed ploidy) where allele copy number is not known in partially heterozygous genotypes. It can import and export data in ABI GeneMapper', Structure', ATetra', Tetrasat'/'Tetra', GenoDive', SPAGeDi', POPDIST', STRand', and binary presence/absence formats. It can calculate pairwise distances between individuals using a stepwise mutation model or infinite alleles model, with or without taking ploidies and allele frequencies into account. These distances can be used for the calculation of clonal diversity statistics or used for further analysis in R. Allelic diversity statistics and Polymorphic Information Content are also available. polysat can assist the user in estimating the ploidy of samples, and it can estimate allele frequencies in populations, calculate pairwise or global differentiation statistics based on those frequencies, and export allele frequencies to SPAGeDi and adegenet'. Functions are also included for assigning alleles to isoloci in cases where one pair of microsatellite primers amplifies alleles from two or more independently segregating isoloci. polysat is described by Clark and Jasieniuk (2011) <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2017) <doi:10.1111/1755-0998.12639>.

r-pkgnet 0.6.0
Propagated dependencies: r-visnetwork@2.1.4 r-rmarkdown@2.30 r-rlang@1.1.6 r-r6@2.6.1 r-magrittr@2.0.4 r-knitr@1.50 r-igraph@2.2.1 r-glue@1.8.0 r-dt@0.34.0 r-data-table@1.17.8 r-covr@3.6.5 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/uptake/pkgnet
Licenses: Modified BSD
Build system: r
Synopsis: Get Network Representation of an R Package
Description:

This package provides tools from the domain of graph theory can be used to quantify the complexity and vulnerability to failure of a software package. That is the guiding philosophy of this package. pkgnet provides tools to analyze the dependencies between functions in an R package and between its imported packages. See the pkgnet website for vignettes and other supplementary information.

r-presmtp 1.1.0
Propagated dependencies: r-survpresmooth@1.1-12 r-mgcv@1.9-4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=presmTP
Licenses: GPL 3
Build system: r
Synopsis: Methods for Transition Probabilities
Description:

This package provides a function for estimating the transition probabilities in an illness-death model. The transition probabilities can be estimated from the unsmoothed landmark estimators developed by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>. Presmoothed estimates can also be obtained through the use of a parametric family of binary regression curves, such as logit, probit or cauchit. The additive logistic regression model and nonparametric regression are also alternatives which have been implemented. The idea behind the presmoothed landmark estimators is to use the presmoothing techniques developed by Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the landmark estimation of the transition probabilities.

r-plgp 1.1-13
Propagated dependencies: r-tgp@2.4-23 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://bobby.gramacy.com/r_packages/plgp/
Licenses: LGPL 2.0+
Build system: r
Synopsis: Particle Learning of Gaussian Processes
Description:

Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) <doi:10.48550/arXiv.0909.5262>. The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos.

r-phyloseqgraphtest 0.1.1
Propagated dependencies: r-phyloseq@1.54.0 r-igraph@2.2.1 r-ggplot2@4.0.1 r-ggnetwork@0.5.14
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/jfukuyama/phyloseqGraphTest
Licenses: CC0
Build system: r
Synopsis: Graph-Based Permutation Tests for Microbiome Data
Description:

This package provides functions for graph-based multiple-sample testing and visualization of microbiome data, in particular data stored in phyloseq objects. The tests are based on those described in Friedman and Rafsky (1979) <http://www.jstor.org/stable/2958919>, and the tests are described in more detail in Callahan et al. (2016) <doi:10.12688/f1000research.8986.1>.

r-password 1.0-1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://enricoschumann.net/R/packages/password/
Licenses: GPL 3
Build system: r
Synopsis: Create Random Passwords
Description:

Create random passwords of letters, numbers and punctuation.

r-pam 2.0.2
Propagated dependencies: r-rlang@1.1.6 r-minpack-lm@1.2-4 r-metrics@0.1.4 r-gridextra@2.3 r-ggthemes@5.1.0 r-ggplot2@4.0.1 r-data-table@1.17.8 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/biotoolbox/pam
Licenses: GPL 3
Build system: r
Synopsis: Fast and Efficient Processing of PAM Data
Description:

Processing Chlorophyll Fluorescence & P700 Absorbance data. Four models are provided for the regression of Pi curves, which can be compared with each other in order to select the most suitable model for the data set. Control plots ensure the successful verification of each regression. Bundled output of alpha, ETRmax, Ik etc. enables fast and reliable further processing of the data.

r-psweight 2.1.2
Propagated dependencies: r-survey@4.4-8 r-superlearner@2.0-29 r-numderiv@2016.8-1.1 r-nnet@7.3-20 r-mass@7.3-65 r-lme4@1.1-37 r-ggplot2@4.0.1 r-gbm@2.2.2
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/thuizhou/PSweight
Licenses: GPL 2+
Build system: r
Synopsis: Propensity Score Weighting for Causal Inference with Observational Studies and Randomized Trials
Description:

Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.

r-postcodesior 0.3.1
Propagated dependencies: r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://docs.ropensci.org/PostcodesioR/
Licenses: GPL 3
Build system: r
Synopsis: API Wrapper Around 'Postcodes.io'
Description:

Free UK geocoding using data from Office for National Statistics. It is using several functions to get information about post codes, outward codes, reverse geocoding, nearest post codes/outward codes, validation, or randomly generate a post code. API wrapper around <https://postcodes.io>.

r-pminternal 0.1.0
Propagated dependencies: r-purrr@1.2.0 r-proc@1.19.0.1 r-pmcalibration@0.2.0 r-pbapply@1.7-4 r-marginaleffects@0.31.0 r-insight@1.4.3 r-dcurves@0.5.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/stephenrho/pminternal
Licenses: GPL 3
Build system: r
Synopsis: Internal Validation of Clinical Prediction Models
Description:

Conduct internal validation of a clinical prediction model for a binary outcome. Produce bias corrected performance metrics (c-statistic, Brier score, calibration intercept/slope) via bootstrap (simple bootstrap, bootstrap optimism, .632 optimism) and cross-validation (CV optimism, CV average). Also includes functions to assess model stability via bootstrap resampling. See Steyerberg et al. (2001) <doi:10.1016/s0895-4356(01)00341-9>; Harrell (2015) <doi:10.1007/978-3-319-19425-7>; Riley and Collins (2023) <doi:10.1002/bimj.202200302>.

r-packmbplsda 0.9.0
Propagated dependencies: r-proc@1.19.0.1 r-mass@7.3-65 r-foreach@1.5.2 r-factominer@2.12 r-doparallel@1.0.17 r-ade4@1.7-23
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=packMBPLSDA
Licenses: GPL 2+
Build system: r
Synopsis: Multi-Block Partial Least Squares Discriminant Analysis
Description:

Several functions are provided to implement a MBPLSDA : components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. The use of this package is described in Brandolini-Bunlon et al (2019. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134).

r-propertee 1.0.4
Propagated dependencies: r-sandwich@3.1-1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/benbhansen-stats/propertee
Licenses: Expat
Build system: r
Synopsis: Standardization-Based Effect Estimation with Optional Prior Covariance Adjustment
Description:

The Prognostic Regression Offsets with Propagation of ERrors (for Treatment Effect Estimation) package facilitates direct adjustment for experiments and observational studies that is compatible with a range of study designs and covariance adjustment strategies. It uses explicit specification of clusters, blocks and treatment allocations to furnish probability of assignment-based weights targeting any of several average treatment effect parameters, and for standard error calculations reflecting these design parameters. For covariance adjustment of its Hajek and (one-way) fixed effects estimates, it enables offsetting the outcome against predictions from a dedicated covariance model, with standard error calculations propagating error as appropriate from the covariance model.

r-ppmr 1.0.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PPMR
Licenses: GPL 3
Build system: r
Synopsis: Probabilistic Two Sample Mendelian Randomization
Description:

Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. It can account for the correlated instruments and the horizontal pleiotropy, and can provide the accurate estimates of both causal effect and horizontal pleiotropy effect as well as the two corresponding p-values. There are two main functions in the PPMR package. One is PMR_individual() for individual level data, the other is PMR_summary() for summary data.

r-pooledpeaks 1.2.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-qpdf@1.4.1 r-pdftools@3.6.0 r-magrittr@2.0.4 r-fragman@1.0.9 r-dplyr@1.1.4 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/kmkuesters/pooledpeaks
Licenses: GPL 3+
Build system: r
Synopsis: Genetic Analysis of Pooled Samples
Description:

Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) <doi:10.1111/j.1541-0420.2008.01010.x>.

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