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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-consrq 1.0
Propagated dependencies: r-rfast@2.1.5.2 r-quantreg@6.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=consrq
Licenses: GPL 2+
Build system: r
Synopsis: Constrained Quantile Regression
Description:

Constrained quantile regression is performed. One constraint is that all beta coefficients (including the constant) cannot be negative, they can be either 0 or strictly positive. Another constraint is that the beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. <doi:10.1017/CBO9780511754098>.

r-covidmx 0.7.7
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-readxl@1.4.5 r-readr@2.1.6 r-rcurl@1.98-1.17 r-pins@1.4.1 r-fs@1.6.6 r-duckdb@1.4.2 r-dplyr@1.1.4 r-dbi@1.2.3 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/RodrigoZepeda/covidmx
Licenses: Expat
Build system: r
Synopsis: Descarga y analiza datos de COVID-19 en México
Description:

Herramientas para el análisis de datos de COVID-19 en México. Descarga y analiza los datos para COVID-19 de la Direccion General de Epidemiologà a de México (DGE) <https://www.gob.mx/salud/documentos/datos-abiertos-152127>, la Red de Infecciones Respiratorias Agudas Graves (Red IRAG) <https://www.gits.igg.unam.mx/red-irag-dashboard/reviewHome> y la Iniciativa Global para compartir todos los datos de influenza (GISAID) <https://gisaid.org/>. English: Downloads and analyzes data of COVID-19 from the Mexican General Directorate of Epidemiology (DGE), the Network of Severe Acute Respiratory Infections (IRAG network),and the Global Initiative on Sharing All Influenza Data GISAID.

r-cdmtools 1.0.6
Propagated dependencies: r-sirt@4.2-133 r-psych@2.5.6 r-plyr@1.8.9 r-gparotation@2025.3-1 r-ggplot2@4.0.1 r-gdina@2.9.12 r-fungible@2.4.4.1 r-foreach@1.5.2 r-dosnow@1.0.20 r-combinat@0.0-8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/pablo-najera/cdmTools
Licenses: GPL 3
Build system: r
Synopsis: Useful Tools for Cognitive Diagnosis Modeling
Description:

This package provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.

r-comfuncs 0.0.6
Propagated dependencies: r-shiny@1.11.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/TuCai/comFuncs
Licenses: Expat
Build system: r
Synopsis: Commonly Used Functions for R Shiny Applications
Description:

This package provides a set of common functions to be used for displaying messages, checking variables, finding absolute paths, starting applications, etc. More functions will be added later.

r-cotram 0.6-0
Propagated dependencies: r-variables@1.1-2 r-tram@1.3-2 r-survival@3.8-3 r-qrng@0.0-11 r-mlt@1.7-3 r-basefun@1.2-5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: http://ctm.R-forge.R-project.org
Licenses: GPL 2
Build system: r
Synopsis: Count Transformation Models
Description:

Count transformation models featuring parameters interpretable as discrete hazard ratios, odds ratios, reverse-time discrete hazard ratios, or transformed expectations. An appropriate data transformation for a count outcome and regression coefficients are simultaneously estimated by maximising the exact discrete log-likelihood using the computational framework provided in package mlt', technical details are given in Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>. The package also contains an experimental implementation of multivariate count transformation models with an application to multi-species distribution models <DOI:10.48550/arXiv.2201.13095>.

r-caramel 1.4
Propagated dependencies: r-geometry@0.5.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/fzao/caRamel
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Automatic Calibration by Evolutionary Multi Objective Algorithm
Description:

The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets that are optimal with regard to a multi-objective target (Monteil et al. <doi:10.5194/hess-24-3189-2020>).

r-cpmr 0.1.0
Propagated dependencies: r-tibble@3.3.0 r-rfast@2.1.5.2 r-generics@0.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/psychelzh/cpmr
Licenses: Expat
Build system: r
Synopsis: Connectome Predictive Modelling in R
Description:

Connectome Predictive Modelling (CPM) (Shen et al. (2017) <doi:10.1038/nprot.2016.178>) is a method to predict individual differences in behaviour from brain functional connectivity. cpmr provides a simple yet efficient implementation of this method.

r-calibratessb 1.3.0
Propagated dependencies: r-survey@4.4-8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/statisticsnorway/CalibrateSSB
Licenses: GPL 2
Build system: r
Synopsis: Weighting and Estimation for Panel Data with Non-Response
Description:

This package provides functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the survey package is used for this purpose. It is also possible to use ReGenesees, which can be installed from <https://github.com/DiegoZardetto/ReGenesees>. Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) <http://www.revistadestatistica.ro/wp-content/uploads/2016/06/RRS2_2016_A021.pdf>.

r-commecometrics 1.0.1
Propagated dependencies: r-viridis@0.6.5 r-tibble@3.3.0 r-sf@1.0-23 r-rnaturalearth@1.1.0 r-raster@3.6-32 r-purrr@1.2.0 r-leaflet@2.2.3 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/mariahm1995/commecometrics
Licenses: Expat
Build system: r
Synopsis: Ecometric Models of Trait–Environment Relationships at the Community Level
Description:

This package provides a framework for modeling relationships between functional traits and both quantitative and qualitative environmental variables at the community level. It includes tools for trait binning, likelihood-based environmental estimation, model evaluation, fossil projection into modern ecometric space, and result visualization. For more details see Vermillion et al. (2018) <doi:10.1007/978-3-319-94265-0_17>, Polly et al. (2011) <doi:10.1098/rspb.2010.2233> and Polly and Head (2015) <doi:10.1017/S1089332600002953>.

r-consensusopls 1.1.0
Propagated dependencies: r-reshape2@1.4.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ConsensusOPLS
Licenses: GPL 3+
Build system: r
Synopsis: Consensus OPLS for Multi-Block Data Fusion
Description:

Merging data from multiple sources is a relevant approach for comprehensively evaluating complex systems. However, the inherent problems encountered when analyzing single tables are amplified with the generation of multi-block datasets, and finding the relationships between data layers of increasing complexity constitutes a challenging task. For that purpose, a generic methodology is proposed by combining the strength of established data analysis strategies, i.e. multi-block approaches and the Orthogonal Partial Least Squares (OPLS) framework to provide an efficient tool for the fusion of data obtained from multiple sources. The package enables quick and efficient implementation of the consensus OPLS model for any horizontal multi-block data structures (observation-based matching). Moreover, it offers an interesting range of metrics and graphics to help to determine the optimal number of components and check the validity of the model through permutation tests. Interpretation tools include score and loading plots, Variable Importance in Projection (VIP), functionality predict for SHAP computing, and performance coefficients such as R2, Q2, and DQ2 coefficients. J. Boccard and D.N. Rutledge (2013) <doi:10.1016/j.aca.2013.01.022>.

r-cppdoubles 0.4.0
Propagated dependencies: r-cpp11@0.5.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cppdoubles
Licenses: Expat
Build system: r
Synopsis: Fast Relative Comparisons of Floating Point Numbers in 'C++'
Description:

Compare double-precision floating point vectors using relative differences. All equality operations are calculated using cpp11'.

r-cprobit 1.0.2
Propagated dependencies: r-nortest@1.0-4 r-gridextra@2.3 r-ggplot2@4.0.1 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cprobit
Licenses: LGPL 3
Build system: r
Synopsis: Conditional Probit Model for Analysing Continuous Outcomes
Description:

This package implements the three-step workflow for robust analysis of change in two repeated measurements of continuous outcomes, described in Ning et al. (in press), "Robust estimation of the effect of an exposure on the change in a continuous outcome", BMC Medical Research Methodology.

r-carat 2.2.1
Propagated dependencies: r-stringr@1.6.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-gridextra@2.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=carat
Licenses: GPL 2+
Build system: r
Synopsis: Covariate-Adaptive Randomization for Clinical Trials
Description:

This package provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) <doi: 10.18637/jss.v107.i02> for details.

r-covid19italy 0.3.1
Propagated dependencies: r-devtools@2.4.6
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/RamiKrispin/covid19italy
Licenses: Expat
Build system: r
Synopsis: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset
Description:

This package provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile <https://www.protezionecivile.it/>.

r-countdata 1.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=countdata
Licenses: Modified BSD
Build system: r
Synopsis: The Beta-Binomial Test for Count Data
Description:

The beta-binomial test is used for significance analysis of independent samples by Pham et al. (2010) <doi:10.1093/bioinformatics/btp677>. The inverted beta-binomial test is used for paired sample testing, e.g. pre-treatment and post-treatment data, by Pham and Jimenez (2012) <doi:10.1093/bioinformatics/bts394>.

r-compound-cox 3.33
Propagated dependencies: r-survival@3.8-3 r-numderiv@2016.8-1.1 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=compound.Cox
Licenses: GPL 2
Build system: r
Synopsis: Univariate Feature Selection and Compound Covariate for Predicting Survival, Including Copula-Based Analyses for Dependent Censoring
Description:

Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al (2019)<DOI:10.1016/j.cmpb.2018.10.020>. Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023 Biomedicines) <DOI:10.3390/biomedicines11030797> and factorial survival analyses (Emura et al 2024 Stat Methods Med Res) <DOI:10.1177/09622802231215805>.

r-cmsafops 1.4.3
Propagated dependencies: r-trend@1.1.6 r-searchtrees@0.5.5 r-raster@3.6-32 r-rainfarmr@0.1 r-progress@1.2.3 r-ncdf4@1.24 r-fnn@1.1.4.1 r-fields@17.1 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://www.cmsaf.eu
Licenses: GPL 3+
Build system: r
Synopsis: Tools for CM SAF NetCDF Data
Description:

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsafops R-package provides a collection of R-operators for the analysis and manipulation of CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

r-cquad 2.3
Propagated dependencies: r-plm@2.6-7 r-mass@7.3-65 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cquad
Licenses: GPL 2+
Build system: r
Synopsis: Conditional Maximum Likelihood for Quadratic Exponential Models for Binary Panel Data
Description:

Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>. For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics <DOI:10.1007/s10614-021-10218-2>.

r-cfo 2.2.0
Propagated dependencies: r-survival@3.8-3 r-scales@1.4.0 r-rcolorbrewer@1.1-3 r-pbapply@1.7-4 r-iso@0.0-21 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://clinicaltrialdesign.shinyapps.io/cfoapp
Licenses: GPL 2
Build system: r
Synopsis: CFO-Type Designs in Phase I/II Clinical Trials
Description:

In phase I clinical trials, the primary objective is to ascertain the maximum tolerated dose (MTD) corresponding to a specified target toxicity rate. The subsequent phase II trials are designed to examine the potential efficacy of the drug based on the MTD obtained from the phase I trials, with the aim of identifying the optimal biological dose (OBD). The CFO package facilitates the implementation of dose-finding trials by utilizing calibration-free odds type (CFO-type) designs. Specifically, it encompasses the calibration-free odds (CFO) (Jin and Yin (2022) <doi:10.1177/09622802221079353>), randomized CFO (rCFO), precision CFO (pCFO), two-dimensional CFO (2dCFO) (Wang et al. (2023) <doi:10.3389/fonc.2023.1294258>), time-to-event CFO (TITE-CFO) (Jin and Yin (2023) <doi:10.1002/pst.2304>), fractional CFO (fCFO), accumulative CFO (aCFO), TITE-aCFO, and f-aCFO (Fang and Yin (2024) <doi: 10.1002/sim.10127>). It supports phase I/II trials for the CFO design and only phase I trials for the other CFO-type designs. The â CFO package accommodates diverse CFO-type designs, allowing users to tailor the approach based on factors such as dose information inclusion, handling of late-onset toxicity, and the nature of the target drug (single-drug or drug-combination). The functionalities embedded in CFO package include the determination of the dose level for the next cohort, the selection of the MTD for a real trial, and the execution of single or multiple simulations to obtain operating characteristics. Moreover, these functions are equipped with early stopping and dose elimination rules to address safety considerations. Users have the flexibility to choose different distributions, thresholds, and cohort sizes among others for their specific needs. The output of the CFO package can be summary statistics as well as various plots for better visualization. An interactive web application for CFO is available at the provided URL.

r-crabs 1.2.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-pracma@2.4.6 r-patchwork@1.3.2 r-magrittr@2.0.4 r-latex2exp@0.9.6 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-desolve@1.40 r-colorspace@2.1-2 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/afmagee/CRABS
Licenses: GPL 3
Build system: r
Synopsis: Congruent Rate Analyses in Birth-Death Scenarios
Description:

Features tools for exploring congruent phylogenetic birth-death models. It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) <doi:10.1038/s41586-020-2176-1>.

r-catdyn 1.1-1
Propagated dependencies: r-optimx@2025-4.9 r-bb@2019.10-1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CatDyn
Licenses: GPL 2+
Build system: r
Synopsis: Fishery Stock Assessment by Catch Dynamics Models
Description:

Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data.

r-cutoff 1.3
Propagated dependencies: r-survival@3.8-3 r-set@1.2 r-rocit@2.1.2 r-do@2.0.0.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/yikeshu0611/cutoff
Licenses: GPL 3
Build system: r
Synopsis: Seek the Significant Cutoff Value
Description:

Seek the significant cutoff value for a continuous variable, which will be transformed into a classification, for linear regression, logistic regression, logrank analysis and cox regression. First of all, all combinations will be gotten by combn() function. Then n.per argument, abbreviated of total number percentage, will be used to remove the combination of smaller data group. In logistic, Cox regression and logrank analysis, we will also use p.per argument, patient percentage, to filter the lower proportion of patients in each group. Finally, p value in regression results will be used to get the significant combinations and output relevant parameters. In this package, there is no limit to the number of cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods, typical Bonferroni and Duglas G (1994) <doi: 10.1093/jnci/86.11.829>, to adjust the p value, Missing values will be deleted by na.omit() function before analysis.

r-cgauc 1.2.1
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cgAUC
Licenses: GPL 2+
Build system: r
Synopsis: Calculate AUC-type measure when gold standard is continuous and the corresponding optimal linear combination of variables with respect to it
Description:

The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.

r-customiser 0.1.1
Propagated dependencies: r-withr@3.0.2 r-rmarkdown@2.30 r-rlang@1.1.6 r-knitr@1.50 r-fs@1.6.6
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/jameslairdsmith/customiser
Licenses: Expat
Build system: r
Synopsis: Use R Markdown to Write your "Rprofile"
Description:

This package provides a simple way to write ".Rprofile" code in an R Markdown file and have it knit to the correct location for your operating system.

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