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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
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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-brolgar 1.0.2
Propagated dependencies: r-vctrs@0.6.5 r-tsibble@1.1.6 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-fabletools@0.5.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/njtierney/brolgar
Licenses: Expat
Build system: r
Synopsis: Browse Over Longitudinal Data Graphically and Analytically in R
Description:

This package provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the tsibble package, and is designed to integrate within the tidyverse', and tidyverts (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in ggplot2 to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.

r-binomialtrend 0.0.0.3
Propagated dependencies: r-pheatmap@1.0.13
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=binomialtrend
Licenses: GPL 3
Build system: r
Synopsis: Calculates the Statistical Significance of a Trend in a Set of Measurements
Description:

Detection of a statistically significant trend in the data provided by the user. This is based on the a signed test based on the binomial distribution. The package returns a trend test value, T, and also a p-value. A T value close to 1 indicates a rising trend, whereas a T value close to -1 indicates a decreasing trend. A T value close to 0 indicates no trend. There is also a command to visualize the trend. A test data set called gtsa_data is also available, which has global mean temperatures for January, April, July, and October for the years 1851 to 2022. Reference: Walpole, Myers, Myers, Ye. (2007, ISBN: 0-13-187711-9).

r-bmstdr 0.8.2
Propagated dependencies: r-stanheaders@2.32.10 r-sptimer@3.3.3 r-sptdyn@2.0.3 r-spbayes@0.4-8 r-rstantools@2.5.0 r-rstan@2.32.7 r-rdpack@2.6.4 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-mnormt@2.1.1 r-mcmcpack@1.7-1 r-inlabru@2.13.0 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-carbayesst@4.0 r-carbayes@6.1.1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://www.sujitsahu.com
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Modeling of Spatio-Temporal Data with R
Description:

Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: rstan', INLA', spBayes', spTimer', spTDyn', CARBayes and CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.

r-bgw 0.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bgw
Licenses: GPL 3
Build system: r
Synopsis: Bunch-Gay-Welsch Statistical Estimation
Description:

This package performs statistical estimation and inference-related computations by accessing and executing modified versions of Fortran subroutines originally published in the Association for Computing Machinery (ACM) journal Transactions on Mathematical Software (TOMS) by Bunch, Gay and Welsch (1993) <doi:10.1145/151271.151279>. The acronym BGW (from the authors last names) will be used when making reference to technical content (e.g., algorithm, methodology) that originally appeared in ACM TOMS. A key feature of BGW is that it exploits the special structure of statistical estimation problems within a trust-region-based optimization approach to produce an estimation algorithm that is much more effective than the usual practice of using optimization methods and codes originally developed for general optimization. The bgw package bundles R wrapper (and related) functions with modified Fortran source code so that it can be compiled and linked in the R environment for fast execution. This version implements a function ('bgw_mle.R') that performs maximum likelihood estimation (MLE) for a user-provided model object that computes probabilities (a.k.a. probability densities). The original motivation for producing this package was to provide fast, efficient, and reliable MLE for discrete choice models that can be called from the Apollo choice modelling R package ( see <https://www.apollochoicemodelling.com>). Starting with the release of Apollo 3.0, BGW is the default estimation package. However, estimation can also be performed using BGW in a stand-alone fashion without using Apollo (as shown in simple examples included in the package). Note also that BGW capabilities are not limited to MLE, and future extension to other estimators (e.g., nonlinear least squares, generalized method of moments, etc.) is possible. The Fortran code included in bgw was modified by one of the original BGW authors (Bunch) under his rights as confirmed by direct consultation with the ACM Intellectual Property and Rights Manager. See <https://authors.acm.org/author-resources/author-rights>. The main requirement is clear citation of the original publication (see above).

r-bamp 2.1.3
Propagated dependencies: r-coda@0.19-4.1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://volkerschmid.github.io/bamp/
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Age-Period-Cohort Modeling and Prediction
Description:

Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.

r-bayesroe 0.2
Propagated dependencies: r-shinybs@0.61.1 r-shiny@1.11.1 r-scales@1.4.0 r-golem@0.5.1 r-ggplot2@4.0.1 r-config@0.3.2 r-colourpicker@1.3.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/waidschrat/bayesROE
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Regions of Evidence
Description:

Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (<https://osf.io/jxnsv>). Besides generic functions, the package also provides an intuitive Shiny application, that can be run in local R environments.

r-bondvaluation 0.1.1
Propagated dependencies: r-timedate@4051.111 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BondValuation
Licenses: GPL 3
Build system: r
Synopsis: Fixed Coupon Bond Valuation Allowing for Odd Coupon Periods and Various Day Count Conventions
Description:

Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond.

r-bunsen 0.1.0
Propagated dependencies: r-survival@3.8-3 r-rcpp@1.1.0 r-clustermq@0.9.9 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bunsen
Licenses: GPL 3+
Build system: r
Synopsis: Marginal Survival Estimation with Covariate Adjustment
Description:

This package provides an efficient and robust implementation for estimating marginal Hazard Ratio (HR) and Restricted Mean Survival Time (RMST) with covariate adjustment using Daniel et al. (2021) <doi:10.1002/bimj.201900297> and Karrison et al. (2018) <doi:10.1177/1740774518759281>.

r-bivregbls 1.1.1
Propagated dependencies: r-ellipse@0.5.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BivRegBLS
Licenses: AGPL 3
Build system: r
Synopsis: Tolerance Interval and EIV Regression - Method Comparison Studies
Description:

Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables (EIV) regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) <doi:10.1002/sim.6872>, B.G. Francq, B. Govaerts (2014) <doi:10.1016/j.chemolab.2014.03.006>, B.G. Francq, B. Govaerts (2014) <http://publications-sfds.fr/index.php/J-SFdS/article/view/262>, B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, <https://dial.uclouvain.be/pr/boreal/object/boreal%3A135862/datastream/PDF_01/view>.

r-brucer 2026.1
Propagated dependencies: r-tidyr@1.3.1 r-texreg@1.39.5 r-stringr@1.6.0 r-rstudioapi@0.17.1 r-rio@1.2.4 r-psych@2.5.6 r-plyr@1.8.9 r-mediation@4.5.1 r-lavaan@0.6-20 r-jtools@2.3.1 r-interactions@1.2.0 r-ggplot2@4.0.1 r-emmeans@2.0.0 r-effectsize@1.0.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-crayon@1.5.3 r-afex@1.5-0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://psychbruce.github.io/bruceR/
Licenses: GPL 3
Build system: r
Synopsis: Broadly Useful Convenient and Efficient R Functions
Description:

Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.

r-bodsr 0.1.0
Propagated dependencies: r-xml2@1.5.0 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bodsr
Licenses: Expat
Build system: r
Synopsis: Call the Bus Open Data Service ('BODS') API Through R
Description:

This package provides a wrapper to allow users to download Bus Open Data Service BODS transport information from the API (<https://www.bus-data.dft.gov.uk/>). This includes timetable and fare metadata (including links for full datasets), timetable data at line level, and real-time location data.

r-biogram 1.6.3
Propagated dependencies: r-slam@0.1-55 r-partitions@1.10-9 r-entropy@1.3.2 r-combinat@0.0-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/michbur/biogram
Licenses: GPL 3
Build system: r
Synopsis: N-Gram Analysis of Biological Sequences
Description:

This package provides tools for extraction and analysis of various n-grams (k-mers) derived from biological sequences (proteins or nucleic acids). Contains QuiPT (quick permutation test) for fast feature-filtering of the n-gram data.

r-bcfrailphdv 0.1.2
Propagated dependencies: r-survival@3.8-3 r-bcfrailph@0.1.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bcfrailphdv
Licenses: GPL 2+
Build system: r
Synopsis: Bivariate Correlated Frailty Models with Varied Variances
Description:

Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Kifle et al (2023) <DOI: 10.4310/22-SII738> with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach.

r-bambi 2.3.6
Propagated dependencies: r-scales@1.4.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-qrng@0.0-11 r-numderiv@2016.8-1.1 r-mvtnorm@1.3-3 r-loo@2.8.0 r-lattice@0.22-7 r-label-switching@1.8 r-gtools@3.9.5 r-future-apply@1.20.0 r-coda@0.19-4.1 r-bridgesampling@1.2-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://doi.org/10.18637/jss.v099.i11
Licenses: GPL 3
Build system: r
Synopsis: Bivariate Angular Mixture Models
Description:

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.

r-boardgames 1.0.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BoardGames
Licenses: GPL 2+
Build system: r
Synopsis: Board Games and Tools for Building Board Games
Description:

This package provides tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.

r-beast 1.2
Propagated dependencies: r-rcolorbrewer@1.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=beast
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series
Description:

Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2019) <doi:10.1515/ijb-2018-0052> for a detailed presentation of the method.

r-bets-covid19 1.0.0
Propagated dependencies: r-rootsolve@1.8.2.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/qingyuanzhao/bets.covid19
Licenses: FSDG-compatible
Build system: r
Synopsis: The BETS Model for Early Epidemic Data
Description:

This package implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.

r-brmsmargins 0.2.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-posterior@1.6.1 r-extraoperators@0.3.0 r-data-table@1.17.8 r-brms@2.23.0 r-bayestestr@0.17.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://joshuawiley.com/brmsmargins/
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Marginal Effects for 'brms' Models
Description:

Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the brms package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).

r-bacistool 1.0.0
Dependencies: jags@4.3.1
Propagated dependencies: r-rjags@4-17
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bacistool
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Classification and Information Sharing (BaCIS) Tool for the Design of Multi-Group Phase II Clinical Trials
Description:

This package provides the design of multi-group phase II clinical trials with binary outcomes using the hierarchical Bayesian classification and information sharing (BaCIS) model. Subgroups are classified into two clusters on the basis of their outcomes mimicking the hypothesis testing framework. Subsequently, information sharing takes place within subgroups in the same cluster, rather than across all subgroups. This method can be applied to the design and analysis of multi-group clinical trials with binary outcomes. Reference: Nan Chen and J. Jack Lee (2019) <doi:10.1002/bimj.201700275>.

r-btyd 2.4.3
Propagated dependencies: r-optimx@2025-4.9 r-matrix@1.7-4 r-hypergeo@1.2-14 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BTYD
Licenses: GPL 3
Build system: r
Synopsis: Implementing BTYD Models with the Log Sum Exp Patch
Description:

This package provides functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.

r-bgphazard 2.1.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-survival@3.8-3 r-stringr@1.6.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-progress@1.2.3 r-magrittr@2.0.4 r-gridextra@2.3 r-ggthemes@5.1.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-brobdingnag@1.2-9
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/EAMI91/BGPhazard
Licenses: GPL 2+
Build system: r
Synopsis: Markov Beta and Gamma Processes for Modeling Hazard Rates
Description:

Computes the hazard rate estimate as described by Nieto-Barajas & Walker (2002), Nieto-Barajas (2003), Nieto-Barajas & Walker (2007) and Nieto-Barajas & Yin (2008).

r-brinda 0.1.5
Propagated dependencies: r-rlang@1.1.6 r-hmisc@5.2-4 r-dplyr@1.1.4 r-data-table@1.17.8 r-berryfunctions@1.22.13
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/hanqiluo/BRINDA
Licenses: FSDG-compatible
Build system: r
Synopsis: Computation of BRINDA Adjusted Micronutrient Biomarkers for Inflammation
Description:

Inflammation can affect many micronutrient biomarkers and can thus lead to incorrect diagnosis of individuals and to over- or under-estimate the prevalence of deficiency in a population. Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) is a multi-agency and multi-country partnership designed to improve the interpretation of nutrient biomarkers in settings of inflammation and to generate context-specific estimates of risk factors for anemia (Suchdev (2016) <doi:10.3945/an.115.010215>). In the past few years, BRINDA published a series of papers to provide guidance on how to adjust micronutrient biomarkers, retinol binding protein, serum retinol, serum ferritin by Namaste (2020), soluble transferrin receptor (sTfR), serum zinc, serum and Red Blood Cell (RBC) folate, and serum B-12, using inflammation markers, alpha-1-acid glycoprotein (AGP) and/or C-Reactive Protein (CRP) by Namaste (2020) <doi:10.1093/ajcn/nqaa141>, Rohner (2017) <doi:10.3945/ajcn.116.142232>, McDonald (2020) <doi:10.1093/ajcn/nqz304>, and Young (2020) <doi:10.1093/ajcn/nqz303>. The BRINDA inflammation adjustment method mainly focuses on Women of Reproductive Age (WRA) and Preschool-age Children (PSC); however, the general principle of the BRINDA method might apply to other population groups. The BRINDA R package is a user-friendly all-in-one R package that uses a series of functions to implement BRINDA adjustment method, as described above. The BRINDA R package will first carry out rigorous checks and provides users guidance to correct data or input errors (if they occur) prior to inflammation adjustments. After no errors are detected, the package implements the BRINDA inflammation adjustment for up to five micronutrient biomarkers, namely retinol-binding-protein, serum retinol, serum ferritin, sTfR, and serum zinc (when appropriate), using inflammation indicators of AGP and/or CRP for various population groups. Of note, adjustment for serum and RBC folate and serum B-12 is not included in the R package, since evidence shows that no adjustment is needed for these micronutrient biomarkers in either WRA or PSC groups (Young (2020) <doi:10.1093/ajcn/nqz303>).

r-bayesdecon 0.1.4
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-msm@1.8.2 r-ks@1.15.1 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesDecon
Licenses: GPL 2+
Build system: r
Synopsis: Density Deconvolution Using Bayesian Semiparametric Methods
Description:

Estimates the density of a variable in a measurement error setup, potentially with an excess of zero values. For more details see Sarkar (2022) <doi:10.1080/01621459.2020.1782220>.

r-bimodalindex 1.1.11
Propagated dependencies: r-oompabase@3.2.10 r-mclust@6.1.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://oompa.r-forge.r-project.org/
Licenses: ASL 2.0
Build system: r
Synopsis: The Bimodality Index
Description:

Defines the functions used to compute the bimodal index as defined by Wang et al. (2009) <https://pmc.ncbi.nlm.nih.gov/articles/PMC2730180/>, <doi:10.4137/CIN.S2846>.

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Total results: 68658