syncons: Efficient and Low-Cost Construction of Synthetic Communities

Provides functions and a Shiny application to design plate layouts for constructing synthetic communities (SynComs) from a pool of strains using 24-, 96- and 384-well microplates. The method generates all possible strain combinations in a scalable, low-cost workflow suitable for high-throughput experiments. Reference: Tian et al. (2024) &lt;<a href="https://doi.org/10.21769%2FBioProtoc.2405576" target="_top">doi:10.21769/BioProtoc.2405576</a>&gt;.

cran.r-project.org

persuasio: Causal Inference on Persuasion Effects

Provides estimation and inference methods for causal persuasion rates in the potential-outcomes framework of Jun and Lee (2023, Journal of Political Economy) &lt;<a href="https://doi.org/10.1086%2F724114" target="_top">doi:10.1086/724114</a>&gt;. The package computes bounds and confidence intervals for average and local persuasion rates under data scenarios with binary outcomes, treatments, and instruments, and also when only the outcome and instrument are observed. It also provides functions for calculating bounds from summary statistics.

cran.r-project.org

lonelyr: Scoring for Common Loneliness Scales

Scoring functions for widely used loneliness measures, with response keys and reverse-scoring drawn from the original scale manuals. Covers the UCLA Loneliness Scale Version 3 and its three-item short form, the de Jong Gierveld 11- and 6-item scales, the Children's Loneliness and Social Dissatisfaction Scale, and the short Social and Emotional Loneliness Scale for Adults. Out-of-range responses raise a warning, and missing-data rules follow each scale's manual.

cran.r-project.org

actinet: Estimate Human Activity from 'Accelerometry' Data

Interfaces the 'actinet' Python module &lt;<a href="https://github.com/OxWearables/actinet" target="_top">https://github.com/OxWearables/actinet</a>&gt; for an activity classification model based on self-supervised learning for wrist-worn accelerometer data.

cran.r-project.org

trialSizing: Tools for Experimental Design Sizing

Sizes field experiments from uniformity-trial data, following the relationship between the coefficient of variation and plot size. Checks a trial for the spatial structure the sizing methods assume (semivariogram, Moran's I, kriged field map), summarises the coefficient of variation over every plot shape the grid admits, and estimates the optimal plot size by the modified maximum curvature method of Meier and Lessman (1971), by the linear response plateau (LRP) and quadratic response plateau (QRP) models, and by the closed form of Paranaiba, Ferreira and Morais (2009), which can be compared side by side. From the coefficient of variation at the optimum it derives the number of replications needed to detect a given difference between treatment means, as in Cargnelutti Filho and others (2014). Every method returns standardised diagnostic statistics, optional bootstrap uncertainty for the breakpoint, and publication-style plots.

cran.r-project.org

StressCensoR: Generalized Stress-Strength Reliability Estimation Under Censoring Schemes

Generalized framework for data generation, Maximum Likelihood Estimation, and Bayesian estimation of stress-strength reliability R = P(Y &lt; X) for arbitrary continuous distributions under censoring schemes based on Chapter 9 of 'Balakrishnan', 'Cramer', and 'Kundu' (2023) &lt;ISBN:978-0-12-398387-9&gt;. Users provide probability density functions, cumulative distribution functions, survival functions, support bounds, parameter ranges, and sample sizes. Implements data generation under Type-I, Type-II, progressive Type-II, Type-I hybrid, Type-II hybrid, generalized hybrid, progressive hybrid, joint, block random, middle, and truncation censoring schemes, accompanied by diagnostic histograms, dot plots, and autocorrelation plots. Maximum Likelihood Estimation supports optimization routines including 'Newton-Raphson', 'Broyden'-'Fletcher'-'Goldfarb'-'Shanno' ('BFGS'), 'BFGS' in R ('BFGSR'), 'Berndt'-'Hall'-'Hall'-'Hausman' ('BHHH'), Simulated Annealing ('SANN'), Conjugate Gradients ('CG'), and 'Nelder'-'Mead' ('NM'), returning summaries ('AIC', 'coef', 'logLik', 'nIter', 'stdEr', summary, 'vcov'). Bayesian estimation of stress-strength reliability R = P(Y &lt; X) is performed via Gibbs sampling, Metropolis-Hastings algorithm, Importance Sampling, and 'Lindley' approximation (1980). Methods and censoring schemes are described in 'Balakrishnan', 'Cramer', and 'Kundu' (2023, ISBN:978-0-12-398387-9), 'Lindley' (1980) &lt;<a href="https://doi.org/10.1111%2Fj.2517-6161.1980.tb01102.x" target="_top">doi:10.1111/j.2517-6161.1980.tb01102.x</a>&gt;, 'Geweke' (1989) &lt;<a href="https://doi.org/10.2307%2F2290062" target="_top">doi:10.2307/2290062</a>&gt;, 'Metropolis' (1953) &lt;<a href="https://doi.org/10.1063%2F1.1699114" target="_top">doi:10.1063/1.1699114</a>&gt;, 'Hastings' (1970) &lt;<a href="https://doi.org/10.1093%2Fbiomet%2F57.1.97" target="_top">doi:10.1093/biomet/57.1.97</a>&gt;, 'Geman' and 'Geman' (1984) &lt;<a href="https://doi.org/10.1109%2FTPAMI.1984.4767596" target="_top">doi:10.1109/TPAMI.1984.4767596</a>&gt;, 'Kundu' and 'Gupta' (2005) &lt;<a href="https://doi.org/10.1016%2Fj.jspi.2004.09.006" target="_top">doi:10.1016/j.jspi.2004.09.006</a>&gt;, 'Kundu' and 'Gupta' (2006) &lt;<a href="https://doi.org/10.1016%2Fj.csda.2005.02.007" target="_top">doi:10.1016/j.csda.2005.02.007</a>&gt;, 'Berndt', 'Hall', 'Hall', and 'Hausman' (1974) &lt;<a href="https://doi.org/10.3386%2Ft0003" target="_top">doi:10.3386/t0003</a>&gt;, 'Fletcher' (1987, ISBN:978-0-471-91547-8), and 'Nelder' and 'Mead' (1965) &lt;<a href="https://doi.org/10.1093%2Fcomjnl%2F7.4.308" target="_top">doi:10.1093/comjnl/7.4.308</a>&gt;.

cran.r-project.org

specmine: Metabolomics and Spectral Data Analysis and Mining

Provides methods for metabolomics and spectral data analysis, including data import, preprocessing, visualization, univariate and multivariate analysis, machine learning, feature selection, and pathway analysis. The package supports analytical workflows for different data types used in metabolomics and spectroscopy. Some optional functionality uses the suggested packages 'cyjShiny' and 'specmine.datasets'. The package 'specmine.datasets' is maintained separately at &lt;<a href="https://github.com/PedroFontao/specmine.datasets" target="_top">https://github.com/PedroFontao/specmine.datasets</a>&gt;.

cran.r-project.org

SampleSizeR: Sample Size Calculations for Epidemiological, Clinical, and Diagnostic Studies

Provides comprehensive methods for sample size determination for epidemiological studies, clinical trials, diagnostic accuracy studies, and diagnostic agreement studies. The package supports prevalence surveys, cluster prevalence studies, unmatched case-control studies, cohort studies, superiority, non-inferiority, and equivalence clinical trials, diagnostic sensitivity, diagnostic specificity, receiver operating characteristic (ROC) area under the curve (AUC), and diagnostic agreement studies. Functions include optional adjustments for finite population correction, design effect, unequal allocation, anticipated response rate, and dropout. Results are returned as standardized 'SampleSizeR' objects with print, summary, plot, and data frame methods.

cran.r-project.org

rpanelauto: Perform Automatic Estimation on Time Series in Multidimensional Panels

Provides a framework for automatic estimation of time-series models for univariate and multidimensional panel data. A user-supplied estimation function is applied independently to each time series, with optional processing before and after estimation. The function returns the transformed data together with the fitted models.

cran.r-project.org

rmoriebricklayer: Reproducible Data Capsules with Provenance and Fallback

Tools for building brick-proof, reproducible, self-contained data capsules. Resolves open-data sources through the Comprehensive Knowledge Archive Network ('CKAN', &lt;<a href="https://ckan.org/" target="_top">https://ckan.org/</a>&gt;) package_show and package_search endpoints, records and verifies provenance with Secure Hash Algorithm 256 ('SHA-256') digests and Internet Archive 'Wayback Machine' (&lt;<a href="https://web.archive.org/" target="_top">https://web.archive.org/</a>&gt;) snapshots, validates downloaded data against a pinned schema, and falls back to schema-driven synthetic data when the real source is unreachable. Run records are captured in a manifest plus a plain-language summary so any result can be traced back to its inputs. Also ships a small compiled C core (fast summary statistics and a self-contained 'SHA-256') that sibling packages in the 'rmorie' ecosystem reach through 'LinkingTo' for a single, shared numeric and provenance-hashing backend.

cran.r-project.org

psreplicate: Access the Political Science Replication Index from R

Search and browse the Political Science Replication Index (&lt;<a href="https://jsakowuah.github.io/polisci-replication/" target="_top">https://jsakowuah.github.io/polisci-replication/</a>&gt;), a searchable, tagged index of replication packages crawled monthly from flagship political science journals' Harvard Dataverse collections, without leaving R. Provides functions to download and cache the index locally and to search and filter it by journal, method, data type, and year.

cran.r-project.org

predHCS: Point and Interval Prediction for Censored Data under Various Hybrid Censoring Schemes

Implements generalized statistical point prediction and prediction intervals for future failure times under various hybrid censoring schemes. Supported censoring schemes include Type-I, Type-II, Generalized Type-I, Generalized Type-II, Unified, Progressive Type-I, and Progressive Type-II hybrid censoring schemes. Available prediction methods include Best Unbiased Predictor (BUP), Conditional Median Predictor (CMP), Maximum Likelihood Predictor (MLP), equal-tailed classical prediction intervals, Highest Conditional Density (HCD) prediction intervals, and Bayesian prediction intervals. Algorithms accept user-defined continuous probability density functions, cumulative distribution functions, quantile functions, or survival functions along with estimated parameter values. Methodological foundations are based on Balakrishnan, Cramer, and Kundu (2023, ISBN:978-0123983879), Shafay and Balakrishnan (2012) &lt;<a href="https://doi.org/10.1080%2F03610918.2011.579367" target="_top">doi:10.1080/03610918.2011.579367</a>&gt; for Type-I hybrid censoring, Balakrishnan and Shafay (2012) &lt;<a href="https://doi.org/10.1080%2F03610926.2010.543300" target="_top">doi:10.1080/03610926.2010.543300</a>&gt; for Type-II hybrid censoring, Shafay (2017) &lt;<a href="https://doi.org/10.1080%2F03610926.2016.1200093" target="_top">doi:10.1080/03610926.2016.1200093</a>&gt; for Generalized Type-I hybrid censoring, Shafay (2016) &lt;<a href="https://doi.org/10.1080%2F00949655.2015.1096361" target="_top">doi:10.1080/00949655.2015.1096361</a>&gt; for Generalized Type-II hybrid censoring, Mohie El-Din, Nagy, and Shafay (2017) &lt;<a href="https://doi.org/10.18576%2Fjsap%2F060113" target="_top">doi:10.18576/jsap/060113</a>&gt; for Unified hybrid censoring, Ebrahimi (1992) &lt;<a href="https://doi.org/10.1109%2F24.126685" target="_top">doi:10.1109/24.126685</a>&gt;, Valiollahi, Asgharzadeh, and Kundu (2017) &lt;<a href="https://doi.org/10.1214%2F15-BJPS302" target="_top">doi:10.1214/15-BJPS302</a>&gt;, and Asgharzadeh, Valiollahi, and Kundu (2015) &lt;<a href="https://doi.org/10.1080%2F00949655.2013.848451" target="_top">doi:10.1080/00949655.2013.848451</a>&gt;.

cran.r-project.org

orbis: Interactive and High-Resolution Layered Graphics with Built-in World Maps

A layered grammar of graphics that compiles plots to a resolution-independent scene description and renders it through two back-ends: a self-contained SVG writer with embedded 'JavaScript' for interactive figures (tooltips, hover highlighting, zoom, pan and legend toggling) and R's own graphics devices for publication-quality output at any resolution. Geographic layers are first class: a simplified world polygon dataset ships with the package and can be drawn with several map projections, including Robinson, Equal Earth and an orthographic globe. The layered grammar follows Wickham (2010) &lt;<a href="https://doi.org/10.1198%2Fjcgs.2009.07098" target="_top">doi:10.1198/jcgs.2009.07098</a>&gt;; projections follow Snyder (1987) &lt;<a href="https://doi.org/10.3133%2Fpp1395" target="_top">doi:10.3133/pp1395</a>&gt; and, for Equal Earth, Savric, Patterson and Jenny (2019) &lt;<a href="https://doi.org/10.1080%2F13658816.2018.1504949" target="_top">doi:10.1080/13658816.2018.1504949</a>&gt;; line simplification uses Douglas and Peucker (1973) &lt;<a href="https://doi.org/10.3138%2FFM57-6770-U75U-7727" target="_top">doi:10.3138/FM57-6770-U75U-7727</a>&gt;; the default colour scales follow the guidance on perceptually uniform palettes of Crameri, Shephard and Heron (2020) &lt;<a href="https://doi.org/10.1038%2Fs41467-020-19160-7" target="_top">doi:10.1038/s41467-020-19160-7</a>&gt;.

cran.r-project.org

oddsapiio: Client for the 'Odds-API.io' Sports Betting Odds API

Query live and upcoming sports betting odds from the 'Odds-API.io' REST API &lt;<a href="https://docs.odds-api.io" target="_top">https://docs.odds-api.io</a>&gt;. Covers sports, bookmakers, leagues, events, per-event odds, value bets and arbitrage bets across 265+ bookmakers, returned as tidy data frames. An API key is required; a free tier is available.

cran.r-project.org

needenv: Validate Required Environment Variables with Defaults

Validates that required environment variables are set and non-empty before environment-dependent code runs. Variables may have call-site defaults, and all unresolved variables are reported together. Successful values are returned as a named list without reading environment files or modifying the process environment.

cran.r-project.org

MYIS: 'Moreau-Yosida' Importance Sampling for Statistical Inference

Implements 'Moreau-Yosida' Markov chain Monte Carlo ('MCMC') importance sampling for parameter estimation and Bayesian inference under smooth, non-differentiable, or light-tailed target posterior distributions and arbitrary probability models with complete or censored data. Users supply user-defined probability density functions, optional distribution functions, parameter ranges, and observations subject to complete, right, left, interval, Type-I, Type-II, progressive Type-II, first-failure, or truncation schemes. Constructs 'Moreau-Yosida' envelopes, gradient-based proposals ('MALA', 'HMC', or 'RWM'), self-normalized importance weights, batch-means asymptotic variance estimates, and Bayesian marginal quantiles. Methodologies are based on 'Shukla', 'Vats', and 'Chi' (2025) &lt;<a href="https://doi.org/10.48550%2FarXiv.2501.02228" target="_top">doi:10.48550/arXiv.2501.02228</a>&gt;, 'Pereyra' (2016) &lt;<a href="https://doi.org/10.1111%2Fsjos.12208" target="_top">doi:10.1111/sjos.12208</a>&gt;, 'Durmus' and others (2022) &lt;<a href="https://doi.org/10.1214%2F22-EJS2027" target="_top">doi:10.1214/22-EJS2027</a>&gt;, 'Chen' and 'Shao' (1999) &lt;<a href="https://doi.org/10.1214%2Fss%2F1009211804" target="_top">doi:10.1214/ss/1009211804</a>&gt;, 'Roberts' and 'Rosenthal' (1998) &lt;<a href="https://doi.org/10.1214%2Faoap%2F1028903378" target="_top">doi:10.1214/aoap/1028903378</a>&gt;, 'Geweke' (1989) &lt;<a href="https://doi.org/10.2307%2F2290062" target="_top">doi:10.2307/2290062</a>&gt;, 'Hesterberg' (1995) &lt;<a href="https://doi.org/10.1080%2F00031305.1995.10476138" target="_top">doi:10.1080/00031305.1995.10476138</a>&gt;, and 'Balakrishnan' and 'Aggarwala' (2000, ISBN:978-0-8176-4001-9).

cran.r-project.org

moranajp: Morphological Analysis for Japanese

Supports morphological analysis for Japanese by using 'MeCab' &lt;<a href="https://taku910.github.io/mecab/" target="_top">https://taku910.github.io/mecab/</a>&gt;, 'Sudachi' &lt;<a href="https://github.com/WorksApplications/Sudachi" target="_top">https://github.com/WorksApplications/Sudachi</a>&gt;, 'Chamame' &lt;<a href="https://chamame.ninjal.ac.jp/" target="_top">https://chamame.ninjal.ac.jp/</a>&gt;, or 'Ginza' &lt;<a href="https://github.com/megagonlabs/ginza" target="_top">https://github.com/megagonlabs/ginza</a>&gt;. Can input a data.frame and obtain all results of 'MeCab' and the row number of the original data.frame as a text id.

cran.r-project.org

LugsailGR: Generalized Gelman-Rubin Diagnostic and Effective Sample Size for MCMC

Provides generalized univariate and multivariate 'Gelman-Rubin' convergence diagnostics, effective sample size ('ESS') estimates, and principled termination thresholds for Markov chain Monte Carlo ('MCMC') simulations, based on Vats and Knudson (2021) &lt;<a href="https://doi.org/10.1214%2F20-STS812" target="_top">doi:10.1214/20-STS812</a>&gt;. The package incorporates replicated lugsail batch means variance estimators to construct stable convergence statistics for single and multiple chains. Additionally, it offers comprehensive tools for evaluating 'MCMC' output generated from user-supplied probability density functions ('PDF') or log-likelihoods, including implementations for censored data models under right, left, interval, 'Type-I', 'Type-II', progressive, and hybrid censoring schemes.

cran.r-project.org

liteformats: Lightweight Output Formats for 'litedown'

A collection of lightweight, minimalist output formats and templates for 'litedown' by Xie (2026) &lt;<a href="https://doi.org/10.32614%2FCRAN.package.litedown" target="_top">doi:10.32614/CRAN.package.litedown</a>&gt;, including resumes, cover letters, and other common document types. Documents are rendered with HTML and CSS and can be printed to PDF with a 'Chromium'-based browser, without requiring 'Pandoc' or a 'LaTeX' installation.

cran.r-project.org

linf: L-Infinity Normalization and Dominant Community State Types

Implements L-infinity normalization for compositional matrices, assigns samples to dominant features, constructs truncated and hierarchically refined dominant community state types, and computes representative landmark profiles. The methods are described in the accompanying publication &lt;<a href="https://doi.org/10.48550%2FarXiv.2503.21543" target="_top">doi:10.48550/arXiv.2503.21543</a>&gt;. Bundled vaginal and gut microbiome data support reproducible demonstrations of the package interface; phenotype fields in the stratified gut subset are illustrative and are not suitable for population-level inference.

cran.r-project.org

invasible: Predicting Invasion Probabilities from Phylogenetic Data and Species Traits

A phylogenetic modelling approach for predicting species invasion risk, out of a given pool of local species where a subset is known to be invasive elsewhere. The package uses phylogenetic signal estimation and phylogenetic linear and logistic models to estimate probabilities of being invasive based on phylogeny and any set of additional predictors. A ranking method is implemented to evaluate prioritisation strategies. A manuscript describing these methods, by Shahar Dubiner and Tamar Guy-Haim, is in preparation.

cran.r-project.org

grip: Graph Drawing with Intelligent Placement (GRIP)

Implements the GRIP multiscale graph layout workflow for ordinary graphs and a geometry-aware weighted GRIP workflow for weighted graphs in 2D and 3D. Provides layout scoring, candidate comparison, multiscale trace diagnostics, synthetic graph families, and advanced experimental geodesic-KK utilities for weighted-layout evaluation and polish. Based on Gajer and Kobourov (2002) &lt;<a href="https://doi.org/10.7155%2Fjgaa.00052" target="_top">doi:10.7155/jgaa.00052</a>&gt; and Gajer, Goodrich and Kobourov (2004) &lt;<a href="https://doi.org/10.1016%2Fj.comgeo.2004.03.014" target="_top">doi:10.1016/j.comgeo.2004.03.014</a>&gt;.

cran.r-project.org

gmeans: G-means Clustering

Gaussian-means (G-means) clustering is a clustering algorithm that extends the k-means algorithm by automatically determining the number of clusters.

cran.r-project.org

glmbayesCore: Core C++ Sampling Engine for 'glmbayes'

Core C++ engine for 'glmbayes': envelope-based iid linear and generalized linear model samplers, prior-family routing, and optional 'OpenCL' acceleration. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) &lt;<a href="https://doi.org/10.1198%2F016214506000000357" target="_top">doi:10.1198/016214506000000357</a>&gt;. Intended as a developer backend for the 'glmbayes' formula interface; end users should use 'glmbayes' for modelling with interfaces analogous to 'lm' and 'glm'. Mixed-model engines are planned for a future release.

cran.r-project.org

fracreg: Fractional Response Regressions

Provides routines for the estimation and specification analysis of fractional response models. Includes univariate one-part, two-part, and double-inflated three-part fractional models. Further incorporates estimators for panel data settings and addresses unobserved heterogeneity and endogeneity via correlated random effects and control function approaches. Extends fractional methodology to multivariate data via fractional multinomial logit models and handles high-dimensional multicollinear data via fractional ridge regression. Calculates analytical partial effects across all model types and includes generalised goodness-of-functional-form (GGOFF) and Regression Equation Specification Error Test (RESET) hypothesis tests. Methods are described in Papke and Wooldridge (1996) &lt;<a href="https://doi.org/10.1002/(SICI)1099-1255(199611)11:6%3C619::AID-JAE418%3E3.0.CO;2-1" target="_top">doi:10.1002/(SICI)1099-1255(199611)11:6%3C619::AID-JAE418%3E3.0.CO;2-1</a>&gt;, Papke and Wooldridge (2008) &lt;<a href="https://doi.org/10.1016%2Fj.jeconom.2008.05.009" target="_top">doi:10.1016/j.jeconom.2008.05.009</a>&gt;, Buis (2008) &lt;<a href="http://maartenbuis.nl/software/likelihoodFmlogit.pdf" target="_top">http://maartenbuis.nl/software/likelihoodFmlogit.pdf</a>&gt;, Ramalho, Ramalho and Murteira (2011) &lt;<a href="https://doi.org/10.1111%2Fj.1467-6419.2009.00602.x" target="_top">doi:10.1111/j.1467-6419.2009.00602.x</a>&gt;, Fang and Ma (2013) &lt;<a href="https://doi.org/10.1080%2F02664763.2012.758246" target="_top">doi:10.1080/02664763.2012.758246</a>&gt;, Mullahy (2015) &lt;<a href="https://doi.org/10.1515%2Fjem-2012-0006" target="_top">doi:10.1515/jem-2012-0006</a>&gt;, Murteira and Ramalho (2016) &lt;<a href="https://doi.org/10.1080%2F07474938.2013.806849" target="_top">doi:10.1080/07474938.2013.806849</a>&gt;, and Rokem and Kay (2020) &lt;<a href="https://doi.org/10.1093%2Fgigascience%2Fgiaa133" target="_top">doi:10.1093/gigascience/giaa133</a>&gt;.

cran.r-project.org

EWAScaller: Query and Analyse the 'EWAS Atlas' Database

Provides a client for the 'EWAS Atlas' web services (&lt;<a href="https://ngdc.cncb.ac.cn/ewas/" target="_top">https://ngdc.cncb.ac.cn/ewas/</a>&gt;; Li et al. (2019) &lt;<a href="https://doi.org/10.1093%2Fnar%2Fgky1027" target="_top">doi:10.1093/nar/gky1027</a>&gt;), allowing users to query epigenome-wide association study (EWAS) data by CpG (cytosine-phosphate-guanine) probe identifier, gene symbol, or genomic region, and to run trait, Gene Ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, and genomic location enrichment analyses on a set of CpG probes. Query functions support concurrent, rate-limited requests to the remote service. Results are returned as tidy data frames with dedicated summary and plotting methods, including word clouds of enriched 'EWAS Atlas' trait terms.

cran.r-project.org

epibyhand: Worked Derivations for Classical Epidemiological Measures

Computes classical epidemiological measures and returns the complete worked derivation alongside the result: every intermediate quantity, the formula, and the formula with the observed numbers substituted in. Intended for teaching, for checking hand calculations, and for generating worked solutions in course materials. Scope is deliberately limited to methods a student can compute by hand on paper. Methods follow Mantel and Haenszel (1959) &lt;<a href="https://doi.org/10.1093%2Fjnci%2F22.4.719" target="_top">doi:10.1093/jnci/22.4.719</a>&gt;, Greenland and Robins (1985, Biometrics 41, 55-68), Robins, Breslow and Greenland (1986, Biometrics 42, 311-323), and Breslow and Day (1980, IARC Scientific Publications No. 32).

cran.r-project.org

DendroFlux: Processing and Analyzing Dendrometer and Sap Flux Data

Data management and cleaning for dendrometer and sap flux data, including gap detection, NA identification, missing value interpolation, and date conversion. The package also calculates multiple growth metrics of tree radial change data, including the cumulative growth over the entire observation period, daily cumulative growth, and growth changes between adjacent time intervals. Various approaches can be applied to calculate the night delta-Tmax required for sap flow (Peters et al., 2018, &lt;<a href="https://doi.org/10.1111%2Fnph.15241" target="_top">doi:10.1111/nph.15241</a>&gt;) and subsequently estimate sap flow density (Granier, 1987, &lt;<a href="https://doi.org/10.1093%2Ftreephys%2F3.4.309" target="_top">doi:10.1093/treephys/3.4.309</a>&gt;). In addition, it supports the creation of simple time‑series point plots to visually display the dynamic changes in tree growth status or sap flow density during that period.

cran.r-project.org