Pauly et al. (2008) <http://legacy.seaaroundus.s3.amazonaws.com/doc/Researcher+Publications/dpauly/PDF/2008/Books%26Chapters/FisheriesInLargeMarineEcosystems.pdf> created (and coined the name) Stock Status Plots for a UNEP compendium on Large Marine Ecosystems(LMEs, Sherman and Hempel (2009)<https://marineinfo.org/imis?module=ref&refid=142061&printversion=1&dropIMIStitle=1>). Stock status plots are bivariate graphs summarizing the status (e.g., developing, fully exploited, overexploited, etc.), through time, of the multispecies fisheries of a fished area or ecosystem. This package contains three functions to generate stock status plots viz., SSplots_pauly() (as per the criteria proposed by Pauly et al.,2008), SSplots_kleisner() (as per the criteria proposed by Kleisner and Pauly (2011) <http://www.ecomarres.com/downloads/regional.pdf> and Kleisner et al. (2013) <doi:10.1111/j.1467-2979.2012.00469.x>)and SSplots_EPI() (as per the criteria proposed by Jayasankar et al.,2021 <https://eprints.cmfri.org.in/11364/>).

Collection of shiny application styling that are based on the GOV.UK Design System. See <https://design-system.service.gov.uk/components/> for details.

Generates region-specific Suess and Laws corrections for stable carbon isotope data from marine organisms collected between 1850 and 2023. Version 0.1.6 of SuessR contains four built-in regions: the Bering Sea ('Bering Sea'), the Aleutian archipelago ('Aleutian Islands'), the Gulf of Alaska ('Gulf of Alaska'), and the subpolar North Atlantic ('Subpolar North Atlantic'). Users can supply their own environmental data for regions currently not built into the package to generate corrections for those regions.

Evaluating the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. Enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. Also available as shiny app. The reference for the methodology is: Zizka et al. (2020) <doi:10.1111/ecog.05102>.

Conducting Bayesian Optimal Interval (BOIN) design for phase I dose-finding trials. simFastBOIN provides functions for pre-computing decision tables, conducting trial simulations, and evaluating operating characteristics. The package uses vectorized operations and the Iso::pava() function for isotonic regression to achieve efficient performance while maintaining full compatibility with BOIN methodology. Version 1.3.2 adds p_saf and p_tox parameters for customizable safety and toxicity thresholds. Version 1.3.1 fixes Date field. Version 1.2.1 adds comprehensive roxygen2 documentation and enhanced print formatting with flexible table output options. Version 1.2.0 integrated C-based PAVA for isotonic regression. Version 1.1.0 introduced conservative MTD selection (boundMTD) and flexible early stopping rules (n_earlystop_rule). Methods are described in Liu and Yuan (2015) <doi:10.1111/rssc.12089>.

Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample designs by Rosén (1997, <doi:10.1016/S0378-3758(96)00186-3>), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, <doi:10.1111/j.1751-5823.2011.00166.x>).

Obtaining accurate and stable estimates of regression coefficients can be challenging when the suggested statistical model has issues related to multicollinearity, convergence, or overfitting. One solution is to use principal component analysis (PCA) results in the regression, as discussed in Chan and Park (2005) <doi:10.1080/01446190500039812>. The swaprinc() package streamlines comparisons between a raw regression model with the full set of raw independent variables and a principal component regression model where principal components are estimated on a subset of the independent variables, then swapped into the regression model in place of those variables. The swaprinc() function compares one raw regression model to one principal component regression model, while the compswap() function compares one raw regression model to many principal component regression models. Package functions include parameters to center, scale, and undo centering and scaling, as described by Harvey and Hansen (2022) <https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>. Additionally, the package supports using Gifi methods to extract principal components from categorical variables, as outlined by Rossiter (2021) <https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html#2_Package>.

Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.

This package implements the Smoothness-Penalized Deconvolution method for estimating a probability density under measurement error of Kent and Ruppert (2023) <doi:10.1080/01621459.2023.2259028>. The estimator is formed by computing a histogram of the error-contaminated data, and then finding an estimate that minimizes a reconstruction error plus a smoothness-inducing penalty term. The primary function, sped(), takes the data and error distribution, and returns the estimator as a function.

For biparental, three and four-way crosses Identity by Descent (IBD) probabilities can be calculated using Hidden Markov Models and inheritance vectors following Lander and Green (<https://www.jstor.org/stable/29713>) and Huang (<doi:10.1073/pnas.1100465108>). One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Fast k-nearest neighbors (K-NN) and principal component analysis (PCA) imputation algorithms for missing values in epigenetic data or other high-dimensional numeric matrices. For PCA, a locally optimal block preconditioned conjugate gradient (LOBPCG) eigensolver with warm starts of both the eigenblock and search direction is also supported. Two complementary imputation strategies are available. Group-wise imputation (e.g., by chromosome) is recommended for Illumina DNA methylation microarrays (e.g., 450K, EPIC) and other matrices with groupable columns. A sliding window approach for K-NN or PCA imputation is recommended only for whole-genome methylation data such as whole-genome bisulfite sequencing (WGBS) or Enzymatic Methyl-seq (EM-seq). The package also supports hyperparameter tuning via repeated cross-validation. The K-NN algorithm is described in: Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and Botstein, D. (1999) "Imputing Missing Data for Gene Expression Arrays". The PCA imputation is an optimized reimplementation of the imputePCA() function from the missMDA package described in: Josse, J. and Husson, F. (2016) <doi:10.18637/jss.v070.i01> "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis".

The function syncSubsample subsamples temporal data of different entities so that the result only contains synchronal events. The function mci calculates the Movement Coordination Index (MCI, see reference on help page for function mci') of a data set created with the function syncSubsample'.

Conducts hierarchical partitioning to calculate individual contributions of spatial and predictors (groups) towards total R2 for spatial simultaneous autoregressive model.

This package provides a Shiny app including the Monaco editor. The Monaco editor is the code editor which powers VS Code'. It is particularly well developed for JavaScript'. In addition to the Monaco editor features, the app provides prettifiers and minifiers for multiple languages, SCSS and TypeScript compilers, code checking for C and C++ (requires cppcheck').

Based on the illness-death model a large number of clinical trials with oncology endpoints progression-free survival (PFS) and overall survival (OS) can be simulated, see Meller, Beyersmann and Rufibach (2019) <doi:10.1002/sim.8295>. The simulation set-up allows for random and event-driven censoring, an arbitrary number of treatment arms, staggered study entry and drop-out. Exponentially, Weibull and piecewise exponentially distributed survival times can be generated. The correlation between PFS and OS can be calculated.

This package implements an extension of the Generalized Berk-Jones (GBJ) statistic for survival data, sGBJ. It computes the sGBJ statistic and its p-value for testing the association between a gene set and a time-to-event outcome with possible adjustment on additional covariates. Detailed method is available at Villain L, Ferte T, Thiebaut R and Hejblum BP (2021) <doi:10.1101/2021.09.07.459329>.

This package implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (JASA 2020) and Kolyan Ray, Botond Szabo, and Gabriel Clara (NeurIPS 2020).

Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper <doi:10.18637/jss.v097.i03>. Note that this package is modelled on the survsim package available in the Stata software (see Crowther and Lambert (2012) <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and Lambert (2013) <doi:10.1002/sim.5823>).

An R shiny user interface for the nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>) package, designed to simplify the modeling process for users. Additionally, this package includes supplementary functions to further enhances the usage of nlmixr2'.

Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>), and the package name stands for "SIngle CEll Growth Analysis in R".

This package implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726.

Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Unionâ s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).

The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.

Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions for importing, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework as outlined in the Supplementary Material of Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.