Fast multi-trait and multi-trail Genome Wide Association Studies (GWAS) following the method described in Zhou and Stephens. (2014), <doi:10.1038/nmeth.2848>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

This package provides tools for Genotype by Environment Interaction (GEI) analysis, using statistical models and visualizations to assess genotype performance across environments. It helps researchers explore interaction effects, stability, and adaptability in multi-environment trials, identifying the best-performing genotypes in different conditions. Which Win Where!

This package provides a comprehensive logging framework for R applications that provides hierarchical logging levels, database integration, and contextual logging capabilities. The package supports SQLite storage for persistent logs, provides colour-coded console output for better readability, includes parallel processing support, and implements structured error reporting with JSON formatting.

Provide various functions and tools to help fit models for estimating treatment effects in stepped wedge cluster randomized trials. Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) "Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect", <doi:10.1002/sim.9511>.

This package provides functions for stratified sampling and assigning custom labels to data, ensuring randomness within groups. The package supports various sampling methods such as stratified, cluster, and systematic sampling. It allows users to apply transformations and customize the sampling process. This package can be useful for statistical analysis and data preparation tasks.

This package provides functions to perform stepwise split regularized regression. The approach first uses a stepwise algorithm to split the variables into the models with a goodness of fit criterion, and then regularization is applied to each model. The weights of the models in the ensemble are determined based on a criterion selected by the user.

This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering.

This package provides tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>, extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.

An open source platform for validation and process control. Tools to analyze data from internal validation of forensic short tandem repeat (STR) kits are provided. The tools are developed to provide the necessary data to conform with guidelines for internal validation issued by the European Network of Forensic Science Institutes (ENFSI) DNA Working Group, and the Scientific Working Group on DNA Analysis Methods (SWGDAM). A front-end graphical user interface is provided. More information about each function can be found in the respective help documentation.

This package provides methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (â Equity Asset Valuation (4th Edition)â , 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).

Use stem analysis data to reconstructing tree growth and carbon accumulation. Users can independently or in combination perform a number of standard tasks for any tree species. (i) Age class determination. (ii) The cumulative growth, mean annual increment, and current annual increment of diameter at breast height (DBH) with bark, tree height, and stem volume with bark are estimated. (iii) Tree biomass and carbon storage estimation from volume and allometric models are calculated. (iv) Height-diameter relationship is fitted with nonlinear models, if diameter at breast height (DBH) or tree height are available, which can be used to retrieve tree height and diameter at breast height (DBH). <https://github.com/forestscientist/StemAnalysis>.

The StockDistFit package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) <https://www.jstage.jst.go.jp/article/jappstat/37/1/37_1_1/_pdf/-char/ja> and Benth et al. (2008) <https://books.google.co.ke/books?hl=en&lr=&id=MHNpDQAAQBAJ&oi=fnd&pg=PR7&dq=Stochastic+modeling+of+commodity+prices+using+the+Variance+Gamma+(VG)+model.+&ots=YNIL2QmEYg&sig=XZtGU0lp4oqXHVyPZ-O8x5i7N3w&redir_esc=y#v=onepage&q&f=false>.

Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees. Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options. As it's built with C5.0 trees, it works only for classification (not for regression).

Settings and functions to extend the knitr Stata engine.

Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning.

Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test.

Collection of spatial transcriptomics datasets stored in SpatialExperiment Bioconductor format, for use in examples, demonstrations, and tutorials. The datasets are from several different platforms and have been sourced from various publicly available sources. Several datasets include images and/or reference annotation labels.

S4 class wrappers for the ODBC and Pool DBI connection, also provides some utilities to paste small datasets to clipboard, rename columns. It is used by the package stacomiR for connections to the database. Development versions of stacomiR are available in R-forge.

This package provides a collection of statistical and geometrical tools including the aligned rank transform (ART; Higgins et al. 1990 <doi:10.4148/2475-7772.1443>; Peterson 2002 <doi:10.22237/jmasm/1020255240>; Wobbrock et al. 2011 <doi:10.1145/1978942.1978963>), 2-D histograms and histograms with overlapping bins, a function for making all possible formulae within a set of constraints, amongst others.

The goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is <https://sdmx.org/?page_id=3215/>.

The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package.

Implementation of analytical models for estimating streamflow depletion due to groundwater pumping, and other related tools. Functions are broadly split into two groups: (1) analytical streamflow depletion models, which estimate streamflow depletion for a single stream reach resulting from groundwater pumping; and (2) depletion apportionment equations, which distribute estimated streamflow depletion among multiple stream reaches within a stream network. See Zipper et al. (2018) <doi:10.1029/2018WR022707> for more information on depletion apportionment equations and Zipper et al. (2019) <doi:10.1029/2018WR024403> for more information on analytical depletion functions, which combine analytical models and depletion apportionment equations.

An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs.

Includes bases for litholog generation: graphical functions based on R base graphics, interval management functions and svg importation functions among others. Also include stereographic projection functions, and other functions made to deal with large datasets while keeping options to get into the details of the data. When using for publication please cite Sebastien Wouters, Anne-Christine Da Silva, Frederic Boulvain and Xavier Devleeschouwer, 2021. The R Journal 13:2, 153-178. The palaeomagnetism functions are based on: Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press. <https://earthref.org/MagIC/books/Tauxe/Essentials/>; Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge University Press, 289 pp.; Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205, <doi: 10.1016/j.cageo.2012.07.021>.