This package provides functions to design and apply tests that are anytime valid. The functions can be used to design hypothesis tests in the prospective/randomised control trial setting or in the observational/retrospective setting. The resulting tests remain valid under both optional stopping and optional continuation. The current version includes safe t-tests and safe tests of two proportions. For details on the theory of safe tests, see Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>, for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021) "Safe Tests and Always-Valid Confidence Intervals for contingency tables and beyond" <arXiv:2106.02693> for details on safe contingency table tests.

This package provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats. These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.

Provide utilities to work with solar time, i.e. where noon is exactly when sun culminates. Provides functions for computing sun position and times of sunrise and sunset.

Computes standard error and confidence interval of various descriptive statistics under various designs and sampling schemes. The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005) <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012) <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>, Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding <doi:10.20982/tqmp.15.1.p001> for specific references. The documentation is available at <https://dcousin3.github.io/superb/> .

This package provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).

Develops a framework for fisheries stock assessment simulation testing with Stock Synthesis (SS) as described in Anderson et al. (2014) <doi:10.1371/journal.pone.0092725>.

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!

Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code.

Starting from a Regression Model, it provides a stepwise procedure to select the linear predictor.

Build a project framework for users with access to only the most basic of automation tools.

This package implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying common change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure.

This package contains space filling based tools for machine learning and data mining. Some functions offer several computational techniques and deal with the out of memory for large big data by using the ff package.

Studies otolith shape variation among fish populations. Otoliths are calcified structures found in the inner ear of teleost fish and their shape has been known to vary among several fish populations and stocks, making them very useful in taxonomy, species identification and to study geographic variations. The package extends previously described software used for otolith shape analysis by allowing the user to automatically extract closed contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000) <doi:10.1111/1475-4983.00148>, choose from conducting either a Fourier or wavelet see Gençay et al (2001) <doi:10.1016/S0378-4371(00)00463-5> transform to the outlines and visualize the mean shape. The output of the package are independent Fourier or wavelet coefficients which can be directly imported into a wide range of statistical packages in R. The package might prove useful in studies of any two dimensional objects.

Plots a QQ-Norm Plot with several Gaussian simulations.

This package provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms.

In the past decade, genome-scale metabolic reconstructions have widely been used to comprehend the systems biology of metabolic pathways within an organism. Different GSMs are constructed using various techniques that require distinct steps, but the input data, information conversion and software tools are neither concisely defined nor mathematically or programmatically formulated in a context-specific manner.The tool that quantitatively and qualitatively specifies each reconstruction steps and can generate a template list of reconstruction steps dynamically selected from a reconstruction step reservoir, constructed based on all available published papers.

This package provides a novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.

This package provides a collection of highly configurable, touch-enabled knob input controls for shiny'. These components can be styled to fit in perfectly in any app, and allow users to set precise values through many input modalities. Users can touch-and-drag, click-and-drag, scroll their mouse wheel, double click, or use keyboard input.

Sometimes it is handy to be able to view an image file on an R graphics device. This package just does that. Currently it supports PNG files.

The Hypothesis tests for the means of independent or paired groups. This package investigates the normality assumption automatically. Then, it tests the hypothesis tests for two independent or paired group means by using parametric or non-parametric tests. It uses the Shapiro-Wilk test to test the normality assumption. For independent two groups, If data comes from the normal distribution, the package uses the Z or t-test according to whether variances are known. For paired groups, it uses paired t-test under normal data sets. If data does not come from the normal distribution, the package uses the Wilcoxon test for independent and paired cases.

This package implements the "shrinkage t" statistic introduced in Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252> and a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described in Zuber and Strimmer (2009) <DOI:10.1093/bioinformatics/btp460>. It also offers a convenient interface to a number of other regularized t-statistics commonly employed in high-dimensional case-control studies.

Analysis of spatial relationships between cell types in spatial transcriptomics data. Spatial proximity is a critical factor in cell-cell communication. The package calculates nearest neighbor distances between specified cell types and provides visualization tools to explore spatial patterns. Applications include studying cell-cell interactions, immune microenvironment characterization, and spatial organization of tissues.

Soil health assessment builds information to improve decision in soil management. It facilitates assessment of soil conditions for crop suitability [such as those given by FAO <https://www.fao.org/land-water/databases-and-software/crop-information/en/>], groundwater recharge, fertility, erosion, salinization [<doi:10.1002/ldr.4211>], carbon sequestration, irrigation potential, and status of soil resources.

This package provides functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/.