Generalized correlation and related measures for assessing nonlinear relationships between variables, including mixed-type data. For methodological background, see <https://r-suzuki.github.io/gcor/method.html>.

This package contains the Global Charcoal database data. Data include charcoal series (age, depth, charcoal quantity, associated units and methods) and information on sedimentary sites (localisation, depositional environment, biome, etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.

Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.

Allows users to quickly and easily generate fake data containing Personally Identifiable Information (PII) through convenience functions.

Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.

This package provides a function to retrieve the system timezone on Unix systems which has been found to find an answer when Sys.timezone() has failed. It is based on an answer by Duane McCully posted on StackOverflow', and adapted to be callable from R. The package also builds on Windows, but just returns NULL.

Optimal design analysis algorithms for any study design that can be represented or modelled as a generalised linear mixed model including cluster randomised trials, cohort studies, spatial and temporal epidemiological studies, and split-plot designs. See <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> for a detailed manual on model specification. A detailed discussion of the methods in this package can be found in Watson, Hemming, and Girling (2023) <doi:10.1177/09622802231202379>.

This package provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.

Focuses on data collecting, analyzing and visualization in green finance and environmental risk research and analysis. Main function includes environmental data collecting from official websites such as MEP (Ministry of Environmental Protection of China, <https://www.mee.gov.cn>), water related projects identification and environmental data visualization.

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases <https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package GISINTEGRATION offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

This package provides a set of wrapper functions that mainly re-produces most of the sequence plots rendered with TraMineR::seqplot(). Whereas TraMineR uses base R to produce the plots this library draws on ggplot2'. The plots are produced on the basis of a sequence object defined with TraMineR::seqdef(). The package automates the reshaping and plotting of sequence data. Resulting plots are of class ggplot', i.e. components can be added and tweaked using + and regular ggplot2 functions.

Simulating single cell RNA-seq data with complicated structure. This package is developed based on the Splat method (Zappia, Phipson and Oshlack (2017) <doi:10.1186/s13059-017-1305-0>). GeneScape incorporates additional features to simulate single cell RNA-seq data with complicated differential expression and correlation structures, such as sub-cell-types, correlated genes (pathway genes) and hub genes.

Four graph-based tests are provided for testing whether two samples are from the same distribution. It works for both continuous data and discrete data.

Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length. Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available. See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data <arXiv:1810.05973>.

Causal mediation analysis for a single exposure/treatment and a single mediator, both allowed to be either continuous or binary. The package implements the difference method and provides point and interval estimates as well as testing for the natural direct and indirect effects and the mediation proportion. Nevo, Xiao and Spiegelman (2017) <doi:10.1515/ijb-2017-0006>.

Receives two vectors, computes appropriate function for group comparison (i.e., t-test, Mann-Whitney; equality of variances), and reports the findings (mean/median, standard deviation, test statistic, p-value, effect size) in APA format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).

Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables. Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>. Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.

Statistical analysis of monthly background checks of gun purchases for the New York Times story "What Drives Gun Sales: Terrorism, Obama and Calls for Restrictions" at <https://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html> is provided.

Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.

Fits gastric emptying time series from MRI or scintigraphic measurements using nonlinear mixed-model population fits with nlme and Bayesian methods with Stan; computes derived parameters such as t50 and AUC.

This package contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.

This package provides functions for estimating Rasch model parameters using the Generalized Linear Model (GLM) framework. The methods implemented are based on Brown (2018, ISBN:978-3-319-93547-8) <doi:10.1007/978-3-319-93549-2> and Debelak et al. (2022, ISBN:978-1-138-71046-7) <doi:10.1201/9781315200620>.

This package provides tools to build and work with bilateral generalized-mean price indexes (and by extension quantity indexes), and indexes composed of generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS). Covers the core mathematical machinery for making bilateral price indexes, computing price relatives, detecting outliers, and decomposing indexes, with wrappers for all common (and many uncommon) index-number formulas. Implements and extends many of the methods in Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007, <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020, <doi:10.5089/9781484354841.069>).

Obtain standardized data from multiple Git services, including GitHub and GitLab'. Designed to be Git service-agnostic, this package assists teams with activities spread across various Git platforms by providing a unified way to access repository data.