Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127 for more details.

Computes probabilities related to group sequential designs for normally distributed test statistics. Enables to derive critical boundaries, power, drift, and confidence intervals of such designs. Supports the alpha spending approach by Lan-DeMets (1994) <doi:10.1002/sim.4780131308>.

Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from GENEActiv <https://activinsights.com/>, .bin-format from GENEA devices (not for sale), and .cwa-format from Axivity <https://axivity.com>. Further, it has functions for reading text files with epoch level aggregates from Actical', Fitbit', Actiwatch', ActiGraph', and PhilipsHealthBand'. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.

Analytics to read in and segment raw GENEActiv accelerometer data into epochs and events. For more details on the GENEActiv device, see <https://activinsights.com/resources/geneactiv-support-1-2/>.

Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>. It estimates the parameters of latent class prevalence and item response probability in LCA with a single line comment. It also provides graphs of item response probabilities. In addition, the package enables to estimate the relationship between the prevalence and covariates.

This package provides methods for recursive partitioning based on the Graded Response Model ('GRM'), extending the MOB algorithm from the partykit package. The package allows for fitting GRM trees that partition the population into homogeneous subgroups based on item response patterns and covariates. Includes specialized plotting functions for visualizing GRM trees with different terminal node displays (threshold regions, parameter profiles, and factor score distributions). For more details on the methods, see Samejima (1969) <doi:10.1002/J.2333-8504.1968.TB00153.X>, Komboz et al. (2018) <doi:10.1177/0013164416664394> and Arimoro et al. (2025) <doi:10.1007/s11136-025-04018-6>.

Estimation of gross output production functions and productivity in the presence of numerous fixed (nonflexible) and a single flexible input using the nonparametric identification strategy specified in Gandhi, Navarro, and Rivers (2020) <doi:10.1086/707736>. Monte Carlo evidence from the paper demonstrates high performance in estimating production function elasticities.

The Graphical Group Ridge GGRidge package package classifies ridge regression predictors in disjoint groups of conditionally correlated variables and derives different penalties (shrinkage parameters) for these groups of predictors. It combines the ridge regression method with the graphical model for high-dimensional data (i.e. the number of predictors exceeds the number of cases) or ill-conditioned data (e.g. in the presence of multicollinearity among predictors). The package reduces the mean square errors and the extent of over-shrinking of predictors as compared to the ridge method.Aldahmani, S. and Zoubeidi, T. (2020) <DOI:10.1080/00949655.2020.1803320>.

Firstly, both functions of the univariate Poisson dispersion index (DI) for count data and the univariate exponential variation index (VI) for nonnegative continuous data are performed. Next, other functions of univariate indexes such the binomial dispersion index (DIb), the negative binomial dispersion index (DInb) and the inverse Gaussian variation index (VIiG) are given. Finally, we are computed some multivariate versions of these functions such that the generalized dispersion index (GDI) with its marginal one (MDI) and the generalized variation index (GVI) with its marginal one (MVI) too.

This package implements GINA-X, a genome-wide iterative fine-mapping method designed for non-Gaussian traits. It supports the identification of credible sets of genetic variants.

Offers various swiss maps as data frames and ggplot2 objects and gives the possibility to add layers of data on the maps. Data are publicly available from the swiss federal statistical office. In addition to the \codemaps2 object (a list of 8 swiss maps, at various levels), there are the data frames with the boundaries used to produce these maps (\codeshp_df, a list with 8 data frames).

Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.

This package provides additional brain surface meshes for cortical and cerebellar visualisation in the ggsegverse ecosystem. Cortical surfaces include pial, white, midthickness, semi-inflated, sphere, smoothwm, and orig at fsaverage5 resolution. Cerebellar surfaces include the Spatially Unbiased Infratentorial Template (SUIT) flatmap. All meshes follow the same vertices/faces data frame format used by ggseg.formats and ggseg3d'.

This package provides a tool to sensitivity analysis using SOBOL (Sobol, 1993) and AMA (Dell'Oca et al. 2017 <doi:10.5194/hess-21-6219-2017>) indices. It allows to identify the most sensitive parameter or parameters of a model.

Implementation of a common set of punctual solutions for Cooperative Game Theory.

This package performs goodness-of-fit tests for model-based clustering based on the methodology developed in <doi:10.48550/arXiv.2511.04206>. It implements a test statistic derived from empirical likelihood to verify the distribution of clusters in multivariate Gaussian mixtures and other latent class models.

This package provides functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.

Generalized Mann-Whitney type tests based on probabilistic indices and new diagnostic plots, for the underlying manuscript see Fischer, Oja (2015) <doi:10.18637/jss.v065.i09>.

The geohabnet package is designed to perform a geographically or spatially explicit risk analysis of habitat connectivity. Xing et al (2021) <doi:10.1093/biosci/biaa067> proposed the concept of cropland connectivity as a risk factor for plant pathogen or pest invasions. As the functions in geohabnet were initially developed thinking on cropland connectivity, users are recommended to first be familiar with the concept by looking at the Xing et al paper. In a nutshell, a habitat connectivity analysis combines information from maps of host density, estimates the relative likelihood of pathogen movement between habitat locations in the area of interest, and applies network analysis to calculate the connectivity of habitat locations. The functions of geohabnet are built to conduct a habitat connectivity analysis relying on geographic parameters (spatial resolution and spatial extent), dispersal parameters (in two commonly used dispersal kernels: inverse power law and negative exponential models), and network parameters (link weight thresholds and network metrics). The functionality and main extensions provided by the functions in geohabnet to habitat connectivity analysis are a) Capability to easily calculate the connectivity of locations in a landscape using a single function, such as sensitivity_analysis() or msean(). b) As backbone datasets, the geohabnet package supports the use of two publicly available global datasets to calculate cropland density. The backbone datasets in the geohabnet package include crop distribution maps from Monfreda, C., N. Ramankutty, and J. A. Foley (2008) <doi:10.1029/2007gb002947> "Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022" and International Food Policy Research Institute (2019) <doi:10.7910/DVN/PRFF8V> "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4". Users can also provide any other geographic dataset that represents host density. c) Because the geohabnet package allows R users to provide maps of host density (as originally in Xing et al (2021)), host landscape density (representing the geographic distribution of either crops or wild species), or habitat distribution (such as host landscape density adjusted by climate suitability) as inputs, we propose the term habitat connectivity. d) The geohabnet package allows R users to customize parameter values in the habitat connectivity analysis, facilitating context-specific (pathogen- or pest-specific) analyses. e) The geohabnet package allows users to automatically visualize maps of the habitat connectivity of locations resulting from a sensitivity analysis across all customized parameter combinations. The primary functions are msean() and sensitivity analysis(). Most functions in geohabnet provide three main outcomes: i) A map of mean habitat connectivity across parameters selected by the user, ii) a map of variance of habitat connectivity across the selected parameters, and iii) a map of the difference between the ranks of habitat connectivity and habitat density. Each function can be used to generate these maps as final outcomes. Each function can also provide intermediate outcomes, such as the adjacency matrices built to perform the analysis, which can be used in other network analysis. Refer to article at <https://garrettlab.github.io/HabitatConnectivity/articles/analysis.html> to see examples of each function and how to access each of these outcome types. To change parameter values, the file called parameters.yaml stores the parameters and their values, can be accessed using get_parameters() and set new parameter values with set_parameters()'. Users can modify up to ten parameters.

Access to The Guardian newspaper's open API <https://open-platform.theguardian.com/>, containing all articles published in The Guardian from 1999 to the present, including article text, metadata, tags and contributor information. An API key and registration is required.

It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).

This package provides tools for decomposing Global Value Chain (GVC) participation and value-added trade. It implements the frameworks proposed by Borin and Mancini (2023) 10.1080/09535314.2022.2153221> for source-based and sink-based decompositions, and by Borin, Mancini, and Taglioni (2025) 10.1093/wber/lhaf017> for tripartite and output-based GVC measures.

Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify.shenoylab.com/>. Citation: <doi:10.5281/zenodo.5136508>.

Annotation helper functions for publication-quality ggplot2 visualisation. These functions make it easier to annotate plots in a way that stays consistent with the set theme.