This package provides functions to fit two-dimensional Gaussian functions, predict values from fits, and produce plots of predicted data via either ggplot2 or base R plotting.

The function combines a scatter plot with ridgelines to better visualise the distribution between sample groups. The plot is created with ggplot2'.

An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with FAIR (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the DublinCore standard (<https://dublincore.org/>), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with ISO/TC211 (<https://committee.iso.org/home/tc211>) and OGC (<https://www.ogc.org/standards/>) geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. PostGIS'), metadata catalogues (eg. GeoNetwork', CSW servers), data servers (eg. GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of EML metadata, and their management with DataOne servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of CF conventions, NetCDF data format and OPeNDAP access protocol, and their management with Thredds servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', DataCite'), to facilitate the publication of data within (meta)data repositories such as Zenodo (<https://zenodo.org>) or DataVerse (<https://dataverse.org/>). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference Zenodo DOI in GeoNetwork'/'GeoServer metadata, or vice versa reference GeoNetwork'/'GeoServer links in Zenodo or EML metadata). The use of standardized configuration files ('JSON or YAML formats) allow fully reproducible workflows to facilitate the work of data and information managers.

Simplify your R data analysis and data visualization workflow by turning your data frame into an interactive Tableau'-like interface, leveraging the graphic-walker JavaScript library and the htmlwidgets package.

Fit generalized linear mixed models (GLMMs) with normal random effects using first-order Laplace, fully exponential Laplace (FEL) with mean-only corrections, and FEL with mean and covariance corrections in the E-step of an expectation-maximization (EM) algorithm. The current development version provides a matrix-based interface (y, X, Z) and supports binary logit and probit, and Poisson log-link models. An EM framework is used to update fixed effects, random effects, and a single variance component tau^2 for G = tau^2 I, with staged approximations (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A pseudo-likelihood engine glmmFEL_pl() implements the working-response / working-weights linearization approach of Wolfinger and O'Connell (1993) <doi:10.1080/00949659308811554>, and is adapted from the implementation used in the RealVAMS package (Broatch, Green, and Karl (2018)) <doi:10.32614/RJ-2018-033>. The FEL implementation follows Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019> and related work (e.g., Tierney, Kass, and Kadane (1989) <doi:10.1080/01621459.1989.10478824>; Rizopoulos, Verbeke, and Lesaffre (2009) <doi:10.1111/j.1467-9868.2008.00704.x>; Steele (1996) <doi:10.2307/2532845>). Package code was drafted with assistance from generative AI tools.

This package provides functions to read in the geometry format under the Neuroimaging Informatics Technology Initiative ('NIfTI'), called GIFTI <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data.

Statistical methodology for sparse multivariate extreme value models. Methods are provided for exact simulation and statistical inference for multivariate Pareto distributions on graphical structures as described in the paper Graphical Models for Extremes by Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.

Extends ggplot2 functionality to the partykit package. ggparty provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class party'.

This package provides a Bayesian statistical model for estimating child (under-five age group) and adult (15-60 age group) mortality. The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. This package uses Markov Chain Monte Carlo (MCMC) to sample from posterior probability distribution by rstan package in R. Details are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012) <doi:10.1016/S0140-6736(12)61719-X>, Wang H, Liddell CA, Coates MM, et al. (2014) <doi:10.1016/S0140-6736(14)60497-9> and Mohammadi, Parsaeian, Mehdipour et al. (2017) <doi:10.1016/S2214-109X(17)30105-5>.

Accurate and computationally efficient p-value calculation methods for a general family of Fisher type statistics (GFisher). The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new p-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution approximation. The technical details can be found in Hong Zhang and Zheyang Wu (2020) <arXiv:2003.01286>.

This package provides a native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.

Convert the chip ID of GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> to GeneBank Accession and ENTREZID <http://www.ncbi.nlm.nih.gov/gene>.

Provide specialized ggplot2 layers and scales for spatial uncertainty visualization, including bivariate choropleth maps, pixel maps, glyph maps, and exceedance probability maps.

The functionality provided by this package is an expansion of the code of the statebins package, created by B. Rudis (2022), <doi:10.32614/CRAN.package.statebins>. It allows for the creation of square choropleths for the entire world, provided an appropriate specified grid is supplied.

This package provides methods for automatic calculation of gene scores from gene count tables, including a Z-score method that requires a table of samples being scored and a count table with control samples; a geometric mean method that does not rely on control samples; and a principal component-based method that summarizes gene expression using user-selected principal components. The Z-score and geometric mean approaches are described in Kim et al. (2018) <doi:10.1089/jir.2017.0127>.

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.

Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.

This package provides a ggplot2'-consistent approach to generating 2D displays of volumetric brain imaging data. Display data from multiple NIfTI images using standard ggplot2 conventions such scales, limits, and themes to control the appearance of displays. The resulting plots are returned as patchwork objects, inheriting from ggplot', allowing for any standard modifications of display aesthetics supported by ggplot2'.

This package provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day ('GSOD') weather data from the from the USA National Centers for Environmental Information ('NCEI'). Units are converted from from United States Customary System ('USCS') units to International System of Units ('SI'). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative humidity ('RH') are calculated from the original data using the improved August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags. For information on the GSOD data from NCEI', please see the GSOD readme.txt file available from, <https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.

This package provides a gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48.

Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange <https://www.b3.com.br/>. All data is downloaded and imported from CVM's public ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/>.

Read examples with interlinear glosses from files or from text and print them in a way compatible with both Latex and HTML outputs.

Duct tape the quanteda ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of quanteda.textmodels and provides a function to setup the Python environment to use the pretrained models from Hugging Face <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.

This package provides a collection of several geoms to create graphics, using ggplot2 and the Cartesian coordinate system. You use the familiar mapping Grammar of Graphics without the need to do another transformation into polar coordinates.