Simulates from discrete and continuous target distributions using geometric Metropolis-Hastings (MH) algorithms. Users specify the target distribution by an R function that evaluates the log un-normalized pdf or pmf. The package also contains a function implementing a specific geometric MH algorithm for performing high-dimensional Bayesian variable selection.

Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. The package enables rapid evaluation of likelihood functions for both single- and multi-subject models across trial-level data. It also offers fast initialisation of starting parameters for genetic sampling with many Markov chains, facilitating estimation in complex models typically found in experimental psychology and behavioural science. These optimisations help reduce computational overhead in large-scale model fitting tasks.

Facilitates efficient visualization of Relative Synonymous Codon Usage patterns across species. Based on analytical outputs from codonW', MEGA', and Phylosuite', it supports multi-species RSCU comparisons and allows users to explore visual analysis of structurally similar datasets.

R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.

Segmentation and classification procedures for data from the Activinsights GENEActiv <https://activinsights.com/technology/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the rpart package to create classification models.

Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.

Lightweight way to read NumPy .npy and .npz files in R. All data types supported by NumPy', with all sizes (converted internally to R native size), both C and Fortran order, and any shape, up to an arbitrary number of dimensions, are supported.

This package implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.

Read, analyze, modify, and write GAMS (General Algebraic Modeling System) data. The main focus of gamstransfer is the highly efficient transfer of data with GAMS <https://www.gams.com/>, while keeping these operations as simple as possible for the user. The transfer of data usually takes place via an intermediate GDX (GAMS Data Exchange) file. Additionally, gamstransfer provides utility functions to get an overview of GAMS data and to check its validity.

This package provides methods to construct and power group sequential clinical trial designs for outcomes at multiple times. Outcomes at earlier times provide information on the final (primary) outcome. A range of recruitment and correlation models are available as are methods to simulate data in order to explore design operating characteristics. For more details see Parsons (2024) <doi:10.1186/s12874-024-02174-w>.

This package provides a grammar of graphics approach for visualizing summary statistics from multiple Genome-wide Association Studies (GWAS). It offers geneticists, bioinformaticians, and researchers a powerful yet flexible tool for illustrating complex genetic associations using data from various GWAS datasets. The visualizations can be extensively customized, facilitating detailed comparative analysis across different genetic studies. Reference: Uffelmann, E. et al. (2021) <doi:10.1038/s43586-021-00056-9>.

This package performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <DOI:10.1093/bioinformatics/bty472>).

Interact with the Google Cloud Vision <https://cloud.google.com/vision/> API in R. Part of the cloudyr <https://cloudyr.github.io/> project.

Provide estimation and data generation tools for a generalization of the transmuted distributions discussed in Shaw and Buckley (2007). See <doi:10.48550/arXiv.0901.0434> for more information.

Writes SAS code to get predicted values from every tree of a gbm.object.

Likelihood-based boosting approaches for generalized mixed models are provided.

This package provides tools for geometric morphometric analyses and multidimensional data. Implements methods for morphological disparity analysis using bootstrap and rarefaction, as reviewed in Foote (1997) <doi:10.1146/annurev.ecolsys.28.1.129>. Includes integration and modularity testing, following Fruciano et al. (2013) <doi:10.1371/journal.pone.0069376>, using Escoufier's RV coefficient as test statistic as well as two-block partial least squares - PLS, Rohlf and Corti (2000) <doi:10.1080/106351500750049806>. Also includes vector angle comparisons, orthogonal projection for data correction (Burnaby (1966) <doi:10.2307/2528217>; Fruciano (2016) <doi:10.1007/s00427-016-0537-4>), and parallel analysis for dimensionality reduction (Buja and Eyuboglu (1992) <doi:10.1207/s15327906mbr2704_2>).

This package performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) <doi:10.1101/2020.02.16.20023465>.

This package provides a ggplot2 extension for drawing Hinton diagrams, a visualisation technique for numerical matrices in which the area of each square is proportional to the magnitude of the corresponding entry. For signed data, white squares indicate positive values and black squares indicate negative values on a grey background. Hinton diagrams are especially useful for visualising PCA weight matrices, correlation matrices, and transition matrices.

Reads corporate data such as board composition and compensation for companies traded at B3, the Brazilian exchange <https://www.b3.com.br/>. All data is downloaded and imported from the ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/DOC/FRE/>.

Analyze small-sample clustered or longitudinal data with binary outcome using modified generalized estimating equations (GEE) with bias-adjusted covariance estimator. The package provides any combination of three GEE methods and 12 covariance estimators.

This package provides deterministic forecasting for weekly, monthly, quarterly, and yearly time series using the Generalized Adaptive Capped Estimator. The method includes preprocessing for missing and extreme values, extraction of multiple growth components (including long-term, short-term, rolling, and drift-based signals), volatility-aware asymmetric capping, optional seasonal adjustment via damped and normalized seasonal factors, and a recursive forecast formulation with moderated growth. The package includes a user-facing forecasting interface and a plotting helper for visualization. Related forecasting background is discussed in Hyndman and Athanasopoulos (2021) <https://otexts.com/fpp3/> and Hyndman and Khandakar (2008) <doi:10.18637/jss.v027.i03>. The method extends classical extrapolative forecasting approaches and is suited for operational and business planning contexts where stability and interpretability are important.

Fits the logistic equation to microbial growth curve data (e.g., repeated absorbance measurements taken from a plate reader over time). From this fit, a variety of metrics are provided, including the maximum growth rate, the doubling time, the carrying capacity, the area under the logistic curve, and the time to the inflection point. Method described in Sprouffske and Wagner (2016) <doi:10.1186/s12859-016-1016-7>.

Reproducible, programmatic retrieval of datasets from the GESIS Data Archive. The GESIS Data Archive <https://search.gesis.org> makes available thousands of invaluable datasets, but researchers using these datasets are caught in a bind. The archive's terms and conditions bar dissemination of downloaded datasets to third parties, but to ensure that one's work can be reproduced, assessed, and built upon by others, one must provide access to the raw data one has employed. The gesisdata package cuts this knot by providing registered users with programmatic, reproducible access to GESIS datasets from within R'.