Automates the process of adding, committing, and pushing changes to a git repository using commit messages generated by passing the git diff output to the OpenAI GPT-3.5 Turbo model (<https://platform.openai.com/docs/models/gpt-3>).

This package provides methods to analyse experimental agriculture data, from data synthesis to model selection and visualisation. The package is named after W.S. Gosset aka â Studentâ , a pioneer of modern statistics in small sample experimental design and analysis.

Facilitates the creation of page layout visualizations in which words are represented as rectangles with sizes relating to the length of the words. Which then is divided in lines and pages for easy overview of up to quite large texts.

This package provides a collection of functions useful in (vegetation) community analyses and ordinations. Includes automatic species selection for ordination diagrams, NMDS stress/scree plots, species response curves, merging of taxa as well as calculation and sorting of synoptic tables.

Fitting hidden Markov models using automatic differentiation and Laplace approximation, allowing for fast inference and flexible covariate effects (including random effects and smoothing splines) on model parameters. The package is described by Michelot (2025) <doi:10.18637/jss.v114.i05>.

Automatically detects Copy Number Variations (CNV) from Next Generation Sequencing data using a machine learning algorithm, Isolation forest. More details about the method can be found in the paper by Cabello-Aguilar (2022) <doi:10.1101/2022.01.03.474771>.

Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.

This package provides functions for grid square codes in Japan (<https://www.stat.go.jp/english/data/mesh/index.html>). Generates the grid square codes from longitude/latitude, geometries, and the grid square codes of different scales, and vice versa.

This package implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly), and characteristic pattern discovery along with interpretable time series classification.

Create and customize interactive trees using the jQuery jsTree <https://www.jstree.com/> plugin library and the htmlwidgets package. These trees can be used directly from the R console, from RStudio', in Shiny apps and R Markdown documents.

This package performs analysis of Differential Item Functioning (DIF) for dichotomous and polytomous items using an iterative hybrid of ordinal logistic regression and item response theory (IRT) according to Choi, Gibbons, and Crane (2011) <doi:10.18637/jss.v039.i08>.

This package provides a framework to load text and excel files through a shiny graphical interface. It allows renaming, transforming, ordering and removing variables. It includes basic exploratory methods such as the mean, median, mode, normality test, histogram and correlation.

This package performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) <DOI:10419/161931>.

This package provides a four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) <https://drive.google.com/file/d/1a8sV3RJ8VofLWikTDTQ7W4XJ76cEj4Fg/view?usp=drive_link>.

This package implements area level of multivariate small area estimation using Hierarchical Bayesian method under Normal and T distribution. The rjags package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) <doi:10.1016/j.csda.2019.106907>.

This package provides tools for interacting with data from experiments done in microtiter plates. Easily read in plate-shaped data and convert it to tidy format, combine plate-shaped data with tidy data, and view tidy data in plate shape.

Estimation and inference for parameters in a Gaussian copula model, treating the univariate marginal distributions as nuisance parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. This package also provides a semiparametric imputation procedure for missing multivariate data.

This package provides a set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. spsurv includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <arXiv:2003.10548>.

Given raster files directly downloaded from various websites, it generates a raster structure where it merges them if they are tiles of the same scene and classifies them according to their spectral and spatial resolution for easy access by name.

This package implements differential language analysis with statistical tests and offers various language visualization techniques for n-grams and topics. It also supports the text package. For more information, visit <https://r-topics.org/> and <https://www.r-text.org/>.

This package provides a simple interface to search available data provided by Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded based on a search result or from a cart file downloaded from Theia website.

Simulation, exploratory data analysis and Bayesian analysis of the p-order Integer-valued Autoregressive (INAR(p)) and Zero-inflated p-order Integer-valued Autoregressive (ZINAR(p)) processes, as described in Garay et al. (2020) <doi:10.1080/00949655.2020.1754819>.

Doubly ranked tests are nonparametric tests for grouped functional and multivariate data. The testing procedure first ranks a matrix (or three dimensional array) of data by column (if a matrix) or by cell (across the third dimension if an array). By default, it calculates a sufficient statistic for the subject's order within the sample using the observed ranks, taken over the columns or cells. Depending on the number of groups, G, the summarized ranks are then analyzed using either a Wilcoxon Rank Sum test (G = 2) or a Kruskal-Wallis (G greater than 2).