This package implements the orthogonal reparameterization approach recommended by Lancaster (2002) to estimate dynamic panel models with fixed effects (and optionally: panel specific intercepts). The approach uses a likelihood-based estimator and produces estimates that are asymptotically unbiased as N goes to infinity, with a T as low as 2.

Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series. Method is based on KubÃ¡Ä ek (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Köning, R., Wimmer, G. and Witkovský, V. (2014) <doi:10.1088/0957-0233/25/11/115001>.

This package provides a function for fitting various penalized Bayesian cumulative link ordinal response models when the number of parameters exceeds the sample size. These models have been described in Zhang and Archer (2021) <doi:10.1186/s12859-021-04432-w>.

This package provides methods to generate a design in the input space that sequentially fills the output space of a black-box function. The output space-filling designs are helpful in inverse design or feature-based modeling problems. See Wang, Shangkun, Adam P. Generale, Surya R. Kalidindi, and V. Roshan Joseph. (2024), Sequential designs for filling output spaces, Technometrics, 66, 65â 76. for details. This work is supported by U.S. National Foundation grant CMMI-1921646.

Creating maps for statistical analysis such as proportional circles, choropleth, typology and flows. Some functions use shiny or leaflet technologies for dynamism and interactivity. The great features are : - Create maps in a web environment where the parameters are modifiable on the fly ('shiny and leaflet technologies). - Create interactive maps through zoom and pop-up ('leaflet technology). - Create frozen maps with the possibility to add labels.

Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included shiny application allows users to simulate outcomes given different ordinal data distributions.

Estimates one-inflated positive Poisson (OIPP) and one-inflated zero-truncated negative binomial (OIZTNB) regression models. A suite of ancillary statistical tools are also provided, including: estimation of positive Poisson (PP) and zero-truncated negative binomial (ZTNB) models; marginal effects and their standard errors; diagnostic likelihood ratio and Wald tests; plotting; predicted counts and expected responses; and random variate generation. The models and tools, as well as four applications, are shown in Godwin, R. T. (2024). "One-inflated zero-truncated count regression models" arXiv preprint <doi:10.48550/arXiv.2402.02272>.

Determine the sea area where the fishing boat operates. The latitude and longitude of geographic coordinates are used to match oceanic areas and economic sea areas. You can plot the distribution map with dotplot() function. Please refer to Flanders Marine Institute (2020) <doi:10.14284/403>.

We provide an R interface to OpenML.org which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. See <https://www.openml.org/guide/api> for more information.

Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance).

Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package alabama', or by sequential quadratic programming using solver slsqp'. Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package cccp'.

Fits community site occupancy models to environmental DNA metabarcoding data collected using spatially-replicated survey design. Model fitting results can be used to evaluate and compare the effectiveness of species detection to find an efficient survey design. Reference: Fukaya et al. (2022) <doi:10.1111/2041-210X.13732>, Fukaya and Hasebe (2025) <doi:10.1002/1438-390X.12219>.

Use health data in the Observational Medical Outcomes Partnership Common Data Model format in Spark'. Functionality includes creating all required tables and fields and creation of a single reference to the data. Native Spark functionality is supported.

An unofficial wrapper for okx exchange v5 API <https://www.okx.com/docs-v5/en/>, including REST API and WebSocket API.

Distributed reproducible computing framework, adopting ideas from git, docker and other software. By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans.

Design and analysis of confirmatory adaptive clinical trials using the optimal conditional error framework according to Brannath and Bauer (2004) <doi:10.1111/j.0006-341X.2004.00221.x>. An extension to the optimal conditional error function using interim estimates as described in Brannath and Dreher (2024) <doi:10.48550/arXiv.2402.00814> and functions to ensure that the resulting conditional error function is non-increasing are also available.

Interact seamlessly with Open Target GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of gene, disease, drug, and genetic data. For more information about the Open Target API (<https://platform.opentargets.org/api>).

Makes it easy to display descriptive information on a data set. Getting an easy overview of a data set by displaying and visualizing sample information in different tables (e.g., time and scope conditions). The package also provides publishable LaTeX code to present the sample information.

This package provides a collection of functions that aid in calculating the optimum time to stock hatchery reared fish into a body of water given the growth, mortality and cost of raising a particular number of individuals to a certain length.

Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) <https://proceedings.mlr.press/v180/ni22a/ni22a.pdf> "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530â 1540".

Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite, and topographic map servers are directly supported. Additionally raster maps may be constructed using custom tile servers. Maps can be plotted using either base graphics, or ggplot2. This package is not affiliated with the OpenStreetMap.org mapping project.

Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.

This package provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data. The package simplifies preprocessing through automated imputation, scaling, and principal component analysis (PCA)-based exploratory analysis, enabling researchers to prepare omics datasets efficiently for downstream statistical and machine learning analyses.

This package provides a tool for interactive exploration of the results from omics experiments to facilitate novel discoveries from high-throughput biology. The software includes R functions for the bioinformatician to deposit study metadata and the outputs from statistical analyses (e.g. differential expression, enrichment). These results are then exported to an interactive JavaScript dashboard that can be interrogated on the user's local machine or deployed online to be explored by collaborators. The dashboard includes sortable tables, interactive plots including network visualization, and fine-grained filtering based on statistical significance.