Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`.

Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimensional data and help for preprocessing and preparing analyses.

Reviews other packages during code review by looking at their dependencies, code style, code complexity, and how internally defined functions interact with one another.

Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.

Chromatin immunoprecipitation DNA sequencing results in genomic tracks that show enriched regions or peaks where proteins are bound. This package implements fast C code that computes the true and false positives with respect to a database of annotated region labels.

Offers tools to estimate and visualize levels of major pollutants (CO, NO2, SO2, Ozone, PM2.5 and PM10) across the conterminous United States for user-defined time ranges. Provides functions to retrieve pollutant data from the U.S. Environmental Protection Agencyâ s Air Quality System (AQS) API service <https://aqs.epa.gov/aqsweb/documents/data_api.html> for interactive visualization through a shiny application, allowing users to explore pollutant levels for a given location over time relative to the National Ambient Air Quality Standards (NAAQS).

Implementations of several methods for principal component analysis using the L1 norm. The package depends on COIN-OR Clp version >= 1.17.4. The methods implemented are PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>, L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>, L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>, L1-PCAhp (Visentin, Prestwich and Armagan 2016) <DOI:10.1007/978-3-319-46227-1_37>, wPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>, awPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>, PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and SharpEl1-PCA (Brooks and Dula, submitted).

Allows users to access the Oregon State Prism climate data (<https://prism.nacse.org/>). Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.

This package provides tools for calculating and viewing topological properties of phylogenetic trees.

This package provides data sets and functions for exploration of Pakistan Population Census 2017 (<http://www.pbscensus.gov.pk/>).

This package provides a collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate.

Transforms datetime data into a format ready for analysis. It offers two core functionalities; aggregating data to a higher level interval (thicken) and imputing records where observations were absent (pad).

This package provides a toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the Merge ToolBox (<https://www.record-linkage.de>) with current privacy-preserving techniques.

Connect R to the PhotosynQ platform (<https://photosynq.org>). It allows to login and logout, as well as receive project information and project data. Further it transforms the received JSON objects into a data frame, which can be used for the final data analysis.

Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.

An innovative tool-set that incorporates graph community detection methods into systematic conservation planning. It is designed to enhance spatial prioritization by focusing on the protection of areas with high ecological connectivity. Unlike traditional approaches that prioritize individual planning units, priorCON focuses on clusters of features that exhibit strong ecological linkages. The priorCON package is built upon the prioritizr package <doi:10.32614/CRAN.package.prioritizr>, using commercial and open-source exact algorithm solvers that ensure optimal solutions to prioritization problems.

Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.

This package provides a native R client library for querying the Prometheus time-series database, using the PromQL query language.

This package provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of palaeoverse is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart/>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) <doi: 10.1111/2041-210X.14099>.

This is a collection of data and functions for common metrics in political science research. Data measuring ideology, and functions calculating geographical diffusion and ideological diffusion - geog.diffuse() and ideo.dist(), respectively. Functions derived from methods developed in: Soule and King (2006) <doi:10.1086/499908>, Berry et al. (1998) <doi:10.2307/2991759>, Cruz-Aceves and Mallinson (2019) <doi:10.1177/0160323X20902818>, and Grossback et al. (2004) <doi:10.1177/1532673X04263801>.

This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the gurobi installation vignette of the prioritizr package (available in <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, CPLEX optimization solver can be obtain from IBM CPLEX web page (available here <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, the rcbc R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>, Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015) <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. See the prioriactions website for more information, documentations and examples.

Model-implied simulation-based power estimation (MSPE) for nonlinear (and linear) SEM, path analysis and regression analysis. A theoretical framework is used to approximate the relation between power and sample size for given type I error rates and effect sizes. The package offers an adaptive search algorithm to find the optimal N for given effect sizes and type I error rates. Plots can be used to visualize the power relation to N for different parameters of interest (POI). Theoretical justifications are given in Irmer et al. (2024a) <doi:10.31219/osf.io/pe5bj> and detailed description are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.

This package provides a set of tools to extract bibliographic content from PubMed database using NCBI REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>. It includes functions to search, download, and convert PubMed bibliographic records into data frames compatible with the bibliometrix package. Features include programmatic query building, batch downloading by PMID, citation enrichment via NCBI E-Link, and robust error handling with automatic retry logic.

Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. <doi:10.1214/23-AOS2345>. Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. <doi:10.1111/jtsa.12728>. Armillotta, M., Tsagris, M. and Fokianos, K. (2023). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. <doi:10.32614/RJ-2023-094>.