Running Focused Identification of the Germplasm Strategy (FIGS) to make best subsets from Genebank Collection.

Simple handling of survey data. Smart handling of meta-information like e.g. variable-labels value-labels and scale-levels. Easy access and validation of meta-information. Useage of value labels and values respectively for subsetting and recoding data.

Biodiversity is a multifaceted concept covering different levels of organization from genes to ecosystems. iNEXT.3D extends iNEXT to include three dimensions (3D) of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional diversity (FD). This package provides functions to compute standardized 3D diversity estimates with a common sample size or sample coverage. A unified framework based on Hill numbers and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates are in the same units of species/lineage equivalents and can be meaningfully compared. The package features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of 3D diversity across individual assemblages. Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) <doi:10.1111/2041-210X.13682> for more details.

An implementation of the Canny Edge Detector for detecting edges in images. The package provides an interface to the algorithm available at <https://github.com/Neseb/canny>.

Call the data wrappers for Izmir Metropolitan Municipality's Open Data Portal. This will return all datasets formatted as Excel files (.csv or .xlsx), as well as datasets that require an API key.

Develops stochastic models based on the Theory of Island Biogeography (TIB) of MacArthur and Wilson (1967) <doi:10.1023/A:1016393430551> and extensions. It implements methods to estimate colonization and extinction rates (including environmental variables) given presence-absence data, simulates community assembly, and performs model selection.

This package provides a port of Python's excellent itertools module to R for efficient looping.

This package provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).

One function to read files. One function to write files. One function to direct plots to screen or file. Automatic file format inference and directory structure creation.

This package provides functions to to compute a continuum of information-based measures for quantifying the temporal stability of populations, communities, and ecosystems, as well as their associated synchrony, based on species (or species assemblage) biomass or other key variables. When biodiversity data are available, the package also enables the assessment of the corresponding diversityâ stability relationships. All measures are applicable in both temporal and spatial contexts. The theoretical and methodological background is detailed in Chao et al. (2025) <doi:10.1101/2025.08.20.671203>.

This package implements approximate Bayesian inference for Structural Equation Models (SEM) using a custom adaptation of the Integrated Nested Laplace Approximation as described in Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x>. Provides a computationally efficient alternative to Markov Chain Monte Carlo (MCMC) for Bayesian estimation, allowing users to fit latent variable models using the lavaan syntax.

Run quality checks on data sets using the same checks that are conducted on the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk>.

This package provides functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].

This package provides a simple wrapper around the ical.js library executing Javascript code via V8 (the Javascript engine driving the Chrome browser and Node.js and accessible via the V8 R package). This package enables users to parse iCalendar files ('.ics', .ifb', .iCal', .iFBf') into lists and data.frames to ultimately do statistics on events, meetings, schedules, birthdays, and the like.

Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. Multivariate methods are implemented with appropriate data transformation.

Using shiny to demo igraph package makes learning graph theory easy and fun.

This package provides a basic set of compact widgets for shiny apps which occupy less space and can appear inline with surrounding text.

This package provides a set of functions for performing null hypothesis testing on samples of persistence diagrams using the theory of permutations. Currently, only two-sample testing is implemented. Inputs can be either samples of persistence diagrams themselves or vectorizations. In the former case, they are embedded in a metric space using either the Bottleneck or Wasserstein distance. In the former case, persistence data becomes functional data and inference is performed using tools available in the fdatest package. Main reference for the interval-wise testing method: Pini A., Vantini S. (2017) "Interval-wise testing for functional data" <doi:10.1080/10485252.2017.1306627>. Main reference for inference on populations of networks: Lovato, I., Pini, A., Stamm, A., & Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>.

This package provides tools for parsing NOAA Integrated Surface Data ('ISD') files, described at <https://www.ncdc.noaa.gov/isd>. Data includes for example, wind speed and direction, temperature, cloud data, sea level pressure, and more. Includes data from approximately 35,000 stations worldwide, though best coverage is in North America/Europe/Australia. Data is stored as variable length ASCII character strings, with most fields optional. Included are tools for parsing entire files, or individual lines of data.

Reads the output of the PerkinElmer InForm software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.

This package provides a test bench for the comparison of missing data imputation methods in uni-variate time series. Imputation methods are compared using different error metrics. Proposed imputation methods and alternative error metrics can be used.

This package provides a multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. IMIX can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. IMIX uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. IMIX features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.

Allows access to data from the Rio de Janeiro Public Security Institute (ISP), such as criminal statistics, data on gun seizures and femicide. The package also contains the spatial data of Pacifying Police Units (UPPs) and Integrated Public Safety Regions, Areas and Circumscriptions.

The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.