Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.

Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.

Gives design points from a sequential full factorial-based Latin hypercube design, as described in Duan, Ankenman, Sanchez, and Sanchez (2015, Technometrics, <doi:10.1080/00401706.2015.1108233>).

Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations. SubVis requires the availability of a web browser.

This package provides data frames that hold certain columns and attributes persistently for data processing in dplyr'.

Tidies up the forecasting modeling and prediction work flow, extends the broom package with sw_tidy', sw_glance', sw_augment', and sw_tidy_decomp functions for various forecasting models, and enables converting forecast objects to "tidy" data frames with sw_sweep'.

R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.

Operationalizes the identification problem of which subset of items should be kept in the shortened version of a said psychometric instrument to best represent the set of items comprised in the original version of the said psychometric instrument.

This package provides a topological version of k-NN: An abstract model is build as 2-dimensional self-organising map. Samples of unknown class are predicted by mapping them on the SOM and analysing class membership of neurons in the neighbourhood.

Read in SAS Data ('.sas7bdat Files) into Apache Spark from R. Apache Spark is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the spark-sas7bdat Spark package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process SAS data in parallel using Spark'. Hereby allowing to execute dplyr statements in parallel on top of SAS data.

Extract the signed backbones of intrinsically dense weighted networks based on the significance filter and vigor filter as described in the following paper. Please cite it if you find this software useful in your work. Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. <arXiv:2012.05216>.

This package provides a platform for computing competition indices and experimenting with spatially explicit individual-based vegetation models.

Herein, we provide a broad variety of functions which are useful for handling, manipulating, and visualizing satellite-based remote sensing data. These operations range from mere data import and layer handling (eg subsetting), over Raster* typical data wrangling (eg crop, extend), to more sophisticated (pre-)processing tasks typically applied to satellite imagery (eg atmospheric and topographic correction). This functionality is complemented by a full access to the satellite layers metadata at any stage and the documentation of performed actions in a separate log file. Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat Global Land Survey data set.

Omics data (e.g. transcriptomics, proteomics, metagenomics...) offer a detailed and multi-dimensional perspective on the molecular components and interactions within complex biological (eco)systems. Analyzing these data requires adapted procedures, which are implemented as steps according to the recipes package.

This package provides a scalable Gibbs sampling implementation for high dimensional Bayesian regression with the continuous spike-and-slab prior. Niloy Biswas, Lester Mackey and Xiao-Li Meng, "Scalable Spike-and-Slab" (2022) <arXiv:2204.01668>.

This package provides tools for the optimization of stratified sampling design. It determines a stratification of a sampling frame that minimizes sample cost while satisfying precision constraints in a multivariate and multidomain context. The approach relies on a genetic algorithm; each candidate partition of the frame is an individual whose fitness is evaluated via the Bethel-Chromy allocation to meet target precisions. Functions support analysis of optimization results, labeling of the frame with new strata, and drawing a sample according to the optimal allocation. Algorithmic components adapt code from the genalg package. See M. Ballin and G. Barcaroli (2020) "R package SamplingStrata: new developments and extension to Spatial Sampling" <doi:10.48550/arXiv.2004.09366>.

Within epidemic outbreaks, infections grow and decline differently between regions, and the velocity of spatial spread differs between countries. The swash library offers a set of model-based analyses for these topics. Spread velocity may be analysed with the Swash-Backwash Model for the Single Epidemic Wave and corresponding functions for bootstrap confidence intervals, country comparison, and visualization of results. Differences in epidemic growth between regions may be analysed using logistic growth models, exponential growth models, Hawkes processes and breakpoint analyses. All functionalities are accessed by the class "infpan" for infections panel data defined in this package, which is built from a data.frame provided by the user.

Estimate the regression coefficients and the baseline hazard of proportional hazard Cox models with left, right or interval censored survival data using maximum penalised likelihood. A non-parametric smooth estimate of the baseline hazard function is provided.

Calculates and plots the SiZer map for scatterplot data. A SiZer map is a way of examining when the p-th derivative of a scatterplot-smoother is significantly negative, possibly zero or significantly positive across a range of smoothing bandwidths.

Fit a spatial-temporal occupancy models using a probit formulation instead of a traditional logit model.

Do multi-gene descent probabilities (Thompson, 1983, <doi:10.1098/rspb.1983.0072>) and special cases thereof (Thompson, 1986, <doi:10.1002/zoo.1430050210>) including inbreeding and kinship coefficients. But does much more: probabilities of any set of genes descending from any other set of genes.

This package provides methods to integrate functions over m-dimensional simplices in n-dimensional Euclidean space. There are exact methods for polynomials and adaptive methods for integrating an arbitrary function.

Evaluates the statistical coherence of existing administrative partitions (e.g. inter-municipal groupings, districts) by identifying spatial units whose attributive profile is more similar to a neighbouring group than to their own. Border units are iteratively reassigned to the group they are most affine with, based on Euclidean or Mahalanobis distance computed on user-supplied numeric variables, with optional per-variable weighting and standardisation. Spatial contiguity is enforced throughout: isolated candidates are reintegrated into their original group, disconnected fragments are resolved, and empty groups are restored. Convergence is monitored via an eta-squared cohesion criterion. The resulting partition can be compared to the original administrative delineation using multilevel models, providing a quantitative measure of boundary inefficiency.

Interactive visualizations of graphs created with the igraph package using a htmlwidgets wrapper for the sigma.js network visualization v2.4.0 <https://www.sigmajs.org/>, enabling to display several thousands of nodes. While several R packages have been developed to interface sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the sigmaNet package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic sigma.js visualizations by overriding the underlying JavaScript code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding sigma.js code include toggling node visibility when clicked using a node attribute and highlighting specific edges. sigma.js is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available.