It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).

This package performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.

Plots survival models from the survival package. Additionally, it plots curves of multistate models from the mstate package. Typically, a plot is drawn by the sequence survplot(), confIntArea(), survCurve() and nrAtRisk(). The separation of the plot in this 4 functions allows for great flexibility to make a custom plot for publication.

Implement different Item Response Theory (IRT) based procedures for the development of static short test forms (STFs) from a test. Two main procedures are considered (Epifania, Anselmi & Robusto, 2022 <doi:10.1007/978-3-031-27781-8_7>). The procedures differ in how the most informative items are selected for the inclusion in the STF, either by considering their item information functions without any reference to any specific latent trait level (benchmark procedure) or by considering their information with respect to specific latent trait levels, denoted as theta targets (theta target procedure). Three methods are implemented for the definition of the theta targets: (i) as the midpoints of equal intervals on the latent trait, (ii) as the centroids of the clusters obtained by clustering the latent trait, and (iii) as user-defined values. Importantly, the number of theta targets defines the number of items included in the STF. For further details on the procedure, please refer to Epifania, Anselmi & Robusto (2022) <doi:10.1007/978-3-031-27781-8_7>.

Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID). It allow sequence-space-time host and viral population data storage, indexation and querying.

We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level.

Encapsulates a number of spatially balanced sampling algorithms, namely, Balanced Acceptance Sampling (equal, unequal, seed point, panels), Halton frames (for discretizing a continuous resource), Halton Iterative Partitioning (equal probability) and Simple Random Sampling. Robertson, B. L., Brown, J. A., McDonald, T. and Jaksons, P. (2013) <doi:10.1111/biom.12059>. Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2017) <doi:10.1016/j.spl.2017.05.004>. Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2018) <doi:10.1007/s10651-018-0406-6>. Robertson, B. L., van Dam-Bates, P. and Gansell, O. (2021a) <doi:10.1007/s10651-020-00481-1>. Robertson, B. L., Davies, P., Gansell, O., van Dam-Bates, P., McDonald, T. (2025) <doi:10.1111/anzs.12435>.

SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.

Semantic Versions allow for standardized management versions. This package implements semantic versioning handling in R. using R6 to create a mutable object that can handle deciphering and checking versions.

Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.

This package provides functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014) <doi:10.5194/gmd-7-1919-2014>.

Allows the user to animate shiny elements when scrolling to view them. The animations are activated using the scrollrevealjs library. See <https://scrollrevealjs.org/> for more information.

This package provides crop yield and meteorological data for Ontario, Canada. Includes functions for fitting and predicting data using spatio-temporal models, as well as tools for visualizing the results. The package builds upon existing R packages, including copula (Hofert et al., 2025) <doi:10.32614/CRAN.package.copula>, and bsts (Scott, 2024) <doi:10.32614/CRAN.package.bsts>.

Create Shiny Apps with collapsible vertical panels. This package provides a new visual arrangement for elements on top of Shiny'. Use the expand and collapse capabilities to leverage web applications with many elements to focus the user attention on the panel of interest.

Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate a normal mean or the difference between two normal means. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of normal means are provided. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L. and Bélisle P. (1997) <https://www.jstor.org/stable/2988525>.

An R wrapper for pulling data from the Spotify Web API <https://developer.spotify.com/documentation/web-api/> in bulk, or post items on a Spotify user's playlist.

Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.

There are numerous places to create and download color palettes. These are usually shared in Adobe swatch file formats of some kind. There is also often the need to use standard palettes developed within an organization to ensure that aesthetics are carried over into all projects and output. Now there is a way to read these swatch files in R and avoid transcribing or converting color values by hand or or with other programs. This package provides functions to read and inspect Adobe Color ('ACO'), Adobe Swatch Exchange ('ASE'), GIMP Palette ('GPL'), OpenOffice palette ('SOC') files and KDE Palette ('colors') files. Detailed descriptions of Adobe Color and Swatch Exchange file formats as well as other swatch file formats can be found at <http://www.selapa.net/swatches/colors/fileformats.php>.

The stochastic (also called on-line) version of the Self-Organising Map (SOM) algorithm is provided. Different versions of the algorithm are implemented, for numeric and relational data and for contingency tables as described, respectively, in Kohonen (2001) <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005) <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004) <doi:10.1016/j.neunet.2004.07.010>. The package also contains many plotting features (to help the user interpret the results), can handle (and impute) missing values and is delivered with a graphical user interface based on shiny'.

This package provides tools for obtaining, processing, and visualizing spectral reflectance data for the user-defined land or water surface classes for visual exploring in which wavelength the classes differ. Input should be a shapefile with polygons of surface classes (it might be different habitat types, crops, vegetation, etc.). The Sentinel-2 L2A satellite mission optical bands pixel data are obtained through the Google Earth Engine service (<https://earthengine.google.com/>) and used as a source of spectral data.

This package provides a novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. SpaTopic is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see <https://xiyupeng.github.io/SpaTopic/>.

Discovers synergistic gene pairs in single-cell RNA-seq and spatial transcriptomics data. Unlike conventional pairwise co-expression analyses that rely on a single correlation metric, scPairs integrates 14 complementary metrics across five orthogonal evidence layers to compute a composite synergy score with optional permutation-based significance testing. The five evidence layers span cell-level co-expression (Pearson, Spearman, biweight midcorrelation, mutual information, ratio consistency), neighbourhood-aware smoothing (KNN-smoothed correlation, neighbourhood co-expression, cluster pseudo-bulk, cross-cell-type, neighbourhood synergy), prior biological knowledge (GO/KEGG co-annotation Jaccard, pathway bridge score), trans-cellular interaction, and spatial co-variation (Lee's L, co-location quotient). This multi-scale design enables researchers to move beyond simple co-expression towards a comprehensive characterisation of cooperative gene regulation at transcriptomic and spatial resolution. For more information, see the package documentation at <https://github.com/zhaoqing-wang/scPairs>.

Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) <doi: 10.1007/s10985-021-09544-2> and Luo et al. (2023) <doi:10.1177/09622802231181471>.

Various functions for creating spherical coordinate system plots via extensions to rgl.