One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci). clusterhap groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, clusterhap groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, clusterhap calculates such probability from relative frequencies.

This package provides a tool that implements the clustering algorithms from mothur (Schloss PD et al. (2009) <doi:10.1128/AEM.01541-09>). clustur make use of the cluster() and make.shared() command from mothur'. Our cluster() function has five different algorithms implemented: OptiClust', furthest', nearest', average', and weighted'. OptiClust is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) <doi:10.1128/mspheredirect.00073-17>). The make.shared() command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently.

Facilitates dynamic exploration of text collections through an intuitive graphical user interface and the power of regular expressions. The package contains 1) a helper function to convert a data frame to a corporaexplorerobject and 2) a Shiny app for fast and flexible exploration of a corporaexplorerobject'. The package also includes demo apps with which one can explore Jane Austen's novels and the State of the Union Addresses (data from the janeaustenr and sotu packages respectively).

Computes the cosine-correlation coefficient for measuring the degree of linear dependence among variables in a multidimensional context. The package implements the generalized cosine-correlation theorem for p-1 variables, providing a quantitative assessment of interrelationships within experimental frameworks. This methodology extends classical correlation measures to higher-dimensional spaces using a dimensional exploration approach based on time scale calculus.

This package provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). CREAM uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. CREAM considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.

Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.

This package provides functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm. Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.

Uses data from the EPSG Registry to look up suitable coordinate reference system transformations for spatial datasets in R. Returns a data frame with CRS codes that can be used for CRS transformation and mapping projects. Please see the EPSG Dataset Terms of Use at <https://epsg.org/terms-of-use.html> for more information.

It is assumed that psychological distances between the categories are equal for the measurement instruments consisted of polytomously scored items. According to Muraki, this assumption must be tested. In the examination process of this assumption, the fit indexes are obtained and evaluated. This package provides that this assumption is removed. By with this package, the converted scale values of all items in a measurement instrument can be calculated by estimating a category parameter set for each item. Thus, the calculations can be made without any need to usage of the common category parameter set. Through this package, the psychological distances of the items are scaled. The scaling of a category parameter set for each item cause differentiation of score of the categories will be got from items. Also, the total measurement instrument score of an individual can be calculated according to the scaling of item score categories by with this package.This package provides that the place of individuals related to the structure to be measured with a measurement instrument consisted of polytomously scored items can be reveal more accurately. In this way, it is thought that the results obtained about individuals can be made more sensitive, and the differences between individuals can be revealed more accurately. On the other hand, it can be argued that more accurate evidences can be obtained regarding the psychometric properties of the measurement instruments.

This package provides methods for analyzing (cell) motion in two or three dimensions. Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>, Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>, Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>, Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>, Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>, Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>, Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.

This tool performs pairwise correlation analysis and estimate causality. Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.

Proposes Seq2seq Time-Feature Analysis using an Encoder-Decoder to project into latent space and a Forward Network to predict the next sequence.

An interactive document on the topic of cluster analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/ClusterAnalysis/>.

This package provides a collection of useful helper routines developed by students of the Center for Mathematical Research, Stankin, Moscow.

This package performs the colocalisation tests described in Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>, Wallace (2020) <doi:10.1371/journal.pgen.1008720>, Wallace (2021) <doi:10.1371/journal.pgen.1009440>.

This package provides a common misconception is that the Hochberg procedure comes up with adequate overall type I error control when test statistics are positively correlated. However, unless the test statistics follow some standard distributions, the Hochberg procedure requires a more stringent positive dependence assumption, beyond mere positive correlation, to ensure valid overall type I error control. To fill this gap, we formulate statistical tests grounded in rank correlation coefficients to validate fulfillment of the positive dependence through stochastic ordering (PDS) condition. See Gou, J., Wu, K. and Chen, O. Y. (2024). Rank correlation coefficient based tests on positive dependence through stochastic ordering with application in cancer studies, Technical Report.

This package provides a convenient R wrapper to the Comet API, which is a cloud platform allowing you to track, compare, explain and optimize machine learning experiments and models. Experiments can be viewed on the Comet online dashboard at <https://www.comet.com>.

Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2025; <doi:10.48550/arXiv.2405.17290>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).

Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) <doi:10.1177/0962280217710835> and Dumuid et al. (2017b) <doi:10.1177/0962280217737805>.

Defines the classes used for "class comparison" problems in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class comparison includes tests for differential expression; see Simon's book for details on typical problem types.

Allows printing of character strings as messages/warnings/etc. with ASCII animals, including cats, cows, frogs, chickens, ghosts, and more.

Evaluates predictive performance under feature-level missingness in repeated-measures continuous glucose monitoring-like data. The benchmark injects missing values at user-specified rates, imputes incomplete feature matrices using an iterative chained-equations approach inspired by multivariate imputation by chained equations (MICE; Azur et al. (2011) <doi:10.1002/mpr.329>), fits Random Forest regression models (Breiman (2001) <doi:10.1023/A:1010933404324>) and k-nearest-neighbor regression models (Zhang (2016) <doi:10.21037/atm.2016.03.37>), and reports mean absolute percentage error and R-squared across missingness rates.

Gain access to the Spark Catalog API making use of the sparklyr API. Catalog <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).

Method for visualizing proportions between objects of different sizes. The proportions are drawn as circles with different diameters, which makes them ideal for visualizing proportions between planets.