This package provides a zero dependency package containing functions to declare labels and missing values, coupled with associated functions to create (weighted) tables of frequencies and various other summary measures. Some of the base functions have been rewritten to make use of the specific information about the missing values, most importantly to distinguish between empty NA and declared NA values. Some functions have similar functionality with the corresponding ones from packages "haven" and "labelled". The aim is to ensure as much compatibility as possible with these packages, while offering an alternative in the objects of class "declared".

This package provides methods for working with nominal dates, times, and durations. Base R has sophisticated facilities for handling time, but these can give unexpected results if, for example, timezone is not handled properly. This package provides a more casual approach to support cases which do not require rigorous treatment. It systematically deconstructs the concepts origin and timezone, and de-emphasizes the display of seconds. It also converts among nominal durations such as seconds, hours, days, and weeks. See ?datetime and ?duration for examples. Adapted from metrumrg <http://r-forge.r-project.org/R/?group_id=1215>.

Two Gray Level Co-occurrence Matrix ('GLCM') implementations are included: The first is a fast GLCM feature texture computation based on Python Numpy arrays ('Github Repository, <https://github.com/tzm030329/GLCM>). The second is a fast GLCM RcppArmadillo implementation which is parallelized (using OpenMP') with the option to return all GLCM features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, <doi:10.1109/ICIP.2019.8803652>.

Uses ggplot2 to visualise either (a) a single DNA/RNA sequence split across multiple lines, (b) multiple DNA/RNA sequences, each occupying a whole line, or (c) base modifications such as DNA methylation called by modified bases models in Dorado or Guppy. Functions starting with visualise_<something>() are the main plotting functions, and functions starting with extract_<something>() are key helper functions for reading files and reformatting data. Source code is available at <https://github.com/ejade42/ggDNAvis> and a full non-expert user guide is available at <https://ejade42.github.io/ggDNAvis/>.

Aligns peak based on peak retention times and matches homologous peaks across samples. The underlying alignment procedure comprises three sequential steps. (1) Full alignment of samples by linear transformation of retention times to maximise similarity among homologous peaks (2) Partial alignment of peaks within a user-defined retention time window to cluster homologous peaks (3) Merging rows that are likely representing homologous substances (i.e. no sample shows peaks in both rows and the rows have similar retention time means). The algorithm is described in detail in Ottensmann et al., 2018 <doi:10.1371/journal.pone.0198311>.

Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) <doi:10.1214/22-EJS2051>). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings.

InferCNV is used to explore tumor single cell RNA-Seq data to identify evidence for somatic large-scale chromosomal copy number alterations, such as gains or deletions of entire chromosomes or large segments of chromosomes. This is done by exploring expression intensity of genes across positions of a tumor genome in comparison to a set of reference "normal" cells. A heatmap is generated illustrating the relative expression intensities across each chromosome, and it often becomes readily apparent as to which regions of the tumor genome are over-abundant or less-abundant as compared to that of normal cells.

This package provides a versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (https://clarabel.org/stable/). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, Clarabel handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. Infeasible problems are detected using using a homogeneous embedding technique.

This package makes the qhull library available in R, in a similar manner as in Octave. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this. Currently the package only gives access to Delaunay triangulation and convex hull computation.

This package provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported.

The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>.

Computes the center of gravity (COG) of character-like binary images using three different methods. This package provides functions for estimating stroke-based, contour-based, and potential energy-based COG. It is useful for analyzing glyph structure in areas such as visual cognition research and font development. The contour-based method was originally proposed by Kotani et al. (2004) <https://ipsj.ixsq.nii.ac.jp/records/36793> and Kotani (2011) <https://shonan-it.repo.nii.ac.jp/records/2000243>, while the potential energy-based method was introduced by Kotani et al. (2006) <doi:10.11371/iieej.35.296>.

Generates simulated data representing the LOX drop testing process (also known as impact testing). A simulated process allows for accelerated study of test behavior. Functions are provided to simulate trials, test series, and groups of test series. Functions for creating plots specific to this process are also included. Test attributes and criteria can be set arbitrarily. This work is not endorsed by or affiliated with NASA. See "ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized Liquid and Gaseous Oxygen Environments" <doi:10.1520/G0086-17>.

This package provides a small subset of plots throughout the U.S. are sampled and assessed "on-the-ground" as forested or non-forested by the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program, but the FIA also has access to remotely sensed data for all land in the country. The forested package contains data frames intended for use in predictive modeling applications where the more easily-accessible remotely sensed data can be used to predict whether a plot is forested or non-forested. Currently, the package provides data for Washington and Georgia.

Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was developed as part of the author's publication in Physics in Medicine and Biology Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.

An implementation of SPRE (standardised predicted random-effects) statistics in R to explore heterogeneity in genetic association meta- analyses, as described by Magosi et al. (2019) <doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision weighted residuals that indicate the direction and extent with which individual study-effects in a meta-analysis deviate from the average genetic effect. Overly influential positive outliers have the potential to inflate average genetic effects in a meta-analysis whilst negative outliers might lower or change the direction of effect. See the getspres website for documentation and examples <https://magosil86.github.io/getspres/>.

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

The Cauchy distribution is a special case of the t distribution when the degrees of freedom are equal to 1. The functions are related to the multivariate Cauchy distribution and include simulation, computation of the density, maximum likelihood estimation, contour plot of the bivariate Cauchy distribution, and discriminant analysis. References include: Nadarajah S. and Kotz S. (2008). "Estimation methods for the multivariate t distribution". Acta Applicandae Mathematicae, 102(1): 99--118. <doi:10.1007/s10440-008-9212-8>, and Kanti V. Mardia, John T. Kent and John M. Bibby (1979). "Multivariate analysis", ISBN:978-0124712522. Academic Press, London.

This package provides functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits. A paper on this package was published in the Journal of Statistical Software <doi:10.18637/jss.v114.i10>.

This package provides tools for exploratory process data analysis. Process data refers to the data describing participants problem-solving processes in computer-based assessments. It is often recorded in computer log files. This package provides functions to read, process, and write process data. It also implements two feature extraction methods to compress the information stored in process data into standard numerical vectors. This package also provides recurrent neural network based models that relate response processes with other binary or scale variables of interest. The functions that involve training and evaluating neural networks are wrappers of functions in keras'.

This package provides a set of concise and efficient tools for statistical production. Can also be used for data management. In statistical production, you deal with complex data and need to control your process at each step of your work. Concise functions are very helpful, because you do not hesitate to use them. The following functions are included in the package. dup checks duplicates. miss checks missing values. tac computes contingency table of all columns. toc compares two tables, spotting significant deviations. chi2_find compares columns within a data.frame, spotting related categories of (a more complex function).

This takes spatial single-cell-type RNA-seq data (specifically designed for Slide-seq v2) that calls copy number alterations (CNAs) using pseudo-spatial binning, clusters cellular units (e.g. beads) based on CNA profile, and visualizes spatial CNA patterns. Documentation about SlideCNA is included in the the pre-print by Zhang et al. (2022, <doi:10.1101/2022.11.25.517982>). The package enrichR (>= 3.0), conditionally used to annotate SlideCNA-determined clusters with gene ontology terms, can be installed at <https://github.com/wjawaid/enrichR> or with install_github("wjawaid/enrichR").

MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology.

Estimation of functional spaces based on traits of organisms. The package includes functions to impute missing trait values (with or without considering phylogenetic information), and to create, represent and analyse two dimensional functional spaces based on principal components analysis, other ordination methods, or raw traits. It also allows for mapping a third variable onto the functional space. See Carmona et al. (2021) <doi:10.1038/s41586-021-03871-y>, Puglielli et al. (2021) <doi:10.1111/nph.16952>, Carmona et al. (2021) <doi:10.1126/sciadv.abf2675>, Carmona et al. (2019) <doi:10.1002/ecy.2876> for more information.