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>.

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.

This package AMARETTO represents an algorithm that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. AMARETTO can be applied in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.

This package provides a lightweight unit testing framework. Main features:

1. install tests with the package;

2. test results are treated as data that can be stored and manipulated;

3. test files are R scripts interspersed with test commands, that can be programmed over;

4. fully automated build-install-test sequence for packages;

5. skip tests when not run locally (e.g. on CRAN);

6. flexible and configurable output printing;

7. compare computed output with output stored with the package;

8. run tests in parallel;

9. extensible by other packages;

10. report side effects.

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>.

This package provides a user-friendly workflow for simulating circadian clock gene networks. Despite decades of advances in modeling circadian clock dynamics, the lack of accessible tools for reproducible simulation workflows hinders the integration of computational modeling with experimental studies. clockSim addresses this gap by providing models and helper functions with step-by-step vignettes. This package opens up system-level exploration of the circadian clock to wet-lab experimentalists, and future development will include additional clock architectures and other gene circuit models. Currently implemented models are based on Leloup and Goldbeter (1998) <doi:10.1177/074873098128999934>.

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>.

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/>.

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>.

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.

Wrapper for Minimap2'. Minimap2 is a very valuable long read aligner for the Pacbio and Oxford Nanopore Technologies sequencing platforms. minimapR is an R wrapper for minimap2 which was developed by Heng Li <me@liheng.org>. *SPECIAL NOTES 1. Examples can only be run from GitHub installation. 2. conda or mamba must be used to install minimapR on your system. 3. For Windows users, minimap2 and samtools can be installed via MSYS2, instructions are provided when minimap2_installation() is run. Li, Heng (2018) <doi:10.1093/bioinformatics/bty191> "Minimap2: pairwise alignment for nucleotide sequences".

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 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").

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.

This package implements time series clustering along with optimized techniques related to the dynamic time warping distance and its corresponding lower bounds. The implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.

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.

Analysis of annual average ocean water level time series from long (minimum length 80 years) individual records, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. Improved trend estimates are based on Singular Spectrum Analysis methods. Various gap-filling options are included to accommodate incomplete time series records. The package also contains a forecasting module to consider the implication of user defined quantum of sea level rise between the end of the available historical record and the year 2100. A wide range of screen and pdf plotting options are available in the package.

This package provides a series of numerical methods for extracting parameters of distributions for risks based on knowing the expected value and c-statistics (e.g., from a published report on the performance of a risk prediction model). This package implements the methodology described in Sadatsafavi et al (2024) <doi:10.48550/arXiv.2409.09178>. The core of the package is mcmap(), which takes a pair of (mean, c-statistic) and the distribution type requested. This function provides a generic interface to more customized functions (mcmap_beta(), mcmap_logitnorm(), mcmap_probitnorm()) for specific distributions.

This package provides a minimalist implementation of model stacking by Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness.

Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2), Sikorska et al., (2013) <doi:10.1186/1471-2105-14-166>, or half-sib/full-sib SNP matrix from real or simulated parents SNP data, assuming mendelian segregation. Simulate phenotypic traits for real or simulated SNP data, controlled by a specific number of quantitative trait loci and their effects, sampled from a Normal or an Uniform distributions, assuming a pure additive model. This is useful for testing association and genomic prediction models or for educational purposes.

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 functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al., 2007 <doi: 10.1093/bioinformatics/btm025>.