R companion to Tsay (2005) Analysis of Financial Time Series, second edition (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11.

Collection of tools that facilitates data access and workflow for spatial analysis of Argentina. Includes historical information from censuses, administrative limits at different levels of aggregation, location of human settlements, among others. Since it is expected that the majority of users will be Spanish-speaking, the documentation of the package prioritizes this language, although an effort is made to also offer annotations in English.

This package provides two new layer types for displaying image data as layers within the Grammar of Graphics framework. Displays images using either a rectangle interface, with a fixed bounding box, or a point interface using a central point and general size parameter. Images can be given as local JPEG or PNG files, external resources, or as a list column containing raster image data.

Analysis of plant pathogen pathotype survey data. Functions provided calculate distribution of susceptibilities, distribution of complexities with statistics, pathotype frequency distribution, as well as diversity indices for pathotypes. This package is meant to be a direct replacement for Herrmann, Löwer and Schachtel's (1999) <doi:10.1046/j.1365-3059.1999.00325.x> Habgood-Gilmour Spreadsheet, HaGiS', previously used for pathotype analysis.

Implementation of icosahedral grids in three dimensions. The spherical-triangular tessellation can be set to create grids with custom resolutions. Both the primary triangular and their inverted penta-hexagonal grids can be calculated. Additional functions are provided that allow plotting of the grids and associated data, the interaction of the grids with other raster and vector objects, and treating the grids as a graphs.

These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area in a conservation analysis. The algorithm is described in: Miranda-Esquivel, D (2016) <DOI:10.1007/978-3-319-22461-9_11>.

The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) <https://proceedings.mlr.press/v48/niu16.html> and the warping algorithm proposed in Niu et al. (2017) <DOI:10.1007/s00180-017-0753-z> are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping.

Convert latex math expressions to HTML and MathML for use in markdown documents or package manual pages. The rendering is done in R using the V8 engine (i.e. server-side), which eliminates the need for embedding the MathJax library into your web pages. In addition a math-to-rd wrapper is provided to automatically render beautiful math in R documentation files.

This package provides a comprehensive graphical user interface for analysis of Affymetrix, Agilent, Illumina, Nimblegen and other microarray data. It can perform miscellaneous tasks such as gene set enrichment and test analyses, identifying gene symbols and building co-expression network. It can also estimate sample size for atleast two-fold expression change. The current version is its slenderized form for compatable and flexible implementation.

Allows the user to create graphs with multiple layers. The user can also modify the layers, the nodes, and the edges. The graph can also be visualized. Zaynab Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>. More about multilayered graphs and their usage can be found in our review paper: Zaynab Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.

This package provides methods for fitting point processes with parameters of generalised additive model (GAM) form are provided. For an introduction to point processes see Cox, D.R & Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.

Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.

Data simulator including genotype, phenotype, pedigree, selection and reproduction in R. It simulates most of reproduction process of animals or plants and provides data for GS (Genomic Selection), GWAS (Genome-Wide Association Study), and Breeding. For ADI model, please see Kao C and Zeng Z (2002) <doi:10.1093/genetics/160.3.1243>. For build.cov, please see B. D. Ripley (1987) <ISBN:9780470009604>.

In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.

UNIfied Cross-Omics deconvolution (Unico) deconvolves standard 2-dimensional bulk matrices of samples by features into a 3-dimensional tensors representing samples by features by cell types. Unico stands out as the first principled model-based deconvolution method that is theoretically justified for any heterogeneous genomic data. For more details see Chen and Rahmani et al. (2024) <doi:10.1101/2024.01.27.577588>.

This package provides helper functions to perform Bayesian model averaging using Markov chain Monte Carlo samples from separate models. Calculates weights and obtains draws from the model-averaged posterior for quantities of interest specified by the user. Weight calculations can be done using marginal likelihoods or log-predictive likelihoods as in Ando, T., & Tsay, R. (2010) <doi:10.1016/j.ijforecast.2009.08.001>.

This package provides a visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using ggplot2'. Multiple sequence alignment can easily be combined with other ggplot2 plots, such as phylogenetic tree Visualized by ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations.

All alleles from the IPD IMGT/HLA <https://www.ebi.ac.uk/ipd/imgt/hla/> and IPD KIR <https://www.ebi.ac.uk/ipd/kir/> database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation.

This package provides functions to allow you to easily pass command-line arguments into R, and functions to aid in submitting your R code in parallel on a cluster and joining the results afterward (e.g. multiple parameter values for simulations running in parallel, splitting up a permutation test in parallel, etc.). See `parseCommandArgs(...) for the main example of how to use this package.

This package provides a dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. This is not an outlier detection method; rather it is a pre-processing method for outlier detection. It brings outliers to the fore-front using fewer basis vectors (Kandanaarachchi, Hyndman 2020) <doi:10.1080/10618600.2020.1807353>.

Calculates the desparsified lasso as originally introduced in van de Geer et al. (2014) <doi:10.1214/14-AOS1221>, and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) <arXiv:2007.10952>. Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) <arXiv:2209.03218>.

Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at <doi:10.48550/arXiv.2409.18227>.

Extract features from tabular data in a declarative fashion, with a focus on processing medical records. Features are specified as JSON and are independently processed before being joined. Input data can be provided as CSV files or as data frames. This setup ensures that data is transformed in a modular and reproducible manner, and allows the same pipeline to be easily applied to new data.

The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules as described by Pattabiraman N, Ward KB, Fleming PJ (1995) <doi:10.1002/jmr.300080603>. Here, we introduce fibos', an R and Python package that extends the OS methodology, as presented in Soares HHM, Romanelli JPR, Fleming PJ, da Silveira CH (2024) <doi:10.1101/2024.11.01.621530>.