Density, distribution function, quantile function and random generation for the Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.

Draws gene or genome maps and comparisons between these, in a publication-grade manner. Starting from simple, common files, it will draw postscript or PDF files that can be sent as such to journals.

This package provides a zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.

This package provides a function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>. More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, <doi:10.1007/978-1-0716-1839-4_12>.

This package provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.

Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables. Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model, respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. This model can be employed in social and behavioral sciences, economy and finance, and genomics, to extract interpretable nonlinear factors. More details can be referred to Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2023) <doi:10.1080/01621459.2021.1999818>.

This package provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).

Derivative Free Gradient Projection Algorithms for Factor Rotation. For more details see ?GPArotateDF. Theory for these functions can be found in the following publications: Jennrich (2004) <doi:10.1007/BF02295647>. Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.

This package provides residual global fit indices for generalized latent variable models.

When evaluating the results of a genome-wide association study (GWAS), it is important to perform a quality control to ensure that the results are valid, complete, correctly formatted, and, in case of meta-analysis, consistent with other studies that have applied the same analysis. This package was developed to facilitate and streamline this process and provide the user with a comprehensive report.

River hydrograph separation and daily runoff time series analysis. Provides various filters to separate baseflow and quickflow. Implements advanced separation technique by Rets et al. (2022) <doi:10.1134/S0097807822010146> which involves meteorological data to reveal genetic components of the runoff: ground, rain, thaw and spring (seasonal thaw). High-performance C++17 computation, annually aggregated variables, statistical testing and numerous plotting functions for high-quality visualization.

Estimation of the generalized beta distribution of the second kind (GB2) and related models using grouped data in form of income shares. The GB2 family is a general class of distributions that provides an accurate fit to income data. GB2group includes functions to estimate the GB2, the Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. GB2group deploys two different econometric strategies to estimate these parametric distributions, the equally weighted minimum distance (EWMD) estimator and the optimally weighted minimum distance (OMD) estimator. Asymptotic standard errors are reported for the OMD estimates. Standard errors of the EWMD estimates are obtained by Monte Carlo simulation. See Jorda et al. (2018) <arXiv:1808.09831> for a detailed description of the estimation procedure.

Interacts with the Glassdoor API <https://www.glassdoor.com/developer/index.htm>. Allows the user to search job statistics, employer statistics, and job progression, where Glassdoor provides a breakdown of other jobs a person did after their current one.

This package provides a plain Rcpp wrapper for MeCab that can segment Chinese, Japanese, and Korean text into tokens. The main goal of this package is to provide an alternative to tidytext using morphological analysis.

Model and estimate the model parameters for the spatial model of individual-level infectious disease transmission in Susceptible-Infected-Recovered (SIR) framework.

Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.

Multidimensional systems allow complex queries to be carried out in an easy way. The geographical dimension, together with the temporal dimension, plays a fundamental role in multidimensional systems. Through this package, vector geographic data layers can be associated to the attributes of geographic dimensions, so that the results of multidimensional queries can be obtained directly as vector layers. The multidimensional structures on which we can define the queries can be created from a flat table or imported directly using functions from this package.

Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the graphicalVAR approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

This package performs geographically weighted Lasso regressions. Find optimal bandwidth, fit a geographically weighted lasso or ridge regression, and make predictions. These methods are specially well suited for ecological inferences. Bandwidth selection algorithm is from A. Comber and P. Harris (2018) <doi:10.1007/s10109-018-0280-7>.

Access Google Cloud machine learning APIs for text and speech tasks. Use the Cloud Translation API for text detection and translation, the Natural Language API to analyze sentiment, entities, and syntax, the Cloud Speech API to transcribe audio to text, and the Cloud Text-to-Speech API to synthesize text into audio files.

Plot glycans following the Symbol Nomenclature for Glycans (SNFG) using ggplot2'. SNFG provides a standardized visual representation of glycan structures.

Unsupervised Clustering and Meta-analysis using Gaussian Mixture Copula Models.

This package provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models. Documentation about gRim is provided by vignettes included in this package and the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>); see citation("gRim") for details.

The GeneCycle package implements the approaches of Wichert et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al. (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically expressed genes from gene expression time series data.