All PubChem compounds are downloaded to a local computer, but for each compound, only partial records are used. The data are organized into small files referenced by PubChem CID. This package also contains functions to parse the biologically relevant compounds from all PubChem compounds, using biological database sources, pathway presence, and taxonomic relationships. Taxonomy is used to generate a lowest common ancestor taxonomy ID (NCBI) for each biological metabolite, which then enables creation of taxonomically specific metabolome databases for any taxon.

Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.

Distributes data from the Polarization in Comparative Attitudes Project. Helper functions enable data retrieval in wide and tidy formats for user-defined countries and years. Provides support for case-insensitive country names in many languages. Mehlhaff (2022) <https://imehlhaff.net/files/Polarization%20and%20Democracy.pdf>.

Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - PvSTATEM', which is an international project aiming for malaria elimination.

Data about Disney Pixar films provided by Wikipedia. This package contains data about the films, the people involved, and their awards.

Determine the chlorophyll a (Chl a) concentrations of different phytoplankton groups based on their pigment biomarkers. The method uses non-negative matrix factorisation and simulated annealing to minimise error between the observed and estimated values of pigment concentrations (Hayward et al. (2023) <doi:10.1002/lom3.10541>). The approach is similar to the widely used CHEMTAX program (Mackey et al. 1996) <doi:10.3354/meps144265>, but is more straightforward, accurate, and not reliant on initial guesses for the pigment to Chl a ratios for phytoplankton groups.

An interface to simplify organizing parameters used in a package, using external configuration files. This attempts to provide a cleaner alternative to options().

This package provides functions for quantifying visible (VIS) and ultraviolet (UV) radiation in relation to the photoreceptors Phytochromes, Cryptochromes, and UVR8 which are present in plants. It also includes data sets on the optical properties of plants. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Estimate penalized synthetic control models and perform hold-out validation to determine their penalty parameter. This method is based on the work by Abadie & L'Hour (2021) <doi:10.1080/01621459.2021.1971535>. Penalized synthetic controls smoothly interpolate between one-to-one matching and the synthetic control method.

This package implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>; minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

This package implements conjugate power priors for efficient Bayesian analysis of normal data. Power priors allow principled incorporation of historical information while controlling the degree of borrowing through a discounting parameter (Ibrahim and Chen (2000) <doi:10.1214/ss/1009212519>). This package provides closed-form conjugate representations for both univariate and multivariate normal data using Normal-Inverse-Chi-squared and Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling. The conjugate framework builds upon standard Bayesian methods described in Gelman et al. (2013, ISBN:978-1439840955).

Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) <doi:10.48550/arXiv.0909.5262>. The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos.

Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).

Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.

An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. Lutkepohl, H. (2007, ISBN:3540262393).

Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation. Calculations of propagated uncertainties are based on matrix calculus including covariance structure according to Arras 1998 <doi:10.3929/ethz-a-010113668> (first order), Wang & Iyer 2005 <doi:10.1088/0026-1394/42/5/011> (second order) and BIPM Supplement 1 (Monte Carlo) <doi:10.59161/JCGM101-2008>.

This package implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), <doi:10.1214/13-EJS810> and Lee, E-K. (2018) <doi:10.18637/jss.v083.i08>. The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group.

This package provides functions that allow you to generate and compare power spectral density (PSD) plots given time series data. Fast Fourier Transform (FFT) is used to take a time series data, analyze the oscillations, and then output the frequencies of these oscillations in the time series in the form of a PSD plot.Thus given a time series, the dominant frequencies in the time series can be identified. Additional functions in this package allow the dominant frequencies of multiple groups of time series to be compared with each other. To see example usage with the main functions of this package, please visit this site: <https://yhhc2.github.io/psdr/articles/Introduction.html>. The mathematical operations used to generate the PSDs are described in these sites: <https://www.mathworks.com/help/matlab/ref/fft.html>. <https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.

Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.

This package provides tools for calculating statistical power for experiments analyzed using linear mixed models. It supports standard designs, including randomized block, split-plot, and Latin Square designs, while offering flexibility to accommodate a variety of other complex study designs.

Includes functions implementing the conditionally optimal matching algorithm, which can be used to generate matched samples in designs with multiple groups. The algorithm is described in Nattino, Song and Lu (2022) <doi:10.1016/j.csda.2021.107364>.

An open-access tool/framework to download, validate, visualize, and analyze multi-source precipitation data. More information and an example of implementation can be found in Vargas Godoy and Markonis (2023, <doi:10.1016/j.envsoft.2023.105711>).

Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice.

This is a data-only package, containing data needed to run the CRAN package pathfindR', a package for enrichment analysis utilizing active subnetworks. This package contains protein-protein interaction network data, data related to gene sets and example input/output data.