Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot XVG output files from the GROMACS molecular dynamics package.

This package provides a project infrastructure with a focus on manuscript creation. Creates a project folder with a single command, containing subdirectories for specific components, templates for manuscripts, and so on.

The goal of planets is to provide of very simple and accessible data containing basic information from all known planets.

This package provides functions for creating color palettes, visualizing palettes, modifying colors, and assigning colors for plotting.

Consider a linear predictive regression setting with a potentially large set of candidate predictors. This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.

This package implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions <doi:10.48550/arXiv.1902.00101>. Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values <doi:10.1080/03610918.2023.2234683>.

Implementation of the Pearson distribution system, including full support for the (d,p,q,r)-family of functions for probability distributions and fitting via method of moments and maximum likelihood method.

Google Trends provides cross-sectional and time-series data on searches, but lacks readily available longitudinal data. Researchers, who want to create longitudinal Google Trends on their own, face practical challenges, such as normalized counts that make it difficult to combine cross-sectional and time-series data and limitations in data formats and timelines that limit data granularity over extended time periods. This package addresses these issues and enables researchers to generate longitudinal Google Trends data. This package is built on pytrends', a Python library that acts as the unofficial Google Trends API to collect Google Trends data. As long as the Google Trends API', pytrends and all their dependencies are working, this package will work. During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of Google Trends data, once the data is created, this package coverts it to longitudinal data. In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect Google Trends data. This error indicates that the user has exceeded the rate limits set by the Google Trends API'. For more information about the Google Trends API - pytrends', visit <https://pypi.org/project/pytrends/>.

Generates simple and beautiful one-page HTML reference manuals with package documentation. Math rendering and syntax highlighting are done server-side in R such that no JavaScript libraries are needed in the browser, which makes the documentation portable and fast to load.

This package contains functions developed to combine the results of querying a plasmid database using short-read sequence typing with the results of a blast analysis against the query results.

This package provides a tidyverse'-style interface to the Brazilian Central Bank (<https://www.bcb.gov.br>) PIX Open Data API <https://olinda.bcb.gov.br/olinda/servico/Pix_DadosAbertos/versao/v1/aplicacao#!/recursos>. Retrieve statistics on PIX keys, transactions by municipality, and monthly transaction summaries. All functions return tibbles and support OData query parameters for filtering, selecting, and ordering data.

This package provides tools for modelling populations and demography using matrix projection models, with deterministic and stochastic model implementations. Includes population projection, indices of short- and long-term population size and growth, perturbation analysis, convergence to stability or stationarity, and diagnostic and manipulation tools.

This package provides a standardized framework to support the selection and evaluation of parametric survival models for time-to-event data. Includes tools for visualizing survival data, checking proportional hazards assumptions (Grambsch and Therneau, 1994, <doi:10.1093/biomet/81.3.515>), comparing parametric (Ishak and colleagues, 2013, <doi:10.1007/s40273-013-0064-3>), spline (Royston and Parmar, 2002, <doi:10.1002/sim.1203>) and cure models, examining hazard functions, and evaluating model extrapolation. Methods are consistent with recommendations in the NICE Decision Support Unit Technical Support Documents (14 and 21 <https://sheffield.ac.uk/nice-dsu/tsds/survival-analysis>). Results are structured to facilitate integration into decision-analytic models, and reports can be generated with rmarkdown'. The package builds on existing tools including flexsurv (Jackson, 2016, <doi:10.18637/jss.v070.i08>)) and flexsurvcure for estimating cure models.

Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.

Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) <doi:10.1016/j.aca.2018.10.062> and PMD based reactomics analysis proposed in Yu and Petrick (2020) <doi:10.1038/s42004-020-00403-z> for gas/liquid chromatographyâ mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. Reactomics analysis could also be performed to build PMD network, assign sources and make biomarker reaction discovery. GUIs for PMD analysis is also included as shiny applications.

Estimate commonly used population genomic statistics and generate publication quality figures. PopGenHelpR uses vcf, geno (012), and csv files to generate output.

Send push notifications to mobile devices or the desktop using Pushover <https://pushover.net>. These notifications can display things such as results, job status, plots, or any other text or numeric data.

Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.

This package provides a low-level package for hosting persistence data. It is part of the TDAverse suite of packages, which is designed to provide a collection of packages for enabling machine learning and data science tasks using persistent homology. Implements a class for hosting persistence data, a number of coercers from and to already existing and used data structures from other packages and functions to compute distances between persistence diagrams. A formal definition and study of bottleneck and Wasserstein distances can be found in Bubenik, Scott and Stanley (2023) <doi:10.1007/s41468-022-00103-8>. Their implementation in phutil relies on the C++ Hera library developed by Kerber, Morozov and Nigmetov (2017) <doi:10.1145/3064175>.

Simulation of models Poisson-Tweedie.

This package provides a network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific abnormal subpathways in the cancer dataset with multi-phenotype samples.

This package provides a collection of R functions that are widely used by the Petersen Lab. Included are functions for various purposes, including evaluating the accuracy of judgments and predictions, performing scoring of assessments, generating correlation matrices, conversion of data between various types, data management, psychometric evaluation, extensions related to latent variable modeling, various plotting capabilities, and other miscellaneous useful functions. By making the package available, we hope to make our methods reproducible and replicable by others and to help others perform their data processing and analysis methods more easily and efficiently. The codebase is provided in Petersen (2025) <doi:10.5281/zenodo.7602890> and on CRAN': <doi: 10.32614/CRAN.package.petersenlab>. The package is described in "Principles of Psychological Assessment: With Applied Examples in R" (Petersen, 2024, 2025a) <doi:10.1201/9781003357421>, <doi:10.25820/work.007199>, <doi:10.5281/zenodo.6466589> and in "Fantasy Football Analytics: Statistics, Prediction, and Empiricism Using R" (Petersen, 2025b).

This package provides a set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) datasets inside the pharmaverse family of packages. SDTM dataset specifications are described in the CDISC SDTM implementation guide, accessible by creating a free account on <https://www.cdisc.org/>.

Interface to Phylocom (<https://phylodiversity.net/phylocom/>), a library for analysis of phylogenetic community structure and character evolution. Includes low level methods for interacting with the three executables, as well as higher level interfaces for methods like aot', ecovolve', bladj', phylomatic', and more.