Tests for a comparison of two partially overlapping samples. A comparison of means using the partially overlapping samples t-test: See Derrick, Russ, Toher and White (2017), Test statistics for the comparison of means for two samples which include both paired observations and independent observations, Journal of Modern Applied Statistical Methods, 16(1). A comparison of proportions using the partially overlapping samples z-test: See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics for comparing two proportions with partially overlapping samples. Journal of Applied Quantitative Methods, 10(3).

Price volatility refers to the degree of variation in series over a certain period of time. This volatility is especially noticeable in agricultural commodities, adding uncertainty for farmers, traders, and others in the agricultural supply chain. Commonly and popularly used four volatility models viz, GARCH, Glosten Jagannatan Runkle-GARCH (GJR-GARCH) model, exponentially weighted moving average (EWMA) model and Multiplicative Error Model (MEM) are selected and implemented. PWAVE, weighted ensemble model based on particle swarm optimization (PSO) is proposed to combine the forecast obtained from all the candidate models. This package has been developed using algorithm of Paul et al. <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.

This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package Peptides by Osorio et al. (2015) <doi:10.32614/RJ-2015-001>). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning.

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.

We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?

Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) <doi:10.1177/1532673X18759644>.

Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x> extended to time-varying covariates. Estimation and variable selection are based on the methodology described in Beretta and Heuchenne (2019) <doi:10.1080/02664763.2018.1554627>; confidence intervals of the parameter estimates may be computed using a bootstrap approach. Moreover, data following the PH cure model may be simulated using a method similar to Hendry (2014) <doi:10.1002/sim.5945>, where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates.

This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in Nanaimo', British Columbia, Canada. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include C++ code developed by Angus Johnson for the Clipper library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory .libPaths()', specifically in ./PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively.

Read Protein Data Bank (PDB) files, performs its analysis, and presents the result using different visualization types including 3D. The package also has additional capability for handling Virus Report data from the National Center for Biotechnology Information (NCBI) database. Nature Structural Biology 10, 980 (2003) <doi:10.1038/nsb1203-980>. US National Library of Medicine (2021) <https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.

This package provides a dataset of Pokemon information in both English and Brazilian Portuguese. The dataset contains 949 rows and 22 columns, including information such as the Pokemon's name, ID, height, weight, stats, type, and more.

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>.

This package provides a native R client library for querying the Prometheus time-series database, using the PromQL query language.

Converts TXT and XML data curated by the United States Patent and Trademark Office (USPTO). Allows conversion of bulk data after downloading directly from the USPTO bulk data website, eliminating need for users to wrangle multiple data formats to get large patent databases in tidy, rectangular format. Data details can be found on the USPTO website <https://bulkdata.uspto.gov/>. Currently, all 3 formats: 1. TXT data (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data (2005-current) can be converted to rectangular, CSV format. Relevant literature that uses data from USPTO includes Wada (2020) <doi:10.1007/s11192-020-03674-4> and Plaza & Albert (2008) <doi:10.1007/s11192-007-1763-3>.

Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.

Given a data matrix with rows representing data vectors and columns representing variables, produces a directed polytree for the underlying causal structure. Based on the algorithm developed in Chatterjee and Vidyasagar (2022) <arxiv:2209.07028>. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large.

This package contains utilities for the analysis of post-translational modifications (PTMs) in proteins, with particular emphasis on the sulfoxidation of methionine residues. Features include the ability to download, filter and analyze data from the sulfoxidation database MetOSite'. Utilities to search and characterize S-aromatic motifs in proteins are also provided. In addition, functions to analyze sequence environments around modifiable residues in proteins can be found. For instance, ptm allows to search for amino acids either overrepresented or avoided around the modifiable residues from the proteins of interest. Functions tailored to test statistical hypothesis related to these differential sequence environments are also implemented. Further and detailed information regarding the methods in this package can be found in (Aledo (2020) <https://metositeptm.com>).

Easy and efficient access to the API provided by Prevedere', an industry insights and predictive analytics company. Query and download indicators, models and workbenches built with Prevedere for further analysis and reporting <https://www.prevedere.com/>.

Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.

This package provides functions for generating progressively Type-II censored data in a mixture structure and fitting models using a constrained EM algorithm. It can also create a progressive Type-II censored version of a given real dataset to be considered for model fitting.

This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.

Convenient structures for creating, sourcing, reading, writing and manipulating ordinal preference data. Methods for writing to/from PrefLib formats. See Nicholas Mattei and Toby Walsh "PrefLib: A Library of Preference Data" (2013) <doi:10.1007/978-3-642-41575-3_20>.

An interactive document for preprocessing the dataset using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/PREPShiny/>.

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.

Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.