Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c).

This package provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

This package provides functions for estimation and data generation for several piecewise lifetime distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) <doi:10.2307/2528247> for methodological details.

Parallel Constraint Satisfaction (PCS) models are an increasingly common class of models in Psychology, with applications to reading and word recognition (McClelland & Rumelhart, 1981), judgment and decision making (Glöckner & Betsch, 2008; Glöckner, Hilbig, & Jekel, 2014), and several other fields (e.g. Read, Vanman, & Miller, 1997). In each of these fields, they provide a quantitative model of psychological phenomena, with precise predictions regarding choice probabilities, decision times, and often the degree of confidence. This package provides the necessary functions to create and simulate basic Parallel Constraint Satisfaction networks within R.

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.

Loads and processes huge text corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>). sally acts as a very fast preprocessor which splits the text files into tokens or n-grams. These output files can then be read with the PRISMA package which applies testing-based token selection and has some replicate-aware, highly tuned non-negative matrix factorization and principal component analysis implementation which allows the processing of very big data sets even on desktop machines.

Validation of risk predictions obtained from survival models and competing risk models based on censored data using inverse weighting and cross-validation. Most of the pec functionality has been moved to riskRegression'.

The goal of pak is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. pak has a dependency solver, so it finds version conflicts before performing the installation. This version of pak supports CRAN, Bioconductor and GitHub packages as well.

This package provides a high performance package implementing random effects and/or sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) <doi:10.2139/ssrn.2702053>.

Portable /proc/self/maps as a data frame. Determine which library or other region is mapped to a specific address of a process. -- R packages can contain native code, compiled to shared libraries at build or installation time. When loaded, each shared library occupies a portion of the address space of the main process. When only a machine instruction pointer is available (e.g. from a backtrace during error inspection or profiling), the address space map determines which library this instruction pointer corresponds to.

We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).

The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.

Can be used to carry out permutation based gene expression pathway analysis. This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C).

This package provides functions to compute the potential model as defined by Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available to customize the model, such as the possibility to fine-tune the distance friction functions or to use custom distance matrices. Some computations are parallelized to improve their efficiency.

An innovative tool-set that incorporates graph community detection methods into systematic conservation planning. It is designed to enhance spatial prioritization by focusing on the protection of areas with high ecological connectivity. Unlike traditional approaches that prioritize individual planning units, priorCON focuses on clusters of features that exhibit strong ecological linkages. The priorCON package is built upon the prioritizr package <doi:10.32614/CRAN.package.prioritizr>, using commercial and open-source exact algorithm solvers that ensure optimal solutions to prioritization problems.

Calculate sample size or power for hierarchical endpoints. The package can handle any type of outcomes (binary, continuous, count, ordinal, time-to-event) and any number of such endpoints. It allows users to calculate sample size with a given power or to calculate power with a given sample size for hypothesis testing based on win ratios, win odds, net benefit, or DOOR (desirability of outcome ranking) as treatment effect between two groups for hierarchical endpoints. The methods of this package are described further in the paper by Barnhart, H. X. et al. (2024, <doi:10.1080/19466315.2024.2365629>).

Carrying out inferences about any linear combination of proportions and the ratio of two proportions.

This package provides functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as vcf files or rsync files generated by the the PoPoolation software) and perform conversion to alternative format (as used in the BayPass and SelEstim software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in TreeMix, BayPass and SelEstim format).

Homogeneity tests of the coefficients in panel data. Currently, only the Hsiao test for determining coefficient homogeneity between the panel data individuals is implemented, as described in Hsiao (2022), "Analysis of Panel Data" (<doi:10.1017/9781009057745>).

Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis, <https://our.oakland.edu/items/c8942577-2562-4d2f-8677-cb8ec0bf6234>.

In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.

This package provides permutation methods for testing in high-dimensional linear models. The tests are often robust against heteroscedasticity and non-normality and usually perform well under anti-sparsity. See Hemerik, Thoresen and Finos (2021) <doi:10.1080/00949655.2020.1836183>.

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>.

This package provides tools for downloading, reading and analyzing the National Survey of Demographic and Health - PNDS, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.