This package provides tools for AI-assisted qualitative data coding using large language models ('LLMs') via the ellmer package, supporting providers including OpenAI', Anthropic', Google', Azure', and local models via Ollama'. Provides a codebook'-based workflow for defining coding instructions and applying them to texts, images, and other data. Includes built-in codebooks for common applications such as sentiment analysis and policy coding, and functions for creating custom codebooks for specific research questions. Supports systematic replication across models and settings, computing inter-coder reliability statistics including Krippendorff's alpha (Krippendorff 2019, <doi:10.4135/9781071878781>) and Fleiss kappa (Fleiss 1971, <doi:10.1037/h0031619>), as well as gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009, <doi:10.1016/j.ipm.2009.03.002>). Provides audit trail functionality for documenting coding workflows following Lincoln and Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness in qualitative research.

Reads the versioned exchange files written by the Zotero plugins zotQDA and qdaZ -- coded fragments, code systems, coding histories and team-consensus results -- validates them against the shipped contract, and reproduces the plugin's graphics with ggplot2'. Adds what those plugins deliberately leave out: six agreement coefficients with bootstrap confidence intervals, the reliability of the segmentation itself, chi-squared tests of code by group tables with effect sizes, correspondence analysis, multidimensional scaling and hierarchical clustering of codes. Projects from other programs can be read through the REFI-QDA interchange standard <https://www.qdasoftware.org/>, which makes those analyses available to users of established software that does not offer them; the subset a .qdpx supports is reported on import. Reference files are included, so every function can be tried without a Zotero installation.

This package provides a collection of routines for finding reference limits using, where appropriate, QQ methodology. All use a data vector X of cases from the reference population. The default is to get the central 95% reference range of the population, namely the 2.5 and 97.5 percentile, with optional adjustment of the range. Along with the reference limits, we want confidence intervals which, for historical reasons, are typically at 90% confidence. A full analysis provides six numbers: â the upper and the lower reference limits, and - each of their confidence intervals. For application details, see Hawkins and Esquivel (2024) <doi:10.1093/jalm/jfad109>.

Option pricing (financial derivatives) techniques mainly following textbook Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.

Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.

This package provides statistical components, tables, and graphs that are useful in Quarto and RMarkdown reports and that produce Quarto elements for special formatting such as tabs and marginal notes and graphs. Some of the functions produce entire report sections with tabs, e.g., the missing data report created by missChk(). Functions for inserting variables and tables inside graphviz and mermaid diagrams are included, and so are special clinical trial graphics for adverse event reporting.

This package provides functions to plot QTL (quantitative trait loci) analysis results and related diagnostics. Part of qtl2', an upgrade of the qtl package to better handle high-dimensional data and complex cross designs.

Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Sottile G, Frumento P, Chiodi M and Bottai M (2020) <doi:10.1177/1471082X19825523>.

This package provides routines to create some quaternions splines: Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels algorithm. The implementations are based on the Python library splines'. Quaternions splines allow to construct spherical curves. References: Barry and Goldman <doi:10.1145/54852.378511>, Kochanek and Bartels <doi:10.1145/800031.808575>.

Create quantile binned and conditional plots for Exploratory Data Analysis. The package provides several plotting functions that are all based on quantile binning. The plots are created with ggplot2 and patchwork and can be further adjusted.

This package provides functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, directional quantile classification, and quantile ratio regression. A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.

Toolbox with shiny applications for widely used psychometric methods. Those methods include following analysis: Item analysis, item response theory calibration, principal component analysis, confirmatory factor analysis - structural equation modeling, generating simulated data. References: Chalmers (2012, <doi:10.18637/jss.v048.i06>); Revelle (2022, <https://CRAN.R-project.org/package=psych Version = 2.2.9.>); Rosseel (2012, <doi:10.18637/jss.v048.i02>); Magis & Raiche (2012, <doi:10.18637/jss.v048.i08>); Magis & Barrada (2017, <doi:10.18637/jss.v076.c01>).

Provide a simple interface to Bloomberg's OpenFIGI API. Please see <https://openfigi.com> for API details and registration. You may be eligible to have an API key to accelerate your loading process.

This package provides functions to have nice rmarkdown outputs of the seasonal and trading day adjustment models made with RJDemetra'.

Rcmdr interface to the sos package. The plug-in renders the sos searching functionality easily accessible via the Rcmdr menus. It also simplifies the task of performing multiple searches and subsequently obtaining the union or the intersection of the results.

This package provides a simple user-friendly library based on the python module reservoirpy'. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters. Finally, it includes several tutorials exploring time series forecasting, classification and hyperparameter tuning. For more information about reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>. This package was developed in the framework of the University of Bordeauxâ s IdEx "Investments for the Future" program / RRI PHDS.

The traditional linear regression trend, Modified Mann-Kendall (MK) non-parameter trend and bootstrap trend are included in this package. Linear regression trend is rewritten by .lm.fit'. MK trend is rewritten by Rcpp'. Finally, those functions are about 10 times faster than previous version in R. Reference: Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. <doi:10.1016/S0022-1694(97)00125-X>.

Analyzing the performance of artificial intelligence (AI) systems/algorithms characterized by a search-and-report strategy. Historically observer performance has dealt with measuring radiologists performances in search tasks, e.g., searching for lesions in medical images and reporting them, but the implicit location information has been ignored. The implemented methods apply to analyzing the absolute and relative performances of AI systems, comparing AI performance to a group of human readers or optimizing the reporting threshold of an AI system. In addition to performing historical receiver operating receiver operating characteristic (ROC) analysis (localization information ignored), the software also performs free-response receiver operating characteristic (FROC) analysis, where lesion localization information is used. A book using the software has been published: Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, Taylor-Francis LLC; 2017: <https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840>. Online updates to this book, which use the software, are at <https://dpc10ster.github.io/RJafrocQuickStart/>, <https://dpc10ster.github.io/RJafrocRocBook/> and at <https://dpc10ster.github.io/RJafrocFrocBook/>. Supported data collection paradigms are the ROC, FROC and the location ROC (LROC). ROC data consists of single ratings per images, where a rating is the perceived confidence level that the image is that of a diseased patient. An ROC curve is a plot of true positive fraction vs. false positive fraction. FROC data consists of a variable number (zero or more) of mark-rating pairs per image, where a mark is the location of a reported suspicious region and the rating is the confidence level that it is a real lesion. LROC data consists of a rating and a location of the most suspicious region, for every image. Four models of observer performance, and curve-fitting software, are implemented: the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM predict proper ROC curves that do not inappropriately cross the chance diagonal. Additionally, RSM parameters are related to search performance (not measured in conventional ROC analysis) and classification performance. Search performance refers to finding lesions, i.e., true positives, while simultaneously not finding false positive locations. Classification performance measures the ability to distinguish between true and false positive locations. Knowing these separate performances allows principled optimization of reader or AI system performance. This package supersedes Windows JAFROC (jackknife alternative FROC) software V4.2.1, <https://github.com/dpc10ster/WindowsJafroc>. Package functions are organized as follows. Data file related function names are preceded by Df', curve fitting functions by Fit', included data sets by dataset', plotting functions by Plot', significance testing functions by St', sample size related functions by Ss', data simulation functions by Simulate and utility functions by Util'. Implemented are figures of merit (FOMs) for quantifying performance and functions for visualizing empirical or fitted operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC (wAFROC) curves. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is implemented via either Dorfman-Berbaum-Metz or the Obuchowski-Rockette methods. Also implemented is single treatment analysis, which allows comparison of performance of a group of radiologists to a specified value, or comparison of AI to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed treatment factors and the aim is to determined performance in each treatment factor averaged over all levels of the second factor. Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study to predict required numbers of readers and cases in a pivotal study to achieve the desired power. Utility and data file manipulation functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be viewed in text or Excel output files. The methods are illustrated with several included datasets from the author's collaborations. This update includes improvements to the code, some as a result of user-reported bugs and new feature requests, and others discovered during ongoing testing and code simplification.

To facilitate using cereal with R via cpp11 or Rcpp'. cereal is a header-only C++11 serialization library. cereal takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, XML', or JSON'. cereal was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. Please see <https://uscilab.github.io/cereal/> for more information.

An integrated solution to perform a series of text mining tasks such as importing and cleaning a corpus, and analyses like terms and documents counts, lexical summary, terms co-occurrences and documents similarity measures, graphs of terms, correspondence analysis and hierarchical clustering. Corpora can be imported from spreadsheet-like files, directories of raw text files, as well as from Dow Jones Factiva', LexisNexis', Europresse and Alceste files.

This package provides a series of functions in some way considered useful to the author. These include methods for subsetting tables and generating indices for arrays, conditioning and intervening in probability distributions, generating combinations, fast transformations, and more...

Rcmdr menu support for many of the functions in the HH package. The focus is on menu items for functions we use in our introductory courses.

We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.

R interface to CPLEX solvers for linear, quadratic, and (linear and quadratic) mixed integer programs. Support for quadratically constrained programming is available. See the file "INSTALL" for details on how to install the Rcplex package in Linux/Unix-like and Windows systems. Support for sparse matrices is provided by an S3-style class "simple_triplet_matrix" from package slam and by objects from the Matrix package class hierarchy.