This package provides a collection of functions for modelling mutations in pedigrees with marker data, as used e.g. in likelihood computations with microsatellite data. Implemented models include equal, proportional and stepwise models, as well as random models for experimental work, and custom models allowing the user to apply any valid mutation matrix. Allele lumping is done following the lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922.

PROMETHEE (Preference Ranking Organisation METHod for Enrichment of Evaluations) based method assesses alternatives to obtain partial and complete rankings. The package also provides the GLNF (Global Local Net Flow) sorting algorithm to classify alternatives into ordered categories, as well as an index function to measure the classification quality. Barrera, F., Segura, M., & Maroto, C. (2023) <doi:10.1111/itor.13288>. Brans, J.P.; De Smet, Y., (2016) <doi:10.1007/978-1-4939-3094-4_6>.

This package provides tools for simplifying the creation and management of data structures suitable for dealing with policy portfolios, that is, two-dimensional spaces of policy instruments and policy targets. The package also allows to generate measures of portfolio characteristics and facilitates their visualization.

This package provides a bioinformatics method developed for analyzing the heterogeneity of single-cell populations. Phitest provides an objective and automatic method to evaluate the performance of clustering and quality of cell clusters.

This package provides a polycross is the pollination by natural hybridization of a group of genotypes, generally selected, grown in isolation from other compatible genotypes in such a way to promote random open pollination. A particular practical application of the polycross method occurs in the production of a synthetic variety resulting from cross-pollinated plants. Laying out these experiments in appropriate designs, known as polycross designs, would not only save experimental resources but also gather more information from the experiment. Different experimental situations may arise in polycross nurseries which may be requiring different polycross designs (Varghese et. al. (2015) <doi:10.1080/02664763.2015.1043860>. " Experimental designs for open pollination in polycross trials"). This package contains a function named PD() which generates nine types of polycross designs suitable for various experimental situations.

This package provides functions to process, format and store ActiGraph GT1M and GT3X accelerometer data.

The Penn World Table 9.x (<http://www.ggdc.net/pwt/>) provides information on relative levels of income, output, inputs, and productivity for 182 countries between 1950 and 2017.

Utilizes the lme4 and optimx packages (previously the optim() function from stats') to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>. Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models.

This package provides a comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.

This package provides tools for extracting and processing structured annotations from R and Python source files to facilitate workflow visualization. The package scans source files for special PUT annotations that define nodes, connections, and metadata within a data processing workflow. These annotations can then be used to generate visual representations of data flows and processing steps across polyglot software environments. Builds on concepts from literate programming Knuth (1984) <doi:10.1093/comjnl/27.2.97> and utilizes directed acyclic graph (DAG) theory for workflow representation Foraita, Spallek, and Zeeb (2014) <doi:10.1007/978-0-387-09834-0_65>. Diagram generation powered by Mermaid Sveidqvist (2014) <https://mermaid.js.org/>.

Win ratio approach to partially ordered data, such as multivariate ordinal responses under product (consensus) or prioritized order. Two-sample tests and multiplicative regression models are implemented (Mao, 2024, under revision).

Computes power and level tables for goodness-of-fit tests for the normal, Laplace, and uniform distributions. Generates output in LaTeX format to facilitate reporting and reproducibility. Explanatory graphs help visualize the statistical power of test statistics under various alternatives. For more details, see Lafaye De Micheaux and Tran (2016) <doi:10.18637/jss.v069.i03>.

This package provides a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from ImageJ', an open platform for scientific image analysis (see <https://imagej.net> for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width.

This package performs statistical tests to compare coefficients and residual variance across models. Also provides graphical methods for assessing heterogeneity in coefficients and residuals. Currently supports linear and generalized linear models.

This package provides a toolbox to facilitate the calculation of political system indicators for researchers. This package offers a variety of basic indicators related to electoral systems, party systems, elections, and parliamentary studies, as well as others. Main references are: Loosemore and Hanby (1971) <doi:10.1017/S000712340000925X>; Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>; Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>; Rae (1968) <doi:10.1177/001041406800100305>; HirschmaÅ (1945) <ISBN:0-520-04082-1>; Kesselman (1966) <doi:10.2307/1953769>; Jones and Mainwaring (2003) <doi:10.1177/13540688030092002>; Rice (1925) <doi:10.2307/2142407>; Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>; SANTOS (2002) <ISBN:85-225-0395-8>.

Computing Average and TPX Power under various BHFDR type sequential procedures. All of these procedures involve control of some summary of the distribution of the FDP, e.g. the proportion of discoveries which are false in a given experiment. The most widely known of these, the BH-FDR procedure, controls the FDR which is the mean of the FDP. A lesser known procedure, due to Lehmann and Romano, controls the FDX, or probability that the FDP exceeds a user provided threshold. This is less conservative than FWE control procedures but much more conservative than the BH-FDR proceudre. This package and the references supporting it introduce a new procedure for controlling the FDX which we call the BH-FDX procedure. This procedure iteratively identifies, given alpha and lower threshold delta, an alpha* less than alpha at which BH-FDR guarantees FDX control. This uses asymptotic approximation and is only slightly more conservative than the BH-FDR procedure. Likewise, we can think of the power in multiple testing experiments in terms of a summary of the distribution of the True Positive Proportion (TPP), the portion of tests truly non-null distributed that are called significant. The package will compute power, sample size or any other missing parameter required for power defined as (i) the mean of the TPP (average power) or (ii) the probability that the TPP exceeds a given value, lambda, (TPX power) via asymptotic approximation. All supplied theoretical results are also obtainable via simulation. The suggested approach is to narrow in on a design via the theoretical approaches and then make final adjustments/verify the results by simulation. The theoretical results are described in Izmirlian, G (2020) Statistics and Probability letters, "<doi:10.1016/j.spl.2020.108713>", and an applied paper describing the methodology with a simulation study is in preparation. See citation("pwrFDR").

This package provides a convenient framework for aggregating and disaggregating continuously varying parameters (for example, case fatality ratio, with age) for proper parametrization of lower-resolution compartmental models (for example, with broad age categories) and subsequent upscaling of model outputs to high resolution (for example, as needed when calculating age-sensitive measures like years-life-lost).

Several tests of quantitative palaeoenvironmental reconstructions from microfossil assemblages, including the null model tests of the statistically significant of reconstructions developed by Telford and Birks (2011) <doi:10.1016/j.quascirev.2011.03.002>, and tests of the effect of spatial autocorrelation on transfer function model performance using methods from Telford and Birks (2009) <doi:10.1016/j.quascirev.2008.12.020> and Trachsel and Telford (2016) <doi:10.5194/cp-12-1215-2016>. Age-depth models with generalized mixed-effect regression from Heegaard et al (2005) <doi:10.1191/0959683605hl836rr> are also included.

Simulate via Markov chain Monte Carlo (hit-and-run algorithm) a Dirichlet distribution conditioned to satisfy a finite set of linear equality and inequality constraints (hence to lie in a convex polytope that is a subset of the unit simplex).

Conservation planning datasets for learning how to use the prioritizr package <https://CRAN.R-project.org/package=prioritizr>.

This package provides convenience functions and pre-programmed Stan models related to the paired comparison factor model. Its purpose is to make fitting paired comparison data using Stan easy. This package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.

This package provides functions to simplify the PatentsView API (<https://patentsview.org/apis/purpose>) query language, send GET and POST requests to the API's seven endpoints, and parse the data that comes back.

This package provides tools that allow developers to write functions for prediction error estimation with minimal programming effort and assist users with model selection in regression problems.

Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.