Shrinkage estimator for polygenic risk prediction (PRS) models based on summary statistics of genome-wide association (GWA) studies. Based upon the methods and original PANPRS package as found in: Chen, Chatterjee, Landi, and Shi (2020) <doi:10.1080/01621459.2020.1764849>.

An implementation of the sample size computation method for network models proposed by Constantin et al. (2023) <doi:10.1037/met0000555>. The implementation takes the form of a three-step recursive algorithm designed to find an optimal sample size given a model specification and a performance measure of interest. It starts with a Monte Carlo simulation step for computing the performance measure and a statistic at various sample sizes selected from an initial sample size range. It continues with a monotone curve-fitting step for interpolating the statistic across the entire sample size range. The final step employs stratified bootstrapping to quantify the uncertainty around the fitted curve.

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.

Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS.

Presentation of distributions such as: two-piece power normal (TPPN), plasticizing component (PC), DS normal (DSN), expnormal (EN), Sulewski plasticizing component (SPC), easily changeable kurtosis (ECK) distributions. Density, distribution function, quantile function and random generation are presented. For details on this method see: Sulewski (2019) <doi:10.1080/03610926.2019.1674871>, Sulewski (2021) <doi:10.1080/03610926.2020.1837881>, Sulewski (2021) <doi:10.1134/S1995080221120337>, Sulewski (2022) <"New members of the Johnson family of probability dis-tributions: properties and application">, Sulewski, Volodin (2022) <doi:10.1134/S1995080222110270>, Sulewski (2023) <doi:10.17713/ajs.v52i3.1434>.

This package provides tools for visual exploratory data analysis with nested data. Includes functions for creating bivariate plots, dot plots, histograms, and violin plots for each group or unit in nested data. Methods are described in Crabtree and Nelson (2017) "Plotrr: Functions for making visual exploratory data analysis with nested data easier" <doi:10.21105/joss.00190>.

An R-Shiny application implementing a method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.

Fit linear mixed models dedicated to the genetic study of plant mixtures, such as those based on general and specific mixing abilities (GMA-SMA) as well as direct and social breeding values (DBV-SBV), also known as direct and indirect genetic effects (DGE-IGE). More details in Forst et al (2019, <doi:10.1016/j.fcr.2019.107571>) for GMA-SMA models, and Salomon et al (2026, <doi:10.64898/2026.03.27.714849>) for DBV-SBV models. The package also provides functions to optimize experimental designs, simulate data sets and compute interaction indices.

This package provides a grammar of graphics framework built on base graphics. It provides a bbplot object and a + operator to incrementally compose plots from data, aesthetic mappings and layers, then render them using the base plotting system. The package includes common geometric layers (points, lines, segments, bars, histograms, boxplots and tiles), scales for color and other aesthetics, legends, faceting, themes, and significance annotations.

Multi-state models are essential tools in longitudinal data analysis. One primary goal of these models is the estimation of transition probabilities, a critical metric for predicting clinical prognosis across various stages of diseases or medical conditions. Traditionally, inference in multi-state models relies on the Aalen-Johansen (AJ) estimator which is consistent under the Markov assumption. However, in many practical applications, the Markovian nature of the process is often not guaranteed, limiting the applicability of the AJ estimator in more complex scenarios. This package extends the landmark Aalen-Johansen estimator (Putter, H, Spitoni, C (2018) <doi:10.1177/0962280216674497>) incorporating presmoothing techniques described by Soutinho, Meira-Machado and Oliveira (2020) <doi:10.1080/03610918.2020.1762895>, offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models with multiple states and potential reversible transitions.

This package provides a framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.

Fornece acesso eficiente à malha espacial de precariedade viária brasileira. O pacote realiza o download em cache e a leitura otimizada (via Apache Arrow) de arquivos Parquet particionados, contendo o cruzamento de variáveis de infraestrutura do Entorno do Censo Demográfico 2022 (IBGE) com a malha viária aberta do Overture Maps. [English] Provides efficient access to the spatial network of road precariousness in Brazil. The package performs cached downloads and optimized reading (via Apache Arrow) of partitioned Parquet files. These files contain the intersection of infrastructure variables from the 2022 Demographic Census (IBGE) with the open street network from Overture Maps. Methodology and datasets are detailed in Passos (2026) <doi:10.5281/zenodo.19711448>.

This package provides a set of tools that enables efficient estimation of penalized Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models that feature one or more sets of high-dimensional fixed effects. The methodology is based on Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> and takes advantage of the method of alternating projections of Gaure (2013) <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) <doi:10.1080/07350015.2015.1102733>.

This package provides a comprehensive set of tools to simulate, evaluate, and compare model-assisted designs for early-phase (Phase I/II) clinical trials, including: - BOIN12 (Bayesian optimal interval phase 1/11 trial design; Lin et al. (2020) <doi:10.1200/PO.20.00257>), - BOIN-ET (Takeda, K., Taguri, M., & Morita, S. (2018) <doi:10.1002/pst.1864>), - EffTox (Thall, P. F., & Cook, J. D. (2004) <doi:10.1111/j.0006-341X.2004.00218.x>), - Ji3+3 (Joint i3+3 design; Lin, X., & Ji, Y. (2020) <doi:10.1080/10543406.2020.1818250>), - PRINTE (probability intervals of toxicity and efficacy design; Lin, X., & Ji, Y. (2021) <doi:10.1177/0962280220977009>), - STEIN (simple toxicity and efficacy interval design; Lin, R., & Yin, G. (2017) <doi:10.1002/sim.7428>), - TEPI (toxicity and efficacy probability interval design; Li, D. H., Whitmore, J. B., Guo, W., & Ji, Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>), - uTPI (utility-based toxicity Probability interval design; Shi, H., Lin, R., & Lin, X. (2024) <doi:10.1002/sim.8922>). Includes flexible simulation parameters that allow researchers to efficiently compute operating characteristics under various fixed and random trial scenarios and export the results.

This package provides methods to detect genetic markers involved in biological adaptation. pcadapt provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) <DOI:10.1111/1755-0998.12592> and later revised in (Privé, 2020) <DOI:10.1093/molbev/msaa053>.

Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).

This package provides a collection of functions that can be used to estimate selection and complementarity effects, sensu Loreau & Hector (2001) <doi:10.1038/35083573>, even in cases where data are only available for a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the statistical corrections used here is available in Clark et al. (2019) <doi:10.1111/2041-210X.13285>.

Implementations of Power Fuzzy Clustering (PFC) and Power Fuzzy Cluster-wise Regression (PFCR) for multivariate data. The package supports Minkowski distances, with the L1 case solved via iteratively re-weighted least squares and the case p > 1 solved via coordinate-wise root finding, as well as an adaptive, regularised Mahalanobis distance with per-cluster covariance matrices. Both plain fuzzy clustering and cluster-wise linear regression are provided. The corresponding paper can be found at Nguyen P.T., Tortora C., and Punzo A. (2026) <doi:10.1109/TFUZZ.2026.3683998>.

It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.

This package provides a parallel estimation method for generalized linear models without compiling with a multithreaded LAPACK or BLAS.

This package provides tools for scraping match statistics and player data from the Athletes Unlimited (UA) website <https://auprosports.com/volleyball/>, the League One Volleyball website <https://lovb.com>, and the Major League (MLV) website <https://provolleyball.com>.

Set of tools to automatize extraction of data on pests from EPPO Data Services and EPPO Global Database and to put them into tables with human readable format. Those function use EPPO database API', thus you first need to register on <https://data.eppo.int> (free of charge). Additional helpers allow to download, check and connect to SQLite EPPO database'.

This package provides a collection of scripts and data files for the statistics text: "Process Improvement using Data" <https://learnche.org/pid/> and the online course "Experimentation for Improvement" found on Coursera. The package contains code for designed experiments, data sets and other convenience functions used in the book.

Shiny app to interactively visualize hierarchical clustering with prototypes. For details on hierarchical clustering with prototypes, see Bien and Tibshirani (2011) <doi:10.1198/jasa.2011.tm10183>. This package currently launches the application.