This package implements the pcgen algorithm, which is a modified version of the standard pc-algorithm, with specific conditional independence tests and modified orientation rules. pcgen extends the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of direct genetic effects.

This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the gurobi installation vignette of the prioritizr package (available in <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, CPLEX optimization solver can be obtain from IBM CPLEX web page (available here <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, the rcbc R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>, Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015) <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. See the prioriactions website for more information, documentations and examples.

This function plots a contour line with a user-defined probability and tightness of fit.

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>.

This package provides tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients and the relationship matrix (includes code for pedigrees from self-pollinated species). The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure ('Vazquez et al., 2010') <doi:10.2527/jas.2009-1952>.

This package implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.

Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.

This package provides functions for data normalization and transformation in preprocessing stages. Implements scaling methods (min-max, Z-score, L2 normalization) and power transformations (Box-Cox, Yeo-Johnson). Box-Cox transformation is described in Box and Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Yeo-Johnson transformation in Yeo and Johnson (2000) <doi:10.1093/biomet/87.4.954>.

This package provides methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

This package provides a method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) Principal component-guided sparse regression <arXiv:1810.04651>.

This package implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.

This package implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.

Displays provenance graphically for provenance collected by the rdt or rdtLite packages, or other tools providing compatible PROV JSON output. The exact format of the JSON created by rdt and rdtLite is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>. More information about rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.

Psychometric mixture models based on flexmix infrastructure. At the moment Rasch mixture models with different parameterizations of the score distribution (saturated vs. mean/variance specification), Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated with or without concomitant variables. See Frick et al. (2012) <doi:10.18637/jss.v048.i07> and Frick et al. (2015) <doi:10.1177/0013164414536183> for details on the Rasch mixture models.

Pivotal Tracker <https://www.pivotaltracker.com> is a project management software-as-a-service that provides a REST API. This package provides an R interface to that API, allowing you to query it and work with its responses.

Automated pain scoring from paw withdrawal tracking data. Based on Jones et al. (2020) "A machine-vision approach for automated pain measurement at millisecond timescales" <doi:10.7554/eLife.57258>.

This package provides a focused implementation of the Public Suffix List (PSL). Bundles a reproducible, pinned PSL snapshot and implements the official prevailing-rule algorithm to answer public-suffix (eTLD) and registrable-domain (eTLD+1) queries. Distinguishes ICANN and PRIVATE rule sections, accepts Unicode and ASCII hostnames via punycoder canonicalization, and supports an explicit, validated offline refresh path. The matcher is compiled with cpp11 and requires no external system library.

This package performs partial principal component analysis of a large sparse matrix. The matrix may be stored as a list of matrices to be concatenated (implicitly) horizontally. Useful application includes cases where the number of total nonzero entries exceed the capacity of 32 bit integers (e.g., with large Single Nucleotide Polymorphism data).

We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.

Send push notifications to mobile devices or the desktop using Pushover <https://pushover.net>. These notifications can display things such as results, job status, plots, or any other text or numeric data.

This package provides tools for statistical testing of correlation coefficients through robust permutation method and large sample approximation method. Tailored to different types of correlation coefficients including Pearson correlation coefficient, weighted Pearson correlation coefficient, Spearman correlation coefficient, and Lin's concordance correlation coefficient.The robust permutation test controls type I error under general scenarios when sample size is small and two variables are dependent but uncorrelated. The large sample approximation test generally controls type I error when the sample size is large (>200).

Identifies reference periods (months, fortnights, and weeks) in Brazil's quarterly PNADC (Pesquisa Nacional por Amostra de Domicilios Continua) survey data and computes calibrated weights for sub-quarterly analysis. The core algorithm uses IBGE (Instituto Brasileiro de Geografia e Estatistica) Parada Tecnica (technical break) rules combined with respondent birthdates to determine which temporal period each survey observation refers to. Period identification follows a nested hierarchy enforced by construction: fortnights require months, weeks require fortnights. Achieves approximately 97% monthly determination rate with the full series (2012-2025). Strict fortnight and week rates are approximately 9% and 3% respectively, as they cannot leverage cross-quarter panel aggregation. Experimental strategies (probabilistic assignment and UPA (Primary Sampling Unit) aggregation) further improve these determination rates. The package provides adaptive hierarchical weight calibration (4/2/1 cell levels for month/fortnight/week) with period-specific smoothing to produce survey weights calibrated to SIDRA (Sistema IBGE de Recuperacao Automatica) population totals. Also includes a SIDRA mensalization module that converts 86+ official rolling quarter series from the IBGE SIDRA API (Application Programming Interface) into exact monthly estimates, without requiring access to microdata. Hecksher (2020) <https://repositorio.ipea.gov.br/handle/11058/9859>.

Calculate Predictive Moran's Eigenvector Maps (pMEM) for spatially-explicit prediction of environmental variables, as defined by Guénard and Legendre (2024) <doi:10.1111/2041-210X.14413>. pMEM extends classical MEM by enabling interpolation and prediction at unsampled locations using spatial weighting functions parameterized by range (and optionally shape). The package implements multiple pMEM types (e.g., exponential, Gaussian, linear) and features a modular architecture that allows programmers to define custom weighting functions. Designed for ecologists, geographers, and spatial analysts working with spatially-structured data.

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, see the Package vignette for details).