Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.

This package provides a set of Study Data Tabulation Model (SDTM) datasets constructed by modifying the pharmaversesdtm package to meet J&J Innovative Medicine's standard data structure for Clinical and Statistical Programming.

Allows users to access the Oregon State Prism climate data (<https://prism.nacse.org/>). Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.

Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).

Implementation of T. Hailperin's procedure to calculate lower and upper bounds of the probability for a propositional-logic expression, given equality and inequality constraints on the probabilities for other expressions. Truth-valuation is included as a special case. Applications range from decision-making and probabilistic reasoning, to pedagogical for probability and logic courses. For more details see T. Hailperin (1965) <doi:10.1080/00029890.1965.11970533>, T. Hailperin (1996) "Sentential Probability Logic" ISBN:0-934223-45-9, and package documentation. Requires the lpSolve package.

Following Sommer (2022) <https://mediatum.ub.tum.de/1658240> portfolio level risk estimates (e.g. Value at Risk, Expected Shortfall) are estimated by modeling each asset univariately by an ARMA-GARCH model and then their cross dependence via a Vine Copula model in a rolling window fashion. One can even condition on variables/time series at certain quantile levels to stress test the risk measure estimates.

Most price indexes are made with a two-step procedure, where period-over-period elementary indexes are first calculated for a collection of elementary aggregates at each point in time, and then aggregated according to a price index aggregation structure. These indexes can then be chained together to form a time series that gives the evolution of prices with respect to a fixed base period. This package contains a collection of functions that revolve around this work flow, making it easy to build standard price indexes, and implement the methods described by Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007, <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020, <doi:10.5089/9781484354841.069>) for bilateral price indexes.

This package implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), <doi:10.1214/13-EJS810> and Lee, E-K. (2018) <doi:10.18637/jss.v083.i08>. The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group.

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 a suite of empirical Bayes methods to use in pharmacovigilance. Contains various model fitting and post-processing functions. For more details see Tan et al. (2025) <doi:10.48550/arXiv.2502.09816>, <doi:10.48550/arXiv.2512.01057>; Koenker and Mizera (2014) <doi:10.1080/01621459.2013.869224>; Efron (2016) <doi:10.1093/biomet/asv068>.

This package provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).

Parsimonious Ultrametric Gaussian Mixture Models via grouped coordinate ascent (equivalent to EM) algorithm characterized by the inspection of hierarchical relationships among variables via parsimonious extended ultrametric covariance structures. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, (2022) <doi:10.1007/s11634-021-00488-x> and (2020) <doi:10.1007/s11634-020-00400-z>.

Computation of robust standard errors of Poisson fixed effects models, following Wooldridge (1999).

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.

Simulation of continuous, correlated high-dimensional data with time to event or binary response, and parallelized functions for Lasso, Ridge, and Elastic Net penalized regression with repeated starts and two-dimensional tuning of the Elastic Net.

Extends the popular lavaan package by adding penalized estimation capabilities. It supports penalty on individual parameters as well as the difference between parameters.

This package provides functions to setup a personal R package that attaches given libraries and exports personal helper functions.

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

An implementation of an S3 class based on a double vector for storing and displaying precision teaching measures, representing a growing or a decaying (multiplicative) change between two frequencies. The main format method allows researchers to display measures (including data.frame) that respect the established conventions in the precision teaching community (i.e., prefixed multiplication or division symbol, displayed number <= 1). Basic multiplication and division methods are allowed and other useful functions are provided for creating, converting or inverting precision teaching measures. For more details, see Pennypacker, Gutierrez and Lindsley (2003, ISBN: 1-881317-13-7).

This package provides a bunch of convenience functions that transform the results of some basic statistical analyses into table format nearly ready for publication. This includes descriptive tables, tables of logistic regression and Cox regression results as well as forest plots.

Wrangle and annotate different types of political texts. It also introduces Urgency Analysis, a new method for the analysis of urgency in political texts.

This package provides functions to create confidence intervals for ratios of Poisson rates under misclassification using double sampling. Implementations of the methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016). "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way Differential Misclassification Using Double Sampling." Computational Statistics & Data Analysis, 95:122â 132.

Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.

Reviews other packages during code review by looking at their dependencies, code style, code complexity, and how internally defined functions interact with one another.