This package provides functions to aid in micro and macro economic analysis and handling of price and currency data. Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020).

For working with the Prevision.io AI model management platform's API <https://prevision.io/>.

This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) <https://etda.libraries.psu.edu/catalog/13521clj5135>.

Metadata and package cache for CRAN-like repositories. This is a utility package to be used by package management tools that want to take advantage of caching.

Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.

Check a data frame for personal information, including names, location, disability status, and geo-coordinates.

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 predictive microbiology model functions and convenience wrappers for fitting primary growth, microbial inactivation, dynamic, omnibus, and cardinal parameter models to experimental data using nonlinear least squares and related mixed-effects or time-varying workflows. Includes helper functions for extracting fitted values, calculating model diagnostics, and comparing fitted models. Implemented model families include those described by: Zwietering et al. (1990) <doi:10.1128/AEM.56.6.1875-1881.1990>, Baranyi and Roberts (1994) <doi:10.1016/0168-1605(94)90157-0>, Baranyi and Roberts (1995) <doi:10.1016/0168-1605(94)00121-L>, Buchanan et al. (1997) <doi:10.1006/fmic.1997.0125>, Richards (1959) <doi:10.1093/jxb/10.2.290>, Fang et al. (2012) <doi:10.1111/j.1750-3841.2012.02873.x>, Fang et al. (2013) <doi:10.1016/j.fm.2012.12.005>, Huang (2008) <doi:10.1111/j.1750-3841.2008.00785.x>, Huang (2009) <doi:10.1016/j.jfoodeng.2008.07.011>, Huang (2013) <doi:10.1016/j.foodcont.2012.11.019>, Geeraerd et al. (2005) <doi:10.1016/j.ijfoodmicro.2004.11.038>, van Boekel (2002) <doi:10.1016/S0168-1605(01)00742-5>, Peleg (1999) <doi:10.1016/S0963-9969(99)00081-2>, Mafart et al. (2002) <doi:10.1016/S0168-1605(01)00624-9>, Albert and Mafart (2005) <doi:10.1016/j.ijfoodmicro.2004.10.016>, Rosso et al. (1993) <doi:10.1006/jtbi.1993.1099>, Rosso et al. (1995) <doi:10.1128/AEM.61.2.610-616.1995>, and Rosso et al. (1996) <doi:10.4315/0362-028X-59.9.944>.

Plot marginal effects for interactions estimated from linear models.

Uses provenance post-execution to help the user understand and debug their script by providing functions to look at intermediate steps and data values, their forwards and backwards lineage, and to understand the steps leading up to warning and error messages. provDebugR uses provenance produced by rdtLite (available on CRAN), stored in PROV-JSON format.

We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?

Fast tools for fitting sparse generalized linear models with convex penalties (lasso) and concave penalties (smoothly clipped absolute deviation and minimax concave penalty). Computation uses multi-stage convex relaxation and pathwise coordinate optimization with warm starts, active-set updates, and screening rules. Core solvers are implemented in C++, and coefficient paths are stored as sparse matrices for memory efficiency.

Pivot easily by specifying rows, columns, values and split.

This package provides a set of concise and efficient tools for statistical production. Can also be used for data management. In statistical production, you deal with complex data and need to control your process at each step of your work. Concise functions are very helpful, because you do not hesitate to use them. The following functions are included in the package. dup checks duplicates. miss checks missing values. tac computes contingency table of all columns. toc compares two tables, spotting significant deviations. chi2_find compares columns within a data.frame, spotting related categories of (a more complex function).

Based on (but not identical to) the no-longer-maintained package phyext', provides enhancements to phylobase classes, specifically for use by package SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in SigTree'); also provides support for a few other extra features.

Performant interactive scatterplot for ~ 1 million points. Zoom, pan, and pick points. Includes tooltips, labels, a grid overlay, legend, and coupled interactions across multiple plots.

Design parameters of the optimal two-period multiarm platform design (controlling for either family-wise error rate or pair-wise error rate) can be calculated using this package, allowing pre-planned deferred arms to be added during the trial. More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) <doi:10.2307/2281208>.

Aims to utilize model-based clustering (unsupervised) for high dimensional and ultra large data, especially in a distributed manner. The code employs pbdMPI to perform a expectation-gathering-maximization algorithm for finite mixture Gaussian models. The unstructured dispersion matrices are assumed in the Gaussian models. The implementation is default in the single program multiple data programming model. The code can be executed through pbdMPI and MPI implementations such as OpenMPI and MPICH'. See the High Performance Statistical Computing website <https://snoweye.github.io/hpsc/> for more information, documents and examples.

Reproducible, programmatic retrieval of survey datasets from the Pew Research Center.

Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) <doi:10.48550/arXiv.0909.5262>. The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos.

Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).

Power and sample size calculation for testing fixed effect coefficients in multilevel linear mixed effect models with one or more than one independent populations. Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.

This package provides a collection of easy-to-use tools for regression analysis of survival data with a cure fraction proposed in Su et al. (2022) <doi:10.1177/09622802221108579>. The modeling framework is based on the Cox proportional hazards mixture cure model and the bounded cumulative hazard (promotion time cure) model. The pseudo-observations approach is utilized to assess covariate effects and embedded in the variable selection procedure.

Creates, fits and samples Pair-Copula Bayesian networks (PCBN) under some restrictions on the underlying Directed Acyclic Graph (DAG), that is, no active cycles nor interfering v-structures, following Derumigny, Horsman and Kurowicka (2025) <doi:10.48550/arXiv.2510.03518>.