This package provides analytic and simulation tools to estimate the minimum sample size required for achieving a target prediction mean-squared error (PMSE) or a specified proportional PMSE reduction (pPMSEr) in linear regression models. Functions implement the criteria of Ma (2023) <https://digital.wpi.edu/downloads/0g354j58c>, support covariance-matrix handling, and include helpers for root-finding and diagnostic plotting.

This package performs smoothed (and non-smoothed) principal/independent components analysis of functional data. Various functional pre-whitening approaches are implemented as discussed in Vidal and Aguilera (2022) â Novel whitening approaches in functional settings", <doi:10.1002/sta4.516>. Further whitening representations of functional data can be derived in terms of a few principal components, providing an avenue to explore hidden structures in low dimensional settings: see Vidal, Rosso and Aguilera (2021) â Bi-smoothed functional independent component analysis for EEG artifact removalâ , <doi:10.3390/math9111243>.

Given a set of source zone polygons such as census tracts or city blocks alongside with population counts and a target zone of incogruent yet superimposed polygon features (such as individual buildings) populR transforms population counts from the former to the latter using Areal Interpolation methods.

This package implements novel tools for estimating sample sizes needed for phylogenetic studies, including studies focused on estimating the probability of true pathogen transmission between two cases given phylogenetic linkage and studies focused on tracking pathogen variants at a population level. Methods described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, DiPrete, and Lessler (2023).

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/>.

Makes the time series prediction easier by automatizing this process using four main functions: prep(), modl(), pred() and postp(). Features different preprocessing methods to homogenize variance and to remove trend and seasonality. Also has the potential to bring together different predictive models to make comparatives. Features ARIMA and Data Mining Regression models (using caret).

Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.

This package provides a suite of Propensity Score Predictive Inference (PSPI) methods to generalize treatment effects in trials to target populations. The package includes an existing model Bayesian Causal Forest (BCF) and four PSPI models (BCF-PS, FullBART, SplineBART, DSplineBART). These methods leverage Bayesian Additive Regression Trees (BART) to adjust for high-dimensional covariates and nonlinear associations, while SplineBART and DSplineBART further use propensity score based splines to address covariate shift between trial data and target population.

Includes JavaScript files that allow plotly maps to render without an internet connection.

This package provides a collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate.

This package provides a system contains easy-to-use tools for the conditional estimation of the prevalence of an emerging or rare infectious diseases using the methods proposed in Guerrier et al. (2023) <arXiv:2012.10745>.

Obtener listado de datos, acceder y extender series del Portal de Datos de Hacienda.Las proyecciones se realizan con forecast', Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>. Search, download and forecast time-series from the Ministry of Economy of Argentina. Forecasts are built with the forecast package, Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>.

The spatial interpolation of genetic distances between samples is based on a modified kriging method that accepts a genetic distance matrix and generates a map of probability of lineage presence. This package also offers tools to generate a map of potential contact zones between groups with user-defined thresholds in the tree to account for old and recent divergence. Additionally, it has functions for IDW interpolation using genetic data and midpoints.

See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.

Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.

Google Pathways Language Model 2 (PaLM 2) as a coding and writing assistant designed for R'. With a range of functions, including natural language processing and coding optimization, to assist R developers in simplifying tedious coding tasks and content searching.

Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042>. For full functionality, the software ncbi-blast+ is needed, see <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html> for more information.

Implementation of a KL-based (Kullback-Leibler) test for MCAR (Missing Completely At Random) in the context of missing data as introduced in Michel et al. (2021) <arXiv:2109.10150>.

Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in Knitr'/'Latex integration within *.Rnw files.

An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) <doi:10.48550/arXiv.2304.00290>. Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only C++ 14 leveraging the Eigen library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided.

Visualizes panel data. It has four main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. For details, see <doi:10.18637/jss.v107.i07>.

Most of the time floating point arithmetic does approximately the right thing. When adding sums or having products of numbers that greatly differ in magnitude, the floating point arithmetic may be incorrect. This package implements the Kahan (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from NumPy', See Castaldo (2008) <doi:10.1137/070679946> for a discussion of accuracy), and arbitrary precision sum (adapted from the fsum in Python ; Shewchuk (1997) <https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf>). In addition, products are changed to long double precision for accuracy, or changed into a log-sum for accuracy.

This package provides functions for calculating and analyzing the proliferative index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, et al. bioRxiv, 2016 <doi:10.1101/063057>.

This package implements conjugate power priors for efficient Bayesian analysis of normal data. Power priors allow principled incorporation of historical information while controlling the degree of borrowing through a discounting parameter (Ibrahim and Chen (2000) <doi:10.1214/ss/1009212519>). This package provides closed-form conjugate representations for both univariate and multivariate normal data using Normal-Inverse-Chi-squared and Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling. The conjugate framework builds upon standard Bayesian methods described in Gelman et al. (2013, ISBN:978-1439840955).