The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.

The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.

This package provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) <doi:10.1080/01621459.2015.1021005>]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.

This package provides a template system based on AdminLTE3 (<https://adminlte.io/themes/v3/>) theme. Comes with default theme that can be easily customized. Developers can upload modified templates on Github', and users can easily download templates with RStudio project wizard. The key features of the default template include light and dark theme switcher, resizing graphs, synchronizing inputs across sessions, new notification system, fancy progress bars, and card-like flip panels with back sides, as well as various of HTML tool widgets.

Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.

This package provides a Shiny app including the Monaco editor. The Monaco editor is the code editor which powers VS Code'. It is particularly well developed for JavaScript'. In addition to the Monaco editor features, the app provides prettifiers and minifiers for multiple languages, SCSS and TypeScript compilers, code checking for C and C++ (requires cppcheck').

This package creates D3 JavaScript scatterplots from R with interactive features : panning, zooming, tooltips, etc.

Implementation of the BLEU-Score in C++ to evaluate the quality of generated text. The BLEU-Score, introduced by Papineni et al. (2002) <doi:10.3115/1073083.1073135>, is a metric for evaluating the quality of generated text. It is based on the n-gram overlap between the generated text and reference texts. Additionally, the package provides some smoothing methods as described in Chen and Cherry (2014) <doi:10.3115/v1/W14-3346>.

Application of theoretical results which ensure that the summation of an infinite discrete series is within an arbitrary margin of error of its true value. The C code under the hood is shared through header files to allow users to sum their own low level functions as well. Based on the paper by Braden (1992) <doi: 10.2307/2324995>.

This package provides a system enables cross study Analysis by extracting and filtering study data for control animals from CDISC SEND Study Repository. These data types are supported: Body Weights, Laboratory test results and Microscopic findings. These database types are supported: SQLite and Oracle'.

This package provides utilities for cleaning survey data, computing weights, and performing descriptive statistical analysis. Methods follow Lohr (2019, ISBN:978-0367272454) "Sampling: Design and Analysis" and Lumley (2010) <doi:10.1002/9780470580066>.

It allows running Praat scripts from R and it provides some wrappers for basic plotting. It also adds support for literate markdown tangling. The package is designed to bring reproducible phonetic research into R.

This package implements confidence interval and sample size methods that are especially useful in psychological research. The methods can be applied in 1-group, 2-group, paired-samples, and multiple-group designs and to a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, plus several measures of correlation and association. Confidence interval and sample size functions are given for single parameters as well as differences, ratios, and linear contrasts of parameters. The sample size functions can be used to approximate the sample size needed to estimate a parameter or function of parameters with desired confidence interval precision or to perform a variety of hypothesis tests (directional two-sided, equivalence, superiority, noninferiority) with desired power. For details see: Statistical Methods for Psychologists, Volumes 1 â 4, <https://dgbonett.sites.ucsc.edu/>.

Modeling spatial dependencies in dependent variables, extending traditional spatial regression approaches. It allows for the joint modeling of both the mean and the variance of the dependent variable, incorporating semiparametric effects in both models. Based on generalized additive models (GAM), the package enables the inclusion of non-parametric terms while maintaining the classical theoretical framework of spatial regression. Additionally, it implements the Generalized Spatial Autoregression (GSAR) model, which extends classical methods like logistic Spatial Autoregresive Models (SAR), probit Spatial Autoregresive Models (SAR), and Poisson Spatial Autoregresive Models (SAR), offering greater flexibility in modeling spatial dependencies and significantly improving computational efficiency and the statistical properties of the estimators. Related work includes: a) J.D. Toloza-Delgado, Melo O.O., Cruz N.A. (2024). "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices". <doi:10.1016/j.spasta.2024.100864>. b) Cruz, N. A., Toloza-Delgado, J. D., Melo, O. O. (2024). "Generalized spatial autoregressive model". <doi:10.48550/arXiv.2412.00945>.

The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) <doi:10.1016/j.jeconom.2017.08.009>, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) <doi:10.1093/biomet/asab037> and Yu, X., Yao, J. and Xue, L. (2022) <doi:10.1080/07350015.2020.1813589>.

This package provides tools to simulate and analyze survival data with interval-, left-, right-, and uncensored observations under common parametric distributions, including "Weibull", "Exponential", "Log-Normal", "Log-Logistic", "Gamma", "Gompertz", "Normal", "Logistic", and "EMV". The package supports both direct maximum likelihood estimation and imputation-based methods, making it suitable for methodological research, simulation benchmarking, and teaching. A web-based companion app is also available for demonstration purposes.

This package provides tools for generating and analyzing simulation studies. Users may easily specify all terms of a simulation study, often in a single line of code. Common univariate and bivariate methods, such as t tests, proportions tests, and chi squared tests, are integrated. Multivariate studies involving linear or logistic regression may also be specified with symbolic inputs. The simulation studies generate data for n observations in each of B experiments. Analyses of each experiment are integrated, and empirical results across the experiments are also provided.

This package provides data about the Star Wars movie franchise in a set of relational tables or as a complete DuckDB database. All data was collected from the open source Star Wars API.

This package provides a set of tools inspired by Stata to explore data.frames ('summarize', tabulate', xtile', pctile', binscatter', elapsed quarters/month, lead/lag).

Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.

An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.

Processes amino acid alignments produced by the IPD-IMGT/HLA (Immuno Polymorphism-ImMunoGeneTics/Human Leukocyte Antigen) Database to identify user-defined amino acid residue motifs shared across HLA alleles, HLA alleles, or HLA haplotypes, and calculates frequencies based on HLA allele frequency data. SSHAARP (Searching Shared HLA Amino Acid Residue Prevalence) uses Generic Mapping Tools (GMT) software and the GMT R package to generate global frequency heat maps that illustrate the distribution of each user-defined map around the globe. SSHAARP analyzes the allele frequency data described by Solberg et al. (2008) <doi:10.1016/j.humimm.2008.05.001>, a global set of 497 population samples from 185 published datasets, representing 66,800 individuals total. Users may also specify their own datasets, but file conventions must follow the prebundled Solberg dataset, or the mock haplotype dataset.

This package provides tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible.

This package provides a general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance.