Easily use Blueprint', the popular React library from Palantir, in your Shiny app. Blueprint provides a rich set of UI components for creating visually appealing applications and is optimized for building complex, data-dense web interfaces. This package provides most components from the underlying library, as well as special wrappers for some components to make it easy to use them in R without writing JavaScript code.

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 goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is <https://sdmx.org/?page_id=3215/>.

Includes functions for interacting with common meta data fields, writing insert statements, calling functions, and more for T-SQL and Postgresql'.

This package implements a regularization method for cumulative link models using the Smooth-Effect-on-Response Penalty (SERP). This method allows flexible modeling of ordinal data by enabling a smooth transition from a general cumulative link model to a simplified version of the same model. As the tuning parameter increases from zero to infinity, the subject-specific effects for each variable converge to a single global effect. The approach addresses common issues in cumulative link models, such as parameter unidentifiability and numerical instability, by maximizing a penalized log-likelihood instead of the standard non-penalized version. Fitting is performed using a modified Newton's method. Additionally, the package includes various model performance metrics and descriptive tools. For details on the implemented penalty method, see Ugba (2021) <doi:10.21105/joss.03705> and Ugba et al. (2021) <doi:10.3390/stats4030037>.

This package provides a collection of statistical hypothesis tests and other techniques for identifying certain spatial relationships/phenomena in DNA sequences. In particular, it provides tests and graphical methods for determining whether or not DNA sequences comply with Chargaff's second parity rule or exhibit purine-pyrimidine parity. In addition, there are functions for efficiently simulating discrete state space Markov chains and testing arbitrary symbolic sequences of symbols for the presence of first-order Markovianness. Also, it has functions for counting words/k-mers (and cylinder patterns) in arbitrary symbolic sequences. Functions which take a DNA sequence as input can handle sequences stored as SeqFastadna objects from the seqinr package.

Implementation of sequential trial emulation for the analysis of observational databases. The SEQTaRget software accommodates time-varying treatments and confounders, as well as binary and failure time outcomes. SEQTaRget allows to compare both static and dynamic strategies, can be used to estimate observational analogs of intention-to-treat and per-protocol effects, and can adjust for potential selection bias induced by losses-to-follow-up. (Paper to come).

Get sun position, sunlight phases (times for sunrise, sunset, dusk, etc.), moon position and lunar phase for the given location and time. Most calculations are based on the formulas given in Astronomy Answers articles about position of the sun and the planets : <https://www.aa.quae.nl/en/reken/zonpositie.html>.

Take screenshots from R command and locate an image position.

This package provides tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. Smooth curves, fill holes, and remove small fragments from lines and polygons.

Fast and regularized version of GWR for large dataset, detailed in Murakami, Tsutsumida, Yoshida, Nakaya, and Lu (2019) <arXiv:1905.00266>.

This package provides a critical first step in systematic literature reviews and mining of academic texts is to identify relevant texts from a range of sources, particularly databases such as Web of Science or Scopus'. These databases often export in different formats or with different metadata tags. synthesisr expands on the tools outlined by Westgate (2019) <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats (such as bibtex', ris', or ciw') in a standard way, and allows merging and deduplication of the resulting dataset.

This package provides functions to compute standardized differences for numeric, binary, and categorical variables on Apache Spark DataFrames using sparklyr'. The implementation mirrors the methods used in the stddiff package but operates on distributed data. See Zhicheng Du, Yuantao Hao (2022) <doi:10.32614/CRAN.package.stddiff> for reference.

We provide functions for estimation and inference of locally-stationary time series using the sieve methods and bootstrapping procedure. In addition, it also contains functions to generate Daubechies and Coiflet wavelet by Cascade algorithm and to process data visualization.

Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).

Self-Consistent Field(SCF) calculation method is one of the most important steps in the calculation methods of quantum chemistry. Ehrenreich, H., & Cohen, M. H. (1959). <doi:10.1103/PhysRev.115.786> However, the most prevailing software in this area, Gaussian''s SCF convergence process is hard to monitor, especially while the job is still running, causing researchers difficulty in knowing whether the oscillation has started or not, wasting time and energy on useless configurations or abandoning the jobs that can actually work. M.J. Frisch, G.W. Trucks, H.B. Schlegel et al. (2016). <https://gaussian.com> SCFMonitor enables Gaussian quantum chemistry calculation software users to easily read the Gaussian .log files and monitor the SCF convergence and geometry optimization process with little effort and clear, beautiful, and clean outputs. It can generate graphs using tidyverse to let users check SCF convergence and geometry optimization processes in real-time. The software supports processing .log files remotely using with rbase::url(). This software is a suitcase for saving time and energy for the researchers, supporting multiple versions of Gaussian'.

This package creates an S4 class "SSM" and defines functions for fitting smooth supersaturated models, a polynomial model with spline-like behaviour. Functions are defined for the computation of Sobol indices for sensitivity analysis and plotting the main effects using FANOVA methods. It also implements the estimation of the SSM metamodel error using a GP model with a variety of defined correlation functions.

Implementations of a large number of tests for symmetry and their bootstrap variants, which can be used for testing the symmetry of random samples around a known or unknown mean. Functions are also there for testing the symmetry of model residuals around zero. Currently, the supported models are linear models and generalized autoregressive conditional heteroskedasticity (GARCH) models (fitted with the fGarch package). All tests are implemented using the Rcpp package which ensures great performance of the code.

This package provides a flexible framework for definition and application of time/depth- based rules for sets of parameters for single grains that can be used to create artificial sediment profiles. Such profiles can be used for virtual sample preparation and synthetic, for instance, luminescence measurements.

This package provides a set of consistent, opinionated functions to quickly check function arguments, coerce them to the desired configuration, or deliver informative error messages when that is not possible.

Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. The spinebil package contains methods to evaluate the performance of projection pursuit index functions using tour methods. A paper describing the methods can be found at <doi:10.1007/s00180-020-00954-8>.

This package provides functions are provided for internal use by the spatial capture-recapture package secr (from version 5.4.0). The idea is to speed up the installation of secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from RcppNumerical and RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data.

Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.

SEM Trees and SEM Forests -- an extension of model-based decision trees and forests to Structural Equation Models (SEM). SEM trees hierarchically split empirical data into homogeneous groups each sharing similar data patterns with respect to a SEM by recursively selecting optimal predictors of these differences. SEM forests are an extension of SEM trees. They are ensembles of SEM trees each built on a random sample of the original data. By aggregating over a forest, we obtain measures of variable importance that are more robust than measures from single trees. A description of the method was published by Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) <doi:10.1037/a0030001> and Arnold, Voelkle, & Brandmaier (2020) <doi:10.3389/fpsyg.2020.564403>.