Takes one or more fitted Cox proportional hazards models and writes a shiny application to a directory specified by the user. The shiny application displays predicted survival curves based on user input, and contains none of the original data used to create the Cox model or models. The goal is towards visualization and presentation of predicted survival curves.

Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies.

Shortest paths between points in grids. Optional barriers and custom transition functions. Applications regarding planet Earth, as well as generally spheres and planes. Optimized for computational performance, customizability, and user friendliness. Graph-theoretical implementation tailored to gridded data. Currently focused on Dijkstra's (1959) <doi:10.1007/BF01386390> algorithm. Future updates broaden the scope to other least cost path algorithms and to centrality measures.

The SoundexBR package provides an algorithm for decoding names into phonetic codes, as pronounced in Portuguese. The goal is for homophones to be encoded to the same representation so that they can be matched despite minor differences in spelling. The algorithm mainly encodes consonants; a vowel will not be encoded unless it is the first letter. The soundex code resultant consists of a four digits long string composed by one letter followed by three numerical digits: the letter is the first letter of the name, and the digits encode the remaining consonants.

This package performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) <DOI:10.1111/1475-6773.12628>.

An MCMC algorithm for simultaneous feature selection and classification, and visualization of the selected features and feature interactions. An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.

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.

Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) <doi:10.17169/refubium-622>.

The implementation to perform the geometric spatial point analysis developed in Hernández & Solàs (2022) <doi:10.1007/s00180-022-01244-1>. It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the sf package. The package has methods to print and plot the results.

The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.

Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.

To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation). The methods used in this package are referenced from Zoran B. PopoviÄ (2017) <doi:10.21037/cdt.2017.03.12>.

This package provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) <doi:10.1093/biomet/86.1.59> and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published.

Bayesian inference for parametric proportional hazards spatial survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) <doi:10.18637/jss.v077.i04>.

This package provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288> and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user.

Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <doi:10.1007/bf02295641> and Dusseldorp, Conversano, Van Os (2010) <doi:10.1198/jcgs.2010.06089>, integrating a regression tree and a multiple regression model.

This package provides a comprehensive set of string manipulation functions based on those found in Python without relying on reticulate'. It provides functions that intend to (1) make it easier for users familiar with Python to work with strings, (2) reduce the complexity often associated with string operations, (3) and enable users to write more readable and maintainable code that manipulates strings.

This package provides statistical process control tools for stochastic textured surfaces. The current version supports the following tools: (1) generic modeling of stochastic textured surfaces. (2) local defect monitoring and diagnostics in stochastic textured surfaces, which was proposed by Bui and Apley (2018a) <doi:10.1080/00401706.2017.1302362>. (3) global change monitoring in the nature of stochastic textured surfaces, which was proposed by Bui and Apley (2018b) <doi:10.1080/00224065.2018.1507559>. (4) computation of dissimilarity matrix of stochastic textured surface images, which was proposed by Bui and Apley (2019b) <doi:10.1016/j.csda.2019.01.019>.

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

This package provides a coherent interface for exploring and transforming multiple related data frames that share a common structure. Complements single-dataset inspection tools by operating across an entire collection at once. Also includes lightweight utilities for related file and folder management tasks.

Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) <doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008) <doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) <arXiv:2303.11892>. Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) <doi:10.1111/j.1467-9892.1982.tb00349.x> or fast univariate KFS equations from Koopman and Durbin (2000) <doi:10.1111/1467-9892.00186>, and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in C++ and linked to R via RcppArmadillo'.

This package provides a fast implementation with additional experimental features for testing, monitoring and dating structural changes in (linear) regression models. strucchangeRcpp features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g. cumulative/moving sum, recursive/moving estimates) and F statistics, respectively. These methods are described in Zeileis et al. (2002) <doi:10.18637/jss.v007.i02>. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals, and their magnitude as well as the model fit can be evaluated using a variety of statistical measures.

This package implements the structural forest methodology for the heterogeneous newsvendor model. The package provides tools to prepare data, fit honest newsvendor trees and forests, and obtain point and distributional predictions for demand decisions under uncertainty.

This package provides functions and data sets for data sharpening. Nonparametric regressions are computed subject to smoothness and other kinds of penalties.