This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) <doi:10.1039/c3ay41160e>. The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.'.

This package provides a collection of functions that enable easy access and updating of a database of data over time. More specifically, the package facilitates type-2 history for data-warehouses and provides a number of Quality of life improvements for working on SQL databases with R. For reference see Ralph Kimball and Margy Ross (2013, ISBN 9781118530801).

Transform a Movie into a Synthetic Picture. A frame every 10 seconds is summarized into one colour, then every generated colors are stacked together.

This package provides deep learning models for right-censored survival data using the torch backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.

This package contains statistical methods to analyze graphs, such as graph parameter estimation, model selection based on the Graph Information Criterion, statistical tests to discriminate two or more populations of graphs, correlation between graphs, and clustering of graphs. References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, Fujita et al. (2017) <doi:10.3389/fnins.2017.00066>, Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.

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.

This package implements a thresholded version of the Sliced Inverse Regression method (Li, K. C. (1991) <doi:10.2307/2290563>), which allows to do variable selection.

Estimates correlation coefficients with associated confidence limits for bivariate, partially censored survival times. Uses the iterative multiple imputation approach proposed by Schemper, Kaider, Wakounig and Heinze (2013) <doi:10.1002/sim.5874>. Provides a scatterplot function to visualize the bivariate distribution, either on the original time scale or as copula.

Stratigraphic ranges of fossil marine animal genera from Sepkoski's (2002) published compendium. No changes have been made to any taxonomic names. However, first and last appearance intervals have been updated to be consistent with stages of the International Geological Timescale. Functionality for generating a plot of Sepkoski's evolutionary fauna is also included. For specific details on the compendium see: Sepkoski, J. J. (2002). A compendium of fossil marine animal genera. Bulletins of American Paleontology, 363, pp. 1â 560 (ISBN 0-87710-450-6). Access: <https://www.biodiversitylibrary.org/item/40634#page/5/mode/1up>.

Performing Item Response Theory analysis such as parameter estimation, ability estimation, item and model fit analyse, local independence assumption, dimensionality assumption, characteristic and information curves under various models with a user friendly shiny interface.

This package provides a graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) <doi:10.1186/s13059-023-03062-0>. The core algorithm is written in C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base R matrices, the Matrix library, igraph graphs, or any data that can be coerced into a matrix.

Allows the user to estimate a vector logistic smooth transition autoregressive model via maximum log-likelihood or nonlinear least squares. It further permits to test for linearity in the multivariate framework against a vector logistic smooth transition autoregressive model with a single transition variable. The estimation method is discussed in Terasvirta and Yang (2014, <doi:10.1108/S0731-9053(2013)0000031008>). Also, realized covariances can be constructed from stock market prices or returns, as explained in Andersen et al. (2001, <doi:10.1016/S0304-405X(01)00055-1>).

Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and SoilLoss(). The rainfall erosivity factors can be calculated or estimated, and soil erodibility will be estimated by the equation extracted from the monograph. Soil loss will be estimated by the product of five factors (rainfall erosivity, soil erodibility, length and steepness slope, cover-management factor and support practice factor. In the future, additional functions can be included. This efforts to advance research in soil and water conservation, with fast and accurate results.

We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The rjags package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.

Mappings for estimated one rep max from commonly used formulas. Convenience functions for turning mass/rep/set data into useful derived quantities.

An htmlwidget of the human body that allows you to hide/show and assign colors to 79 different body parts. The human widget is an htmlwidget', so it works in Quarto documents, R Markdown documents, or any other HTML medium. It also functions as an input/output widget in a shiny app.

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.

Find non-linear formulas that fits your input data. You can systematically explore and memorize the possible formulas and it's cross-validation performance, in an incremental fashion. Three main interoperable search functions are available: 1) random.search() performs a random exploration, 2) genetic.search() employs a genetic optimization algorithm, 3) comb.search() combines best results of the first two. For more details see Tomasoni et al. (2026) <doi:10.1208/s12248-026-01232-z>.

Calculates (unconditional) post-selection confidence intervals and p-values for the coefficients of (generalized) linear models.

Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well.

An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see <arXiv:1505.01333>).

This package provides a search interface to look up terms on Google', Bing', DuckDuckGo', Startpage', Ecosia', rseek', Twitter', StackOverflow', RStudio Community', GitHub', and BitBucket'. Upon searching, a browser window will open with the aforementioned search results.

This package provides a collection of methods for the Bayesian estimation of Spatial Probit, Spatial Ordered Probit and Spatial Tobit Models. Original implementations from the works of LeSage and Pace (2009, ISBN: 1420064258) were ported and adjusted for R, as described in Wilhelm and de Matos (2013) <doi:10.32614/RJ-2013-013>.

Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.