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.

Fit a spatial-temporal occupancy models using a probit formulation instead of a traditional logit model.

Read SubRip <https://sourceforge.net/projects/subrip/> subtitle files as data frames for easy text analysis or manipulation. Easily shift numeric timings and export subtitles back into valid SubRip timestamp format to sync subtitles and audio.

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 workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.

Parse Standard Schedules Information file (types 2 and 3) into a Data Frame. Can also expand schedules into flights.

This package provides a set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.

Implementations of classical and machine learning models for survival analysis, including deep neural networks via keras and tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from Python via reticulate <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using Rcpp <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the Python package pycox <https://github.com/havakv/pycox>.

This package provides a fast implementation of the weighted information similarity aggregation (WISE) test for detecting serial dependence, particularly suited for high-dimensional and non-Euclidean time series. Includes functions for constructing similarity matrices and conducting hypothesis testing. Users can use different similarity measures and define their own weighting schemes. For more details see Q Zhu, M Liu, Y Han, D Zhou (2025) <doi:10.48550/arXiv.2509.05678>.

Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. <arXiv:1811.06782>, using a grid of binary variables to estimate the spread of a disease on the grid over the years.

This package provides a set of user interface components to create outstanding shiny apps <https://shiny.posit.co/>, with the power of React JavaScript <https://react.dev/>. Seamlessly support dark and light themes, customize CSS with tailwind <https://tailwindcss.com/>.

Computes a simple blinding index for randomized controlled trials introduced in Petroff, Bacak, Dagres, Dilk, Wachter: A simple blinding index for randomized controlled trials. Contemp Clin Trials Commun. 2024 Nov 26;42:101393. <doi:10.1016/j.conctc.2024.101393>. PMID: 39686958.

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.

Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies, following a general simulation workflow that closely aligns with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>. Also includes functions for calculating bootstrap confidence intervals (including normal, basic, studentized, percentile, bias-corrected, and bias-corrected-and-accelerated) with tidy output, as well as for extrapolating confidence interval coverage rates and hypothesis test rejection rates following techniques suggested by Boos and Zhang (2000) <DOI:10.1080/01621459.2000.10474226>.

Catch advice for data-limited vertebrate and invertebrate fisheries managed by harvest slot limits using the SlotLim harvest control rule. The package accompanies the manuscript "SlotLim: catch advice for data-limited vertebrate and invertebrate fisheries managed by harvest slot limits" (Pritchard et al., in prep). Minimum data requirements: at least two consecutive years of catch data, lengthâ frequency distributions, and biomass or abundance indices (all from fishery-dependent sources); species-specific growth rate parameters (either von Bertalanffy, Gompertz, or Schnute); and either the natural mortality rate ('M') or the maximum observed age ('tmax'), from which M is estimated. The following functions have optional plotting capabilities that require ggplot2 installed: prop_target(), TBA(), SAM(), catch_advice(), catch_adjust(), and slotlim_once().

Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Denti (2024) <doi:10.1214/24-BA1458>.

This package provides functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices.

Validate data.frames against schemas to ensure that data matches expectations. Define schemas using tidyselect and predicate functions for type consistency, nullability, and more. Schema failure messages can be tailored for non-technical users and are ideal for user-facing applications such as in shiny or plumber'.

This package provides a systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially expressed subpathways based on cultured human cells treated with drugs in the cMap (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.

These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from fleet segment to metier level.

Efficient implementation of sparse group lasso with optional bound constraints on the coefficients; see <doi:10.18637/jss.v110.i06>. It supports the use of a sparse design matrix as well as returning coefficient estimates in a sparse matrix. Furthermore, it correctly calculates the degrees of freedom to allow for information criteria rather than cross-validation with very large data. Finally, the interface to compiled code avoids unnecessary copies and allows for the use of long integers.

Efficiently estimate shape parameters of periodic time series imagery with which a statistical seasonal trend analysis (STA) is subsequently performed. STA output can be exported in conventional raster formats. Methods to visualize STA output are also implemented as well as the calculation of additional basic statistics. STA is based on (R. Eastman, F. Sangermano, B. Ghimire, H. Zhu, H. Chen, N. Neeti, Y. Cai, E. Machado and S. Crema, 2009) <doi:10.1080/01431160902755338>.

This package provides a programmatic interface to <http://sp2000.org.cn>, re-written based on an accompanying Species 2000 API. Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global <http://catalogueoflife.org>, China animal scientific database <http://zoology.especies.cn> and catalogue of life Taiwan <https://taibnet.sinica.edu.tw/home_eng.php>. The development of SP2000 package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project <C176240405> and Yunnan University's Research Innovation Fund for Graduate Students <2019227>.

An efficient implementation of SCCI using Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data.