Performance analysis workflow that combines the power of the R language (and the tidyverse realm) and many auxiliary tools to provide a consistent, flexible, extensible, fast, and versatile framework for the performance analysis of task-based applications that run on top of the StarPU runtime (with its MPI (Message Passing Interface) layer for multi-node support). Its goal is to provide a fruitful prototypical environment to conduct performance analysis hypothesis-checking for task-based applications that run on heterogeneous (multi-GPU, multi-core) multi-node HPC (High-performance computing) platforms.

Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an RStudio gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.

Calculates and plots the SiZer map for scatterplot data. A SiZer map is a way of examining when the p-th derivative of a scatterplot-smoother is significantly negative, possibly zero or significantly positive across a range of smoothing bandwidths.

Analysis of spatial relationships between cell types in spatial transcriptomics data. Spatial proximity is a critical factor in cell-cell communication. The package calculates nearest neighbor distances between specified cell types and provides visualization tools to explore spatial patterns. Applications include studying cell-cell interactions, immune microenvironment characterization, and spatial organization of tissues.

Strength training prescription using percent-based approach requires numerous computations and assumptions. STMr package allow users to estimate individual reps-max relationships, implement various progression tables, and create numerous set and rep schemes. The STMr package is originally created as a tool to help writing JovanoviÄ M. (2020) Strength Training Manual <ISBN:979-8604459898>.

Lexicons and tools to perform sentiment analysis on Italian texts. Lexicons included: Sentix 3.0, MAL, ElIta VAD and basic emotions (Plutchik's wheel of emotions). For more details about the lexicons, see Basile & Nissim (2013), "Sentiment Analysis on Italian Tweets", <https://aclanthology.org/W13-1614/>; Vassallo et al. (2019), "The Tenuousness of Lemmatization in Lexicon-based Sentiment Analysis", <https://aclanthology.org/2019.clicit-1.79/>; Di Palma (2024), "ELIta: A New Italian Language Resource for Emotion Analysis", <https://aclanthology.org/2024.clicit-1.36/>.

Print function signatures and find overly complicated code.

Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells. SuperCell uses velocyto.R <doi:10.1038/s41586-018-0414-6> <https://github.com/velocyto-team/velocyto.R> for RNA velocity and WeightedCluster <doi:10.12682/lives.2296-1658.2013.24> <https://mephisto.unige.ch/weightedcluster/> for weighted clustering on metacells. We also recommend installing scater Bioconductor package <doi:10.18129/B9.bioc.scater> <https://bioconductor.org/packages/release/bioc/html/scater.html>.

RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. sglasso package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.

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.

Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition. The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468). The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al. The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al. The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test. The other functions in the package are internal functions.

Model age schedules of mortality, nqx, suitable for a life table. This package implements the SVD-Comp mortality model indexed by either child or child/adult mortality. Given input value(s) of either 5q0 or (5q0, 45q15), the qx() function generates single-year 1qx or 5-year 5qx conditional age-specific probabilities of dying. See Clark (2016) <doi:10.48550/arXiv.1612.01408> and Clark (2019) <doi:10.1007/s13524-019-00785-3>.

This package provides functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics.

Datasets and functions for the book "Statistiques pour lâ économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). The first chapter of the book is dedicated to an introduction to statistics and their world. The second chapter deals with univariate exploratory statistics and graphics. The third chapter deals with bivariate and multivariate exploratory statistics and graphics. The fourth chapter is dedicated to data exploration with Principal Component Analysis. The fifth chapter is dedicated to data exploration with Correspondance Analysis. The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. The seventh chapter is dedicated to data exploration with automatic clustering. The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions. The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests. The tenth chapter is dedicated to an Gaussian linear model. The eleventh chapter is dedicated to an introduction to time series. The twelfth chapter is dedicated to an introduction to probit and logit models. Various example datasets are shipped with the package as well as some new functions.

By calling the SimpleTex <https://simpletex.cn/> open API implements text and mathematical formula recognition on the image, and the output formula can be used directly with Markdown and LaTeX'.

Create short sprint acceleration-velocity (AVP) and force-velocity (FVP) profiles and predict kinematic and kinetic variables using the timing-gate split times, laser or radar gun data, tether devices data, as well as the data provided by the GPS and LPS monitoring systems. The modeling method utilized in this package is based on the works of Furusawa K, Hill AV, Parkinson JL (1927) <doi: 10.1098/rspb.1927.0035>, Greene PR. (1986) <doi: 10.1016/0025-5564(86)90063-5>, Chelly SM, Denis C. (2001) <doi: 10.1097/00005768-200102000-00024>, Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) <doi: 10.1519/JSC.0000000000002081>, Samozino P. (2018) <doi: 10.1007/978-3-319-05633-3_11>, Samozino P. and Peyrot N., et al (2022) <doi: 10.1111/sms.14097>, Clavel, P., et al (2023) <doi: 10.1016/j.jbiomech.2023.111602>, Jovanovic M. (2023) <doi: 10.1080/10255842.2023.2170713>, and Jovanovic M., et al (2024) <doi: 10.3390/s24092894>.

Scripts and exercises that use card shuffling to teach confidence interval comparisons for different estimators.

This package implements S3 classes for storing dates and date-times based on the Jalali calendar. The main design goal of shide is consistency with base R's Date and POSIXct'. It provide features such as: date-time parsing, formatting and arithmetic.

This package provides tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>, extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.

Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) <arXiv:2204.10426>.

Modern classes for tracking and movement data, building on sf spatial infrastructure, and early theoretical work from Turchin (1998, ISBN: 9780878938476), and Calenge et al. (2009) <doi:10.1016/j.ecoinf.2008.10.002>. Tracking data are series of locations with at least 2-dimensional spatial coordinates (x,y), a time index (t), and individual identification (id) of the object being monitored; movement data are made of trajectories, i.e. the line representation of the path, composed by steps (the straight-line segments connecting successive locations). sftrack is designed to handle movement of both living organisms and inanimate objects.

Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported.

This package provides a survival prediction framework using spatially adjusted protein summaries from spatial proteomics data, including imaging mass cytometry data. Cell-level protein intensities are modeled with spatial spline regression to estimate spatially adjusted mean expression and residual variance. Methodological details are described in Ahn et al. (2026) <doi:10.64898/2026.06.08.730964>.

R interface to Apache Spark, a fast and general engine for big data processing, see <https://spark.apache.org/>. This package supports connecting to local and remote Apache Spark clusters, provides a dplyr compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.