Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. Wraps the sentencepiece library <https://github.com/google/sentencepiece> which provides a language independent tokenizer to split text in words and smaller subword units. The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) <doi:10.18653/v1/D18-2012>. Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using word2vec', as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) <http://www.lrec-conf.org/proceedings/lrec2018/pdf/1049.pdf>.

This package provides a non convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .

Adds Progressive Web App support for Shiny apps, including desktop and mobile installations.

Assigns a score projection from 0 to 1 between a given in vivo stage and each single cluster from an in vitro dataset. The score is assigned based on the the fraction of specific markers of the in vivo stage that are conserved in the in vitro clusters <https://github.com/ScialdoneLab>.

Fast Multiplication and Marginalization of Sparse Tables <doi:10.18637/jss.v111.i02>.

Compiles and displays the available data sets regarding the Italian school system, with a focus on the infrastructural aspects. Input datasets are downloaded from the web, with the aim of updating everything to real time. The functions are divided in four main modules, namely Get', to scrape raw data from the web Util', various utilities needed to process raw data Group', to aggregate data at the municipality or province level Map', to visualize the output datasets.

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>.

Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.

Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detection (Liu, Ting and Zhou <doi:10.1145/2133360.2133363>).

This package performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.

This package provides a collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.

This package provides methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices. Hyperparameter optimization can be done with a number of techniques but we recommend either Bayesian optimization or particle swarm. For using "Kriging", users need to install a version of the archived DiceOptim R package.

Permits determination of a set of optimal dynamic treatment regimes and sample size for a SMART design in the Bayesian setting with binary outcomes. Please see Artman (2020) <arXiv:2008.02341>.

This package provides a tool to interactively explore the embeddings created by dimension reduction methods such as Principal Components Analysis (PCA), Multidimensional Scaling (MDS), T-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) or any other.

Makes the React library Chakra UI usable in Shiny apps. Chakra UI components include alert dialogs, drawers (sliding panels), menus, modals, popovers, sliders, and more.

Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects allowing integration with other packages such adegenet. Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance.

This package provides wrappers for common activity patterns in simmer trajectories.

This package provides functions to parse and analyze logs generated by ShinyProxy containers. It extracts metadata from log file names, reads log contents, and computes summary statistics (such as the total number of lines and lines containing error messages), facilitating efficient monitoring and debugging of ShinyProxy deployments.

Read in SAS Data ('.sas7bdat Files) into Apache Spark from R. Apache Spark is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the spark-sas7bdat Spark package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process SAS data in parallel using Spark'. Hereby allowing to execute dplyr statements in parallel on top of SAS data.

Run SQL queries across Snowflake', Amazon Redshift', PostgreSQL', SQLite', and DuckDB from R with a single function. Optionally stream and cache large query results to a local DuckDB database for efficient work with larger-than-memory datasets.

This package provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).

Sparse redundancy analysis for high dimensional (biomedical) data. Directional multivariate analysis to express the maximum variance in the predicted data set by a linear combination of variables of the predictive data set. Implemented in a partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.

This package provides a collection of tools for clinical trial data management and analysis in research and teaching. The package is mainly collected for personal use, but any use beyond that is encouraged. This package has migrated functions from agdamsbo/daDoctoR', and new functions has been added. Version follows months and year. See NEWS/Changelog for release notes. This package includes sampled data from the TALOS trial (Kraglund et al (2018) <doi:10.1161/STROKEAHA.117.020067>). The win_prob() function is based on work by Zou et al (2022) <doi:10.1161/STROKEAHA.121.037744>. The age_calc() function is based on work by Becker (2020) <doi:10.18637/jss.v093.i02>.

Data obtained from surveys contains information not only about the survey responses, but also the survey metadata, e.g. the original survey questions and the answer options. The surveydata package makes it easy to keep track of this metadata, and to easily extract columns with specific questions.