Evaluating probabilistic forecasts via proper scoring rules. scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families. Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score. For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.

This package provides functions to compute standardized differences for numeric, binary, and categorical variables on Apache Spark DataFrames using sparklyr'. The implementation mirrors the methods used in the stddiff package but operates on distributed data. See Zhicheng Du, Yuantao Hao (2022) <doi:10.32614/CRAN.package.stddiff> for reference.

Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.

Regularized version of partial least square approaches providing sparse, group, and sparse group versions of partial least square regression models (Liquet, B., Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>). Version of PLS Discriminant analysis is also provided.

This package provides a collection of functions for statistical and multivariate analysis of surface-related data, with a focus on antimicrobial activity and omniphobicity. Designed to support materials scientists and researchers in exploring structureâ function relationships in surface-engineered materials through reproducible and interpretable workflows. For more details, see Li et al. (2021) <doi:10.1002/advs.202100368>, and Kwon et al. (2020) <doi:10.3390/polym12081826>.

Facilitates basic and equation-based analyses of some important soil properties related to soil chemical environment and nutrient availability to plants. Freundlich H (1907). <doi:10.1515/zpch-1907-5723>. Datta SP, Bhadoria PBS (1999). <doi:10.1002%2F%28SICI%291522-2624%28199903%29162%3A2%3C183%3A%3AAID-JPLN183%3E3.0.CO%3B2-A>."Boron adsorption and desorption in some acid soils of West Bengal, India". Langmuir I (1918). <doi:10.1021/ja02242a004> "The adsorption of gases on plane surfaces of glass, mica, and platinum". Khasawneh FE (1971). <doi:10.2136/sssaj1971.03615995003500030029x> "Solution ion activity and plant growth".

This package provides a user-friendly framework for estimating a wide variety of cross-sectional and panel stochastic frontier models. Suitable for a broad range of applications, the implementation offers extensive flexibility in specification and estimation techniques.

The goal of snpsettest is to provide simple tools that perform set-based association tests (e.g., gene-based association tests) using GWAS (genome-wide association study) summary statistics. A set-based association test in this package is based on the statistical model described in VEGAS (versatile gene-based association study), which combines the effects of a set of SNPs accounting for linkage disequilibrium between markers. This package uses a different approach from the original VEGAS implementation to compute set-level p values more efficiently, as described in <https://github.com/HimesGroup/snpsettest/wiki/Statistical-test-in-snpsettest>.

Develop outstanding shiny apps for iOS and Android as well as beautiful shiny gadgets. shinyMobile is built on top of the latest Framework7 template <https://framework7.io>. Discover 14 new input widgets (sliders, vertical sliders, stepper, grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) combined with the power of server-side notifications such as alerts, modals, toasts, action sheets, sheets (and more) as well as 3 layouts (single, tabs and split).

Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.

Draw syntenic relationships between genome assemblies. There are 3 functions which take a tab delimited file containing alignment data for syntenic blocks between genomes to produce either a linear alignment plot, an evolution highway style plot, or a painted ideogram representing syntenic relationships. There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format to the required data structure.

This package provides a simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.

This package creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. For a description of AdaBoost, see Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included.

This package provides a framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by spacyr'. For further reference, see: Stuhler (2022) <doi: 10.1177/00491241221099551>.

This package provides a framework for visualizing and exploring results of a Management Strategy Evaluation (MSE). The publication quality figures and tables can be developed directly from the R console, or interactively explored with the Slick App. For more details, see the `Slick` website <https://slick.bluematterscience.com>.

Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing <doi:10.1109/TSP.2016.2605073>.

The goal of SAFEPG is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment.

Utility functions that help with common base-R problems relating to lists. Lists in base-R are very flexible. This package provides functions to quickly and easily characterize types of lists. That is, to identify if all elements in a list are null, data.frames, lists, or fully named lists. Other functionality is provided for the handling of lists, such as the easy splitting of lists into equally sized groups, and the unnesting of data.frames within fully named lists.

Multiple imputation of missing data in a dataset using MICT or MICT-timing methods. The core idea of the algorithms is to fill gaps of missing data, which is the typical form of missing data in a longitudinal setting, recursively from their edges. Prediction is based on either a multinomial or random forest regression model. Covariates and time-dependent covariates can be included in the model.

This package provides a widget for shiny apps to handle schedule expression input, using the cron-expression-input JavaScript component. Note that this does not edit the crontab file, it is just an input element for the schedules. See <https://github.com/DatalabFabriek/shinycroneditor/blob/main/inst/examples/shiny-app.R> for an example implementation.

The computation of a seasonal index is a fundamental step in time-series forecasting when the data exhibits seasonality. Specifically, a seasonal index quantifies â for each season (e.g. month, quarter, week) â the relative magnitude of the seasonal effect compared to the overall average level of the series. This package has been developed to compute seasonal index for time series data and it also seasonalise and desesaonalise the time series data.

This package provides a unified framework for detecting spatially variable genes (SVGs) in spatial transcriptomics data. This package integrates multiple state-of-the-art SVG detection methods including MERINGUE (Moran's I based spatial autocorrelation), Giotto binSpect (binary spatial enrichment test), SPARK-X (non-parametric kernel-based test), and nnSVG (nearest-neighbor Gaussian processes). Each method is implemented with optimized performance through vectorization, parallelization, and C++ acceleration where applicable. Methods are described in Miller et al. (2021) <doi:10.1101/gr.271288.120>, Dries et al. (2021) <doi:10.1186/s13059-021-02286-2>, Zhu et al. (2021) <doi:10.1186/s13059-021-02404-0>, and Weber et al. (2023) <doi:10.1038/s41467-023-39748-z>.

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 a pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata.