This package provides the density, distribution, quantile and generation functions of some obscure probability distributions, including the doubly non-central t, F, Beta, and Eta distributions; the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares; the product of non-central chi-squares to powers; the product of doubly non-central F variables; the product of independent normals.

An English language syllable counter, plus readability score measure-er. For readability, we support Flesch Reading Ease and Flesch-Kincaid Grade Level ('Kincaid et al'. 1975) <https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>, Automated Readability Index ('Senter and Smith 1967) <https://apps.dtic.mil/sti/citations/AD0667273>, Simple Measure of Gobbledygook (McLaughlin 1969), and Coleman-Liau (Coleman and Liau 1975) <doi:10.1037/h0076540>. The package has been carefully optimized and should be very efficient, both in terms of run time performance and memory consumption. The main methods are vectorized by document, and scores for multiple documents are computed in parallel via OpenMP'.

Produce small area population estimates by fitting census data to survey data.

This package performs simulations of binary spatial raster data using the Ising model (Ising (1925) <doi:10.1007/BF02980577>; Onsager (1944) <doi:10.1103/PhysRev.65.117>). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) <doi:10.1098/rsos.231005>).

Launch a shiny application for tidymodels results. For classification or regression models, the app can be used to determine if there is lack of fit or poorly predicted points.

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>.

Interface to the Sensor Tower API <https://app.sensortower.com/api/docs/app_analysis> for mobile app analytics and market intelligence. Provides functions to retrieve app metadata, publisher information, download and revenue estimates, active user metrics, category rankings, and market trends. The package includes data processing utilities to clean and aggregate metrics across platforms, automatic app name resolution, and tools for generating professional analytics dashboards. Supports both iOS and Android app ecosystems with unified data structures for cross-platform analysis.

Uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.

Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.

An updated and extended version of spm package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

This package provides a set of tools inspired by Stata to explore data.frames ('summarize', tabulate', xtile', pctile', binscatter', elapsed quarters/month, lead/lag).

Database of genes which frequently sustain somatic mutations, but are unlikely to drive cancer.

Collection of custom input controls and user interface components for Shiny applications. Give your applications a unique and colorful style !

This package provides computational tools for estimating inverse regions and constructing the corresponding simultaneous outer and inner confidence regions. Acceptable input includes both one-dimensional and two-dimensional data for linear, logistic, functional, and spatial generalized least squares regression models. Functions are also available for constructing simultaneous confidence bands (SCBs) for these models. The definition of simultaneous confidence regions (SCRs) follows Sommerfeld et al. (2018) <doi:10.1080/01621459.2017.1341838>. Methods for estimating inverse regions, SCRs, and the nonparametric bootstrap are based on Ren et al. (2024) <doi:10.1093/jrsssc/qlae027>. Methods for constructing SCBs are described in Crainiceanu et al. (2024) <doi:10.1201/9781003278726> and Telschow et al. (2022) <doi:10.1016/j.jspi.2021.05.008>.

This package provides monthly statistics on the number of monthly air passengers at SFO airport such as operating airline, terminal, geo, etc. Data source: San Francisco data portal (DataSF) <https://data.sfgov.org/Transportation/Air-Traffic-Passenger-Statistics/rkru-6vcg>.

Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. Frequently, the operations to be performed aim to transform a flat table into a star schema. Transformations can be carried out using professional extract, transform and load tools or tools intended for data transformation for end users. With the tools mentioned, this transformation can be carried out, but it requires a lot of work. The main objective of this package is to define transformations that allow obtaining stars from flat tables easily. In addition, it includes basic data cleaning, dimension enrichment, incremental data refresh and query operations, adapted to this context.

Easily create pretty popup messages (modals) in Shiny'. A modal can contain text, images, OK/Cancel buttons, an input to get a response from the user, and many more customizable options.

This package provides a facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.

Analyze public-use micro data from the Survey of Consumer Finances. Provides tools to download prepared data files, construct replicate-weighted multiply imputed survey designs, compute descriptive statistics and model estimates, and produce plots and tables. Methods follow design-based inference for complex surveys and pooling across multiple imputations. See the package website and the code book for background.

Simulates the cultural evolution of quantitative traits of bird song. SongEvo is an individual- (agent-) based model. SongEvo is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing.

This package provides historical datasets related to John Snow's 1854 cholera outbreak study in London. Includes data on cholera cases, water pump locations, and the street layout, enabling analysis and visualisation of the outbreak.

Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.

Used to construct the URLs and parameters of Socrata Open Data API <https://dev.socrata.com> calls, using the API's SoQL parameter format. Has method-chained and sensical syntax. Plays well with pipes.

This package implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.