Plots survival models from the survival package. Additionally, it plots curves of multistate models from the mstate package. Typically, a plot is drawn by the sequence survplot(), confIntArea(), survCurve() and nrAtRisk(). The separation of the plot in this 4 functions allows for great flexibility to make a custom plot for publication.

Routines for a collection of screen-and-clean type variable selection procedures, including UPS and GS.

This package provides methods for constructing and maintaining a database of presentations in R. The presentations are either ones that the user gives or gave or presentations at a particular event or event series. The package also provides a plot method for the interactive mapping of the presentations using leaflet by grouping them according to country, city, year and other presentation attributes. The markers on the map come with popups providing presentation details (title, institution, event, links to materials and events, and so on).

Regression inference for multiple populations by integrating summary-level data using stacked imputations. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2021) A synthetic data integration framework to leverage external summary-level information from heterogeneous populations <arXiv:2106.06835>.

This package provides a novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. seer package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.

Cellular population mapping (CPM) a deconvolution algorithm in which single-cell genomics is required in only one or a few samples, where in other samples of the same tissue, only bulk genomics is measured and the underlying fine resolution cellular heterogeneity is inferred.

This package provides tools for fitting self-validated ensemble models (SVEM; Lemkus et al. (2021) <doi:10.1016/j.chemolab.2021.104439>) in small-sample design-of-experiments and related workflows, using elastic net and relaxed elastic net regression via glmnet (Friedman et al. (2010) <doi:10.18637/jss.v033.i01>). Fractional random-weight bootstraps with anti-correlated validation copies are used to tune penalty paths by validation-weighted AIC/BIC. Supports Gaussian and binomial responses, deterministic expansion helpers for shared factor spaces, prediction with bootstrap uncertainty, and a random-search optimizer that respects mixture constraints and combines multiple responses via desirability functions. Also includes a permutation-based whole-model test for Gaussian SVEM fits (Karl (2024) <doi:10.1016/j.chemolab.2024.105122>). Package code was drafted with assistance from generative AI tools.

Computes the sit coefficient between two vectors x and y, possibly all paired coefficients for a matrix. The reference for the methods implemented here is Zhang, Yilin, Canyi Chen, and Liping Zhu. 2022. "Sliced Independence Test." Statistica Sinica. <doi:10.5705/ss.202021.0203>. This package incorporates the Galton peas example.

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.

Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper <doi:10.18637/jss.v097.i03>. Note that this package is modelled on the survsim package available in the Stata software (see Crowther and Lambert (2012) <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and Lambert (2013) <doi:10.1002/sim.5823>).

Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup.

This package implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered 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 SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.

The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species.

This package provides functions to speed up the exploratory analysis of simple datasets using dplyr'. Functions are provided to do the common tasks of calculating confidence intervals.

Functionality to parse server-sent events with a high-level interface that can be extended for custom applications.

Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.

Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) <doi:10.1080/01621459.1988.10478561>.

This package provides tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385>, Wilson (2025) <DOI:10.1002/mrm.30462> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

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'.

An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.

Simulation of recurrent event data for non-constant baseline hazard in the total time model with risk-free intervals and possibly a competing event. Possibility to cut the data to an interim data set. Data can be plotted. Details about the method can be found in Jahn-Eimermacher, A. et al. (2015) <doi:10.1186/s12874-015-0005-2>.

The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added.

This package provides a framework for performing discrete (share-level) simulations of investment strategies. Simulated portfolios optimize exposure to an input signal subject to constraints such as position size and factor exposure. For background see L. Chincarini and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".

Interactive visualizations of graphs created with the igraph package using a htmlwidgets wrapper for the sigma.js network visualization v2.4.0 <https://www.sigmajs.org/>, enabling to display several thousands of nodes. While several R packages have been developed to interface sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the sigmaNet package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic sigma.js visualizations by overriding the underlying JavaScript code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding sigma.js code include toggling node visibility when clicked using a node attribute and highlighting specific edges. sigma.js is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available.