Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting, bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.

Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.

Soft-margin support vector machines (SVMs) are a common class of classification models. The training of SVMs usually requires that the data be available all at once in a single batch, however the Stochastic majorization-minimization (SMM) algorithm framework allows for the training of SVMs on streamed data instead Nguyen, Jones & McLachlan(2018)<doi:10.1007/s42081-018-0001-y>. This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss.

Optimal hard thresholding of singular values. The procedure adaptively estimates the best singular value threshold under unknown noise characteristics. The threshold chosen by ScreeNOT is optimal (asymptotically, in the sense of minimum Frobenius error) under the the so-called "Spiked model" of a low-rank matrix observed in additive noise. In contrast to previous works, the noise is not assumed to be i.i.d. or white; it can have an essentially arbitrary and unknown correlation structure, across either rows, columns or both. ScreeNOT is proposed to practitioners as a mathematically solid alternative to Cattell's ever-popular but vague Scree Plot heuristic from 1966. If you use this package, please cite our paper: David L. Donoho, Matan Gavish and Elad Romanov (2023). "ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise." Annals of Statistics, 2023 (To appear). <arXiv:2009.12297>.

Quality control charts for survival outcomes. Allows users to construct the Continuous Time Generalized Rapid Response CUSUM (CGR-CUSUM) <doi:10.1093/biostatistics/kxac041>, the Biswas & Kalbfleisch (2008) <doi:10.1002/sim.3216> CUSUM, the Bernoulli CUSUM and the risk-adjusted funnel plot for survival data <doi:10.1002/sim.1970>. These procedures can be used to monitor survival processes for a change in the failure rate.

This package contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.

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.

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.

RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.

High dimensional time to events data analysis with variable selection technique. Currently support LASSO, clustering and Bonferroni's correction.

Computation of sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation). The level of sparsity of the portfolios, i.e., the number of selected assets, is controlled through a regularization parameter. Different tracking measures are available, namely, the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking error (HETE), and Huber downside risk (HDR). See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>.

Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) <doi:10.5194/hess-25-4947-2021>.

Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees. Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options. As it's built with C5.0 trees, it works only for classification (not for regression).

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 provides functions and classes for spatial resampling to use with the rsample package, such as spatial cross-validation (Brenning, 2012) <doi:10.1109/IGARSS.2012.6352393>. The scope of rsample and spatialsample is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by spatialsample do not contain much overhead in memory.

Classical methods for combining summary data from genome-wide association studies (GWAS) only use marginal genetic effects and power can be compromised in the presence of heterogeneity. subgxe is a R package that implements p-value assisted subset testing for association (pASTA), a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA generalizes association analysis based on subsets by incorporating gene-environment interactions into the testing procedure.

Bayesian clustering of spatial regions with similar functional shapes using spanning trees and latent Gaussian models. The method enforces spatial contiguity within clusters and supports a wide range of latent Gaussian models, including non-Gaussian likelihoods, via the R-INLA framework. The algorithm is based on Zhong, R., Chacón-Montalván, E. A., and Moraga, P. (2024) <doi:10.48550/arXiv.2407.12633>, extending the approach of Zhang, B., Sang, H., Luo, Z. T., and Huang, H. (2023) <doi:10.1214/22-AOAS1643>. The package includes tools for model fitting, convergence diagnostics, visualization, and summarization of clustering results.

This package provides a framework for simulating spatially explicit genomic data which leverages real cartographic information for programmatic and visual encoding of spatiotemporal population dynamics on real geographic landscapes. Population genetic models are then automatically executed by the SLiM software by Haller et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom built-in simulation SLiM script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the SLiM built-in back-end script, or using an efficient coalescent population genetics simulator msprime by Baumdicker et al. (2022) <doi:10.1093/genetics/iyab229> with a custom-built Python script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the Python module tskit by Kelleher et al. (2019) <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis.

Tidies up the forecasting modeling and prediction work flow, extends the broom package with sw_tidy', sw_glance', sw_augment', and sw_tidy_decomp functions for various forecasting models, and enables converting forecast objects to "tidy" data frames with sw_sweep'.

Interface to interact with the modelling framework SIMPLACE and to parse the results of simulations.

Mixed DNA profiles can be sampled according to models for probabilistic genotyping. Peak height variability is modelled using a log normal distribution or a gamma distribution. Sample contributors may be related according to a pedigree.

This package provides a S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio). The resource can be a tidy file to be downloaded from the object store, or a data lake (such as Delta Lake) Parquet file to be read by Apache Spark.

Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2018 <doi:10.1002/env.2481>).

This is a shape preserving spline <doi:10.1137/0720057> which is guaranteed to be monotonic and concave or convex if the data is monotonic and concave or convex. It does not use any optimisation and is therefore quick and smoothly converges to a fixed point in economic dynamics problems including value function iteration. It also automatically gives the first two derivatives of the spline and options for determining behaviour when evaluated outside the interpolation domain.