This package provides interface to sparsepp - fast, memory efficient hash map. It is derived from Google's excellent sparsehash implementation. We believe sparsepp provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's dense_hash_map is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance).

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

Singular spectrum analysis (SSA) decomposes a time series into interpretable components like trends, oscillations, and noise without strict distributional and structural assumptions. For method details see Golyandina N, Zhigljavsky A (2013). <doi:10.1007/978-3-642-34913-3>.

It provides cumulative distribution function (CDF), quantile, p-value, statistical power calculator and random number generator for a collection of group-testing procedures, including the Higher Criticism tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests, the one-sided phi-divergence tests, etc. The input are a group of p-values. The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of signal detection, the null hypothesis means no signals. In the context of the goodness-of-fit testing, which contrasts a group of i.i.d. random variables to a given continuous distribution, the input p-values can be obtained by the CDF transformation. The null hypothesis means that these random variables follow the given distribution. For reference, see [1]Hong Zhang, Jiashun Jin and Zheyang Wu. "Distributions and power of optimal signal-detection statistics in finite case", IEEE Transactions on Signal Processing (2020) 68, 1021-1033; [2] Hong Zhang and Zheyang Wu. "The general goodness-of-fit tests for correlated data", Computational Statistics & Data Analysis (2022) 167, 107379.

Supports eigenvalue block-averaging p-values (Foldnes, Grønneberg, 2018) <doi:10.1080/10705511.2017.1373021>, penalized eigenvalue block-averaging p-values (Foldnes, Moss, Grønneberg, 2024) <doi:10.1080/10705511.2024.2372028>, penalized regression p-values (Foldnes, Moss, Grønneberg, 2024) <doi:10.1080/10705511.2024.2372028>, as well as traditional p-values such as Satorra-Bentler. All p-values can be calculated using unbiased or biased gamma estimates (Du, Bentler, 2022) <doi:10.1080/10705511.2022.2063870> and two choices of chi square statistics.

This package provides a general-purpose implementation of synthetic control methods that accounts for potential spillover effects between units. Based on the methodology of Cao and Dowd (2019) <doi:10.48550/arXiv.1902.07343> "Estimation and Inference for Synthetic Control Methods with Spillover Effects".

Offers Bayesian semiparametric density estimation and tail-index estimation for heavy tailed data, by using a parametric, tail-respecting transformation of the data to the unit interval and then modeling the transformed data with a purely nonparametric logistic Gaussian process density prior. Based on Tokdar et al. (2022) <doi:10.1080/01621459.2022.2104727>.

This package provides tools for setting up ("design"), conducting, and evaluating large-scale simulation studies with graphics and tables, including parallel computations.

Some R functions, such as optim(), require a function its gradient passed as separate arguments. When these are expensive to calculate it may be much faster to calculate the function (fn) and gradient (gr) together since they often share many calculations (chain rule). This package allows the user to pass in a single function that returns both the function and gradient, then splits (hence splitfngr') them so the results can be accessed separately. The functions provided allow this to be done with any number of functions/values, not just for functions and gradients.

Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>), it also allows the user to create embedding vectors for text which can be used in other analyses. GPU acceleration is supported on Windows and Linux.

This package provides functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.

This package provides standardized effect decomposition (direct, indirect, and total effects) for three major structural equation modeling frameworks: lavaan', piecewiseSEM', and plspm'. Automatically handles zero-effect variables, generates publication-ready ggplot2 visualizations, and returns both wide-format and long-format effect tables. Supports effect filtering, multi-model object inputs, and customizable visualization parameters. For a general overview of the methods used in this package, see Rosseel (2012) <doi:10.18637/jss.v048.i02> and Lefcheck (2016) <doi:10.1111/2041-210X.12512>.

Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.

This package performs the change-point detection in regression coefficients of linear model by partitioning the regression coefficients into two classes of smoothness. The change-point and the regression coefficients are jointly estimated.

Testing the mediation effect of multiple SNPs on an outcome through a mediator.

This package provides deep learning models for right-censored survival data using the torch backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.

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.

Analysis of seed germination data using the physiological time modelling approach. Includes functions to fit hydrotime and thermal-time models with the traditional approaches of Bradford (1990) <doi:10.1104/pp.94.2.840> and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. Allows to fit models to grouped datasets, i.e. datasets containing multiple species, seedlots or experiments.

Detection of item-wise Differential Item Functioning (DIF) in fitted mirt', multipleGroup or bfactor models using score-based structural change tests. Under the hood the sctest() function from the strucchange package is used.

Simple and quick method of exporting the most often used survival analysis results to an Excel sheet.

Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" <arXiv:1808.09262>.

Analyse species-habitat associations in R. Therefore, information about the location of the species (as a point pattern) is needed together with environmental conditions (as a categorical raster). To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.

This package provides a graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) <doi:10.1186/s13059-023-03062-0>. The core algorithm is written in C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base R matrices, the Matrix library, igraph graphs, or any data that can be coerced into a matrix.

This package provides a covariance estimator for multivariate normal data that is sparse and positive definite. Implements the majorize-minimize algorithm described in Bien, J., and Tibshirani, R. (2011), "Sparse Estimation of a Covariance Matrix," Biometrika. 98(4). 807--820.