Characterization of a mid-summer drought (MSD) with precipitation based statistics. The MSD is a phenomenon of decreased rainfall during a typical rainy season. It is a feature of rainfall in much of Central America and is also found in other locations, typically those with a Mediterranean climate. Details on the metrics are in Maurer et al. (2022) <doi:10.5194/hess-26-1425-2022>.

This package implements a novel density-based approach for estimating unknown parameters, distribution visualisations and meta-analyses of quantiles and ther functions. A detailed vignettes with example datasets and code to prepare data and analyses is available at <https://bookdown.org/a2delivera/metaquant/>. The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, <doi:10.48550/arXiv.2411.10971>).

This package provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) <DOI:10.1111/j.1467-9868.2012.01041.x>, the centered autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>, and the Bayesian spatial filtering model of Hughes (2017) <arXiv:1706.04651>.

Unified methods for comparing two independent or paired proportions. Provides classical, exact, score-based, non-inferiority, equivalence, effect-size, confidence-interval, and stratified procedures with standardized publication-ready output. Farrington-Manning inference is supported through established score-based methods described by Farrington and Manning (1990) <doi:10.2307/2532443> and implemented through ratesci', while additional established methods are provided through DescTools and base R.

This package provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.

Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>. Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.

This package provides tools for optimizing sequencing resource allocation and estimating pathogen lineage prevalence under real-world genomic surveillance conditions. Implements constrained allocation optimization for limited sequencing capacity across multiple regions and sample sources. Includes Horvitz-Thompson and post-stratified estimators that account for unequal sequencing rates, delay-adjusted nowcasting for right-censored reporting data, and combined design-weighted delay-corrected inference with uncertainty propagation.

Import data from the STATcube REST API or from the open data portal of Statistics Austria. This package includes a client for API requests as well as parsing utilities for data which originates from STATcube'. Documentation about STATcubeR is provided by several vignettes included in the package as well as on the public pkgdown page at <https://statistikat.github.io/STATcubeR/>.

This package provides a function that behaves nearly as base::source() but implements a caching mechanism on disk, project based. It allows to quasi source() R scripts that gather data but can fail or consume to much time to respond even if nothing new is expected. It comes with tools to check and execute on demand or when cache is invalid the script.

This package implements a Bayesian hierarchical model designed to identify skips in mobile menstrual cycle self-tracking on mobile apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>.

This package provides a programmatic interface in R for the US Department of Transportation (DOT) National Highway Transportation Safety Administration (NHTSA) vehicle identification number (VIN) API, located at <https://vpic.nhtsa.dot.gov/api/>. The API can decode up to 50 vehicle identification numbers in one call, and provides manufacturer information about the vehicles, including make, model, model year, and gross vehicle weight rating (GVWR).

An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted for climate downscaling according to D'Onofrio et al. (2018) <doi:10.1175/JHM-D-13-096.1> and for complex topography as in Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field.

This package provides functions to correct biased transition and fertility estimates in population projection matrices caused by small sample sizes. Small or short-term studies frequently produce structural zeros (biologically possible transitions never observed) and structural ones (transitions estimated at 100% survival, stasis, or mortality that are biologically implausible). Both distort matrix structure and bias estimates of population growth. Implements a multinomial-Dirichlet Bayesian prior for transition probabilities and a Gamma-Poisson prior for reproduction, allowing analysts to incorporate prior biological knowledge and regularise estimates from rare or unobserved events. Includes functions to compute marginal posterior credible intervals for all transition probabilities (transition_CrI()), visualise those intervals as point-range plots (plot_transition_CrI()), and display the full posterior beta density for each matrix entry (plot_transition_density()). Methods are described in Tremblay et al. (2021) <doi:10.1016/j.ecolmodel.2021.109526>.

Rodi is a dependency injection framework for Python applications.

Its features include

• Type resolution by signature types annotations.

• Type resolution by class annotations.

• Type resolution by names and aliases.

• Build graph of objects without the need for source code changes.

• Minimum overhead to obtain services, once the objects graph is built.

• Support for singleton, transient, and scoped services.

epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.

scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters.

This package provides statistical tools to analyze heterogeneous effects of rare variants within genes that are associated with multiple traits. The package implements methods for assessing pleiotropic effects and identifying allelic heterogeneity, which can be useful in large-scale genetic studies. Methods include likelihood-based statistical tests to assess these effects. For more details, see Lu et al. (2024) <doi:10.1101/2024.10.01.614806>.

This package provides a data package with 2 main package variables: signature and etiology'. The signature variable contains the latest mutational signature profiles released on COSMIC <https://cancer.sanger.ac.uk/signatures/> for 3 mutation types: * Single base substitutions in the context of preceding and following bases, * Doublet base substitutions, and * Small insertions and deletions. cosmicsig stands for COSMIC signatures. Please run ?'cosmicsig for more information.

An implementation of the decimated two-dimensional complex dual-tree wavelet transform as described in Kingsbury (1999) <doi:10.1098/rsta.1999.0447> and Selesnick et al. (2005) <doi:10.1109/MSP.2005.1550194>. Also includes the undecimated version and spectral bias correction described in Nelson et al. (2018) <doi:10.1007/s11222-017-9784-0>. The code is partly based on the dtcwt Python library.

Density ratio estimation. The estimated density ratio function can be used in many applications such as anomaly detection, change-point detection, covariate shift adaptation. The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>), RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>), and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).

This package provides a plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.

This package provides tools for fitting periodic coefficients regression models to data where periodicity plays a crucial role. It allows users to model and analyze relationships between variables that exhibit cyclical or seasonal patterns, offering functions for estimating parameters and testing the periodicity of coefficients in linear regression models. For simple periodic coefficient regression model see Regui et al. (2024) <doi:10.1080/03610918.2024.2314662>.

This package implements the Staggered Synthetic Control (SSC) method for estimating treatment effects in panel data with staggered adoption, as proposed by Cao, Lu, and Wu (2020) <doi:10.48550/arXiv.1912.06320>. Constructs synthetic control weights via constrained quadratic programming, estimates heterogeneous treatment effects and event-time average treatment effects on the treated (ATT), and provides placebo-in-time confidence intervals and p-values.

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.