This package provides functions for fitting discrete distribution models to count data. Included are the Poisson, the negative binomial, the Poisson-inverse gaussian and, most importantly, a new implementation of the Poisson-beta distribution (density, distribution and quantile functions, and random number generator) together with a needed new implementation of Kummer's function (also: confluent hypergeometric function of the first kind). Three different implementations of the Gillespie algorithm allow data simulation based on the basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for four variants of each of the three aforementioned distributions are also available. The variants include one population and two population mixtures, both with and without zero-inflation. The package depends on the MPFR libraries (<https://www.mpfr.org/>) which need to be installed separately (see description at <https://github.com/fuchslab/scModels>). This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on bioRxiv.

This package provides functions to run and assist four different similarity measures. The similarity measures included are: longest common subsequence (LCSS), Frechet distance, edit distance and dynamic time warping (DTW). Each of these similarity measures can be calculated from two n-dimensional trajectories, both in matrix form.

This package provides ggplot2 graphics for analysing time series data. It aims to fit into the tidyverse and grammar of graphics framework for handling temporal data.

This package provides functions for stratified sampling and assigning custom labels to data, ensuring randomness within groups. The package supports various sampling methods such as stratified, cluster, and systematic sampling. It allows users to apply transformations and customize the sampling process. This package can be useful for statistical analysis and data preparation tasks.

Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) <doi:10.1111/biom.13457>.

Contains, as a main contribution, a function to fit a regression model with possibly right, left or interval censored observations and with the error distribution expressed as a mixture of G-splines. Core part of the computation is done in compiled C++ written using the Scythe Statistical Library Version 0.3.

This package implements the SoftBart model of described by Linero and Yang (2018) <doi:10.1111/rssb.12293>, with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the BayesTree package.

To determine sample size or power for case-control studies to be analyzed using logistic regression.

This package provides a collection of forecast verification routines developed for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.

Discovery of spatial patterns with Hidden Markov Random Field. This package is designed for spatial transcriptomic data and single molecule fluorescent in situ hybridization (FISH) data such as sequential fluorescence in situ hybridization (seqFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH). The methods implemented in this package are described in Zhu et al. (2018) <doi:10.1038/nbt.4260>.

Offers a comprehensive solution for managing empty states in Shiny applications. It provides tools to create both default and customizable components for scenarios where data is absent or doesn't match user-defined filters. The package prioritizes user experience, ensuring clarity and consistency even when data is not available to display.

This package provides a simple HTTP server allows to connect GUI clients to R.

This package provides a programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility ('GBIF'), iNaturalist', eBird', Integrated Digitized Biocollections ('iDigBio'), VertNet', Ocean Biogeographic Information System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes functionality for retrieving species occurrence data, and combining those data.

This package provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.

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.

This package provides a meta-package that loads the complete sitrep ecosystem for applied epidemiology analysis. This package provides report templates and automatically loads companion packages, including epitabulate (for epidemiological tables), epidict (for data dictionaries), epikit (for epidemiological utilities), and apyramid (for age-sex pyramids). Simply load sitrep to access all functions from the ecosystem.

This package implements the Bayesian model selection method with suspected latent grouping factor methodology of Metzger and Franck (2020), <doi:10.1080/00401706.2020.1739561>. SLGF detects latent heteroscedasticity or group-based regression effects based on the levels of a user-specified categorical predictor.

Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book.

Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) <doi: 10.1007/s10985-021-09544-2> and Luo et al. (2023) <doi:10.1177/09622802231181471>.

The StockDistFit package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) <https://www.jstage.jst.go.jp/article/jappstat/37/1/37_1_1/_pdf/-char/ja> and Benth et al. (2008) <https://books.google.co.ke/books?hl=en&lr=&id=MHNpDQAAQBAJ&oi=fnd&pg=PR7&dq=Stochastic+modeling+of+commodity+prices+using+the+Variance+Gamma+(VG)+model.+&ots=YNIL2QmEYg&sig=XZtGU0lp4oqXHVyPZ-O8x5i7N3w&redir_esc=y#v=onepage&q&f=false>.

Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via TMB (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).

An overall test for seasonality of a given time series in addition to a set of individual seasonality tests as described by Ollech and Webel (forthcoming): An overall seasonality test. Bundesbank Discussion Paper.

The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of Sim.PLFN package are: (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that Sim.PLFN package works on the basis of FuzzyNumbers package.

The explosion of biobank data offers immediate opportunities for gene-environment (GxE) interaction studies of complex diseases because of the large sample sizes and rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in GxE assessment, especially for set-based GxE variance component (VC) tests, a widely used strategy to boost overall GxE signals and to evaluate the joint GxE effect of multiple variants from a biologically meaningful unit (e.g., gene). We present SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based GxE tests, to permit GxE VC test scalable to biobank data. SEAGLE employs modern matrix computations to achieve the same â exactâ results as the original GxE VC tests, and does not impose additional assumptions nor relies on approximations. SEAGLE can easily accommodate sample sizes in the order of 10^5, is implementable on standard laptops, and does not require specialized equipment. The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, Lin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.