Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.

EPE's (Empresa de Pesquisa Energética) 4MD (Modelo de Mercado da Micro e Minigeração Distribuà da - Micro and Mini Distributed Generation Market Model) model to forecast the adoption of Distributed Generation. Given the user's assumptions, it is possible to estimate how many consumer units will have distributed generation in Brazil over the next 10 years, for example. In addition, it is possible to estimate the installed capacity, the amount of investments that will be made in the country and the monthly energy contribution of this type of generation. <https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-689/topico-639/NT_Metodologia_4MD_PDE_2032_VF.pdf>.

Runs a Shiny App in the local machine for basic statistical and graphical analyses. The point-and-click interface of Shiny App enables obtaining the same analysis outputs (e.g., plots and tables) more quickly, as compared with typing the required code in R, especially for users without much experience or expertise with coding. Examples of possible analyses include tabulating descriptive statistics for a variable, creating histograms by experimental groups, and creating a scatter plot and calculating the correlation between two variables.

Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EMâ s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.

This package provides a novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.

The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge <https://www.synapse.org/ProstateCancerChallenge> presented in Guinney J, Wang T, Laajala TD, et al. (2017) <doi:10.1016/S1470-2045(16)30560-5> is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects.

This is an R package implementing the epidemic volatility index (EVI), as discussed by Kostoulas et. al. (2021) and variations by Pateras et. al. (2023). EVI is a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold.

Ever read or wrote source files containing sectioning comments? If these comments are markdown style section comments, you can excerpt them and set a table of contents using the python package excerpts (<https://pypi.org/project/excerpts/>).

Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages BDgraph', bgms and BGGM', as well as network comparison fitted using the bgms and BBGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for BDgraph and bgms it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.

This package provides an implementation of the maximum likelihood methods for deriving Elo scores as published in Foerster, Franz et al. (2016) <DOI:10.1038/srep35404>.

Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.

Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package.

Analyses EuFMDiS output files in a Shiny App. The distributions of relevant output parameters are described in form of tables (quantiles) and plots. The App is called using eufmdis.adapt::run_adapt().

Simulate and fitting exponential multivariate Hawkes model. This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.2307/2334319>, with an exponential kernel and fits the parameters from the data. Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with maxLik package.

This package implements event extraction and early classification of events in data streams in R. It has the functionality to generate 2-dimensional data streams with events belonging to 2 classes. These events can be extracted and features computed. The event features extracted from incomplete-events can be classified using a partial-observations-classifier (Kandanaarachchi et al. 2018) <doi:10.1371/journal.pone.0236331>.

Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.

Simulates and estimates the Exponential Random Partition Model presented in the paper Hoffman, Block, and Snijders (2023) <doi:10.1177/00811750221145166>. It can also be used to estimate longitudinal partitions, following the model proposed in Hoffman and Chabot (2023) <doi:10.1016/j.socnet.2023.04.002>. The model is an exponential family distribution on the space of partitions (sets of non-overlapping groups) and is called in reference to the Exponential Random Graph Models (ERGM) for networks.

Allows to calculate the probabilities of occurrences of an event in a great number of repetitions of Bernoulli experiment, through the application of the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. Gives the possibility to show the results graphically and analytically, and to compare the results obtained by the application of the above theorems with those calculated by the direct application of the Binomial formula. Is basically useful for educational purposes.

This package provides convenience functions for researching experiences including user, customer, patient, employee, and other human experiences. It provides a suite of tools to simplify data exploration such as benchmarking, comparing groups, and checking for differences. The outputs translate statistical approaches in applied experience research to human readable output.

Extract features from tabular data in a declarative fashion, with a focus on processing medical records. Features are specified as JSON and are independently processed before being joined. Input data can be provided as CSV files or as data frames. This setup ensures that data is transformed in a modular and reproducible manner, and allows the same pipeline to be easily applied to new data.

This package provides an R interface to the Evolution API <https://evoapicloud.com>, enabling sending and receiving WhatsApp messages directly from R'. Functions include sending text, media (image/video/document), audio, stickers, geographic locations, contacts, polls, interactive lists and button messages. Also includes number verification and structured CLI logging for debugging.

Datasets from Nelson, Coffin and Copeland "Introductory Statistics for Engineering Experimentation" (Elsevier, 2003) with sample code.

Misc functions programmed by Eduard Szöcs. Provides read_regnie() to read gridded precipitation data from German Weather Service (DWD, see <http://www.dwd.de/> for more information).

This package provides functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises.