This package provides programmatic access to the Dark Sky API <https://darksky.net/dev/docs>, which provides current or historical global weather conditions.

Detection of differential item functioning (DIF) among dichotomously scored items and differential distractor functioning (DDF) among unscored items with non-linear regression procedures based on generalized logistic regression models (Hladka & Martinkova, 2020, <doi:10.32614/RJ-2020-014>).

Model fitting and evaluation tools for double generalized linear models (DGLMs). This class of models uses one generalized linear model (GLM) to fit the specified response and a second GLM to fit the deviance of the first model.

Algorithm to handle with optimal subset selection for distributed local principal component analysis. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.

This package provides a convenient framework to simulate, test, power, and visualize data for differential expression studies with lognormal or negative binomial outcomes. Supported designs are two-sample comparisons of independent or dependent outcomes. Power may be summarized in the context of controlling the per-family error rate or family-wise error rate. Negative binomial methods are described in Yu, Fernandez, and Brock (2017) <doi:10.1186/s12859-017-1648-2> and Yu, Fernandez, and Brock (2020) <doi:10.1186/s12859-020-3541-7>.

This package provides methods for estimating multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring. Cho, H., Holloway, S. T., and Kosorok, M. R. (2022) <doi:10.1093/biomet/asac047>.

Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path.

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly,our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Dampened weighted least squares ('DWLS') is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. See: <https://www.nature.com/articles/s41467-019-10802-z.pdf> for more information about the development of DWLS and the methods behind our functions.

Written to help undergraduate as well as graduate students to get started with R for basic econometrics without the need to import specific functions and datasets from many different sources. Primarily, the package is meant to accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2024, ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook Auer, L.v. (2023, ISBN: 978-3-658-42699-6).

This package provides density functions for the joint distribution of choice, response time and confidence for discrete confidence judgments as well as functions for parameter fitting, prediction and simulation for various dynamical models of decision confidence. All models are explained in detail by Hellmann et al. (2023; Preprint available at <https://osf.io/9jfqr/>, published version: <doi:10.1037/rev0000411>). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, <doi:10.1037/a0019737>), and various race models. C++ code for dynWEV and 2DSD is based on the rtdists package by Henrik Singmann.

Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Magrini, 2018 <doi:10.2478/bile-2018-0012>; Magrini et al., 2019 <doi:10.1007/s11135-019-00855-z>). DLSEMs account for temporal delays in the dependence relationships among the variables through a single parameter per covariate, thus allowing to perform dynamic causal inference in a feasible fashion. Endpoint-constrained quadratic, quadratic decreasing, linearly decreasing and gamma lag shapes are available.

Time series analysis of network connectivity. Detects and visualizes change points between networks. Methods included in the package are discussed in depth in Baek, C., Gates, K. M., Leinwand, B., Pipiras, V. (2021) "Two sample tests for high-dimensional auto-covariances" <doi:10.1016/j.csda.2020.107067> and Baek, C., Gampe, M., Leinwand B., Lindquist K., Hopfinger J. and Gates K. (2023) â Detecting functional connectivity changes in fMRI dataâ <doi:10.1007/s11336-023-09908-7>.

This package provides a Shiny'-based interactive platform for end-to-end data science workflows. Provides modules for data import (CSV, Excel', RDS, TXT), data preprocessing (missing value imputation, encoding, scaling, outlier removal), exploratory data analysis with interactive plots and normality tests, supervised learning (regression and classification each with eight algorithms), and unsupervised learning (k-means, hierarchical clustering, density-based spatial clustering of applications with noise). Designed for students and practitioners in data science and artificial intelligence.

This package provides an extensive and curated collection of datasets related to the digestive system, stomach, intestines, liver, pancreas, and associated diseases. This package includes clinical trials, observational studies, experimental datasets, cohort data, and case series involving gastrointestinal disorders such as gastritis, ulcers, pancreatitis, liver cirrhosis, colon cancer, colorectal conditions, Helicobacter pylori infection, irritable bowel syndrome, intestinal infections, and post-surgical outcomes. The datasets support educational, clinical, and research applications in gastroenterology, public health, epidemiology, and biomedical sciences. Designed for researchers, clinicians, data scientists, students, and educators interested in digestive diseases, the package facilitates reproducible analysis, modeling, and hypothesis testing using real-world and historical data.

This package performs Diallel Analysis with R using Griffing's and Hayman's approaches. Four different Methods (1: Method-I (Parents + F1's + reciprocals); 2: Method-II (Parents and one set of F1's); 3: Method-III (One set of F1's and reciprocals); 4: Method-IV (One set of F1's only)) and two Models (1: Fixed Effects Model; 2: Random Effects Model) can be applied using Griffing's approach.

Access Datastream content through <https://product.datastream.com/dswsclient/Docs/Default.aspx>., our historical financial database with over 35 million individual instruments or indicators across all major asset classes, including over 19 million active economic indicators. It features 120 years of data, across 175 countries â the information you need to interpret market trends, economic cycles, and the impact of world events. Data spans bond indices, bonds, commodities, convertibles, credit default swaps, derivatives, economics, energy, equities, equity indices, ESG, estimates, exchange rates, fixed income, funds, fundamentals, interest rates, and investment trusts. Unique content includes I/B/E/S Estimates, Worldscope Fundamentals, point-in-time data, and Reuters Polls. Alongside the content, sit a set of powerful analytical tools for exploring relationships between different asset types, with a library of customizable analytical functions. In-house timeseries can also be uploaded using the package to comingle with Datastream maintained datasets, use with these analytical tools and displayed in Datastreamâ s flexible charting facilities in Microsoft Office.

Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haplotype searches in a multiple infection setting. This package is primarily developed as part of the Pf3k project, which is a global collaboration using the latest sequencing technologies to provide a high-resolution view of natural variation in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from patient blood sample, which often contains more than one parasite strain, with unknown proportions. This package is used for deconvoluting mixed haplotypes, and reporting the mixture proportions from each sample.

Efficient computation of multivariate statistical depth functions in arbitrary dimension d. Implements Mahalanobis depth, Tukey (halfspace) depth, Liu simplicial depth (via adaptive Monte Carlo), projection depth, and spatial depth. Provides depth-based medians, central regions, outlier detection, and depth-depth plots. C++ backends via Rcpp and RcppEigen ensure performance at large n and d. References: Liu (1990) <doi:10.1214/aos/1176347507>, Zuo and Serfling (2000) <doi:10.1214/aos/1016218226>, Vardi and Zhang (2000) <doi:10.1073/pnas.97.4.1423>.

This package contains data organized by topics: categorical data, regression model, means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.

Estimation of the average treatment effect when controlling for high-dimensional confounders using debiased inverse propensity score weighting (DIPW). DIPW relies on the propensity score following a sparse logistic regression model, but the regression curves are not required to be estimable. Despite this, our package also allows the users to estimate the regression curves and take the estimated curves as input to our methods. Details of the methodology can be found in Yuhao Wang and Rajen D. Shah (2020) "Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders" <arXiv:2011.08661>. The package relies on the optimisation software MOSEK <https://www.mosek.com/> which must be installed separately; see the documentation for Rmosek'.

Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).

Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes, but often require collection of additional information may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. See the following references for details on the methods: Vickers (2006) <doi:10.1177/0272989X06295361>, Vickers (2008) <doi:10.1186/1472-6947-8-53>, and Pfeiffer (2020) <doi:10.1002/bimj.201800240>.

Plots dependency logos from a set of aligned input sequences.

Distributed estimation method is based on a Laplace factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.