It helps in determination of sample size for estimating population mean or proportion under simple random sampling with or without replacement and stratified random sampling without replacement. When prior information on the population coefficient of variation (CV) is unavailable, then a preliminary sample is drawn to estimate the CV which is used to compute the final sample size. If the final size exceeds the preliminary sample size, then additional units are drawn; otherwise, the preliminary sample size is considered as final sample size. For stratified random sampling without replacement design, it also calculates the sample size in each stratum under different allocation methods for estimation of population mean and proportion based upon the availability of prior information on sizes of the strata, standard deviations of the strata and costs of drawing a sampling unit in the strata.For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" <https://archive.org/details/samplingtechniqu0000coch_t4x6>.

This package provides a modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods. Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon. Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity, acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) <doi:10.1016/S0042-6989(03)00084-1>, Otero-Millan et al. (2014)<doi:10.1167/14.2.18>, and Nyström and Holmqvist (2010) <doi:10.3758/BRM.42.1.188>) to label individual samples and then applies a majority vote approach to identify saccades. The package includes three methods but can be extended via custom functions. It also uses a modular approach to compute velocity and acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades.

Remove batch effects by projecting query batches into the reference batch space.

This package provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.

Model age schedules of mortality, nqx, suitable for a life table. This package implements the SVD-Comp mortality model indexed by either child or child/adult mortality. Given input value(s) of either 5q0 or (5q0, 45q15), the qx() function generates single-year 1qx or 5-year 5qx conditional age-specific probabilities of dying. See Clark (2016) <doi:10.48550/arXiv.1612.01408> and Clark (2019) <doi:10.1007/s13524-019-00785-3>.

This package creates an S4 class "SSM" and defines functions for fitting smooth supersaturated models, a polynomial model with spline-like behaviour. Functions are defined for the computation of Sobol indices for sensitivity analysis and plotting the main effects using FANOVA methods. It also implements the estimation of the SSM metamodel error using a GP model with a variety of defined correlation functions.

This package provides functions for computing test subscores using different methods in both classical test theory (CTT) and item response theory (IRT). This package enables three types of subscoring methods within the framework of CTT and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) <doi:10.4324/9781410604729>, (2) Haberman's subscoring methods (Haberman, 2008) <doi:10.3102/1076998607302636>, and (3) Yen's objective performance index (OPI; Yen, 1987) <https://www.ets.org/research/policy_research_reports/publications/paper/1987/hrap>. It also includes functions to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to examine whether test subscores are of added value. In addition, the package includes a function to assess the local independence assumption of IRT with Yen's Q3 statistic (Yen, 1984 <doi:10.1177/014662168400800201>; Yen, 1993 <doi:10.1111/j.1745-3984.1993.tb00423.x>).

Label, recode, rename, and convert datasets and ASCII files more efficiently. speedycode automates the code necessary for labeling variables with the labelled package, recoding and renaming variables with dplyr syntax, and converting ASCII files with the readroper package. Most functions require only the name of the dataset and the code will be automatically written. Some convenience functions useful for converting ASCII files are also included.

Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations.

This package provides a toolkit for Partially Observed Markov Decision Processes (POMDP). Provides bindings to C++ libraries implementing the algorithm SARSOP (Successive Approximations of the Reachable Space under Optimal Policies) and described in Kurniawati et al (2008), <doi:10.15607/RSS.2008.IV.009>. This package also provides a high-level interface for generating, solving and simulating POMDP problems and their solutions.

Empirical likelihood methods for asymptotically efficient estimation of models based on conditional or unconditional moment restrictions; see Kitamura, Tripathi & Ahn (2004) <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013) <doi:10.1002/cjs.11183>. Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in Rcpp for speed.

This package provides a set of functions for generating SPSS syntax files from the R environment.

This package provides a collection of functions to search and download street view imagery ('Mapilary <https://www.mapillary.com/developer/api-documentation>) and to extract, quantify, and visualize visual features. Moreover, there are functions provided to generate Qualtrics survey in TXT format using the collection of street views for various research purposes.

Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)", Cengage Learning.

This package provides functionality for analytically calculating parameters (via the InteractionPoweR package) useful for simulation of moderated multiple regression, based on the correlations among the predictors and outcome and the reliability of predictors.

Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's and Chao's regression. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) <doi:10.1191/1471082X03st057oa>, Böhning and van der Heijden (2019) <doi:10.1214/18-AOAS1232>, Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) <doi:10.1007/s10260-021-00556-8>.

Get started with new projects by dropping a skeleton of a new project into a new or existing directory, initialise git repositories, and create reproducible environments with the renv package. The package allows for dynamically named files, folders, file content, as well as the functionality to drop individual template files into existing projects.

Utilities for single nucleotide polymorphism (SNP) based kinship analysis testing and evaluation. The skater package contains functions for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing identity by descent (IBD) segment data. Package functions and methods are described in Turner et al. (2021) "skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation" <doi:10.1101/2021.07.21.453083>.

Bayesian variable selection, model choice, and regularized estimation for (spatial) generalized additive mixed regression models via stochastic search variable selection with spike-and-slab priors.

Handles both vector and matrices, using a flexible S4 class for automatic differentiation. The method used is forward automatic differentiation. Many functions and methods have been defined, so that in most cases, functions written without automatic differentiation in mind can be used without change.

Apply the spectral residual algorithm to data, such as a time series, to detect anomalies. Anomaly scores can be used to determine outliers based upon a threshold or fed into more sophisticated prediction models. Methods are based upon "Time-Series Anomaly Detection Service at Microsoft", Ren, H., Xu, B., Wang, Y., et al., (2019) <doi:10.48550/arXiv.1906.03821>.

Download and read datasets from the Swiss National Science Foundation (SNF, FNS, SNSF; <https://snf.ch>). The package is lightweight and without dependencies. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. There are also utilities for comparing different versions of datasets, i.e. to report added, removed and changed entries.

This takes spatial single-cell-type RNA-seq data (specifically designed for Slide-seq v2) that calls copy number alterations (CNAs) using pseudo-spatial binning, clusters cellular units (e.g. beads) based on CNA profile, and visualizes spatial CNA patterns. Documentation about SlideCNA is included in the the pre-print by Zhang et al. (2022, <doi:10.1101/2022.11.25.517982>). The package enrichR (>= 3.0), conditionally used to annotate SlideCNA-determined clusters with gene ontology terms, can be installed at <https://github.com/wjawaid/enrichR> or with install_github("wjawaid/enrichR").

This package provides a small set of helper functions to convert sjPlot HTML-tables to R data.frame objects / knitr::kable-tables.