Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis.
This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.
Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data. DCPO uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with Stan'. References: Solt (2020) <doi:10.31235/osf.io/d5n9p>.
This package provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models. As a special case of the EBglmnet package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.
This package provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.
Generates experiments - simulating structured or experimental data as: completely randomized design, randomized block design, latin square design, factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; Naes et al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985; Montgomery, 2001, ISBN:0471316490).
Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) <doi:10.1371/journal.pcbi.1004978>).
Set of R functions to be coupled with the xeus-r jupyter kernel in order to drive execution of code in notebook input cells, how R objects are to be displayed in output cells, and handle two way communication with the front end through comms.
Streamflow (and climate) reconstruction using Linear Dynamical Systems. The advantage of this method is the additional state trajectory which can reveal more information about the catchment or climate system. For details of the method please refer to Nguyen and Galelli (2018) <doi:10.1002/2017WR022114>.
Compute the multiple Grubbs-Beck low-outlier test on positively distributed data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow data processing discussed in Cohn et al. (2013) <doi:10.1002/wrcr.20392> and England et al. (2017) <doi:10.3133/tm4B5>.
Run multiple Large Language Model predictions against a table. The predictions run row-wise over a specified column. It works using a one-shot prompt, along with the current row's content. The prompt that is used will depend of the type of analysis needed.
Generation of response patterns under dichotomous and polytomous computerized multistage testing (MST) framework. It holds various item response theory (IRT) and score-based methods to select the next module and estimate ability levels (Magis, Yan and von Davier (2017, ISBN:978-3-319-69218-0)).
Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>. It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.
Calibrate p-values under a robust perspective using the methods developed by Sellke, Bayarri, and Berger (2001) <doi:10.1198/000313001300339950> and obtain measures of the evidence provided by the data in favor of point null hypotheses which are safer and more straightforward to interpret.
Construction method of nested resolvable designs from a projective geometry defined on Galois field of order 2. The obtained Resolvable designs are used to build uniform design. The presented results are based on <https://eudml.org/doc/219563> and A. Boudraa et al. (See references).
This package implements the Bayesian quantile regression model for binary longitudinal data (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>. The model handles both fixed and random effects and implements both a blocked and an unblocked Gibbs sampler for posterior inference.
This package provides functions for fitting semiparametric regression models for panel count survival data. An overview of the package can be found in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and Chiou et al. (2018) <doi:10.1111/insr.12271>.
Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.
Framework provides functions to parse Training Center XML (TCX) files and extract key activity metrics such as total distance, total time, calories burned, maximum altitude, and power values (watts). This package is useful for analyzing workout and training data from devices that export TCX format.
This package provides methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) <doi:10.48550/arXiv.2108.04731>.
An interface between R and the Valhalla API. Valhalla is a routing service based on OpenStreetMap data. See <https://valhalla.github.io/valhalla/> for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometer distance).
The pscl is an R package providing classes and methods for:
Bayesian analysis of roll call data (item-response models);
elementary Bayesian statistics;
maximum likelihood estimation of zero-inflated and hurdle models for count data;
utility functions.
Native R interface to TMB (Template Model Builder) so models can be written entirely in R rather than C++'. Automatic differentiation, to any order, is available for a rich subset of R features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. RTMB provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.