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Bayesian Hierarchical Regression on Clearance Rates in the Presence of Lag and Tail Phases
An implementation of the Bayesian Clearance Estimator (Fogarty et al. (2015)
Censoring Data and Likelihood-Based Correlation Estimation
A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness.
Automatic Fixed Rank Kriging
Automatic fixed rank kriging for (irregularly located)
spatial data using a class of basis functions with multi-resolution features
and ordered in terms of their resolutions. The model parameters are estimated
by maximum likelihood (ML) and the number of basis functions is determined
by Akaike's information criterion (AIC). For spatial data with either one
realization or independent replicates, the ML estimates and AIC are efficiently
computed using their closed-form expressions when no missing value occurs. Details
regarding the basis function construction, parameter estimation, and AIC calculation
can be found in Tzeng and Huang (2018)
Simulate from ODE-Based Population PK/PD and Systems Pharmacology Models
Facilitates simulation from hierarchical, ordinary differential equation (ODE) based models typically employed in drug development. A model specification file is created consisting of R and C++ code that is parsed, compiled, and dynamically loaded into the R session. Input data are passed in and simulated data are returned as R objects. A dosing event engine allows interventions (bolus and infusion) to be managed separately from the model code. Differential equations are solved with the 'DLSODA' routine in 'ODEPACK' (< https://computation.llnl.gov/casc/odepack/>).
Companion Package for Statistics with R
Provides functions and datasets to support inference with the open access book "An Introduction to Bayesian Thinking", available online < https://statswithr.github.io/book> and online videos for the "Statistics with R Specialization" < https://www.coursera.org/specializations/statistics>. which includes an introduction to Bayesian inference and decision making for one and two sample credible intervals and hypothesis testing for Gaussian and Binomial data, in addition to frequentist inference using model-based and randomization-based methods. To help with understanding concepts, 'shiny' applications are used to aide visualization of sampling distributions, credible intervals, hypothesis testing, Lindley's and Bartlett's paradoxes. For development versions or to report issues, please visit < https://github.com/StatsWithR/statsr>.
Flexible Simulations of Biological Sequence Evolution
An extensible object-oriented framework for the Monte Carlo simulation of sequence evolution written in 100 percent R. It is built on the top of the R.oo and ape packages and uses Gillespie's direct method to simulate substitutions, insertions and deletions.
Nonparametric Models for Longitudinal Data
Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods for the conditional-mean and conditional-distribution based models with longitudinal Data.
Weighted Portmanteau Tests for Time Series Goodness-of-fit
This packages contains the Weighted Portmanteau Tests as described in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing' accepted for publication by the Journal of the American Statistical Association.
Parsing Expression Grammars in Rcpp
A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement their own grammars and easily expose them in R packages.
A C++ Framework for Plant-Plant Interaction IBMs
A tool for simulating a variety of spatial individual-based models of
plant-plant interactions. User-created models can include any number of species, each of
which can be structured in any number of life-stages, where each life-stage has specific
death, growth and reproduction rates, as well as specific interaction radius, dispersal
radius, and interaction effects over each other species/life-stage. Life stages were modeled
so as to be a stochastic, individual-based version of differential Matrix Population
Models (Caswell 2001, ISBN:0-87893-096-5). Interactions can be positive
(facilitation) or negative (competition) and can affect death rates, growth rates or
reproduction rates. Interactions from multiple numbers are additive, so as to best
approximate classic population dynamics models such as the logistic model and
Lotka-Volterra model (Britton 2004, ISBN:9781852335366). All models work in continuous time, implemented as an optimized version
of the Gillespie algorithm (Gillespie 1976