Imports: survival , dplyr , tidyr , ggplot2 , mvtnorm , matrixcalc
Description: Analyze data with measurement error when there is a validation subsample randomly selected from the full sample.  The method assumes surrogate variables measured with error are available for the full sample, and reference variables measured with little or no error are available for this randomly selected subsample of the full sample.  Measurement errors may be differential or non differential, in any or all predictors (simultaneously) as well as outcome.  The "augmented" estimates derived are based upon the multivariate correlation between regression model parameter estimates for the reference variables and for the surrogate variables in the validation subset.  Because the validation subsample is chosen at random whatever biases are imposed by measurement error, non-differential or differential, are reflected in this correlation and can be used to derive estimates for the reference variables using data from the whole sample.  The main functions in the package are meerva.fit which calculates estimates for a dataset, and meerva.sim.block which simulates multiple datasets as described by the user, and analyzes these datasets, storing the regression coefficient estimates for inspection. This work derives from Chen Y-H, Chen H. (2000) <doi:10.1111/1467-9868.00243>, Chen Y-H. (2002) <doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015) <doi:10.1016/j.jmva.2015.05.017> and Tong J, Huang J, Chubak J, et al. (2020) <doi:10.1093/jamia/ocz180>.

Description: Allows management of 'Meetup' groups via the <https:www.meetup.com/meetup_api/>.
Provided are a set of functions that enable fetching information of joined meetups, attendance,
and members. This package requires the use of an API key.

Title: Faunistic count data handling and reporting
Description: R package for faunistic count data handling and reporting. The name 'mefa' stands for the term 'metafaunistics' indicating that handling of basic data is only the first, but the most critical and sometimes most time consuming part of data analysis. It contains functions to create and manage objects combining basic faunistic (sample/species/count or crosstabulated) count data and sample/species attribute tables. Segments within the count data and samples with zero count can be indicated and used in subsequent operations. Reports can be generated in plain text or LaTeX format.
Packaged: Thu Dec  6 09:46:37 2007; Peter Solymos