# Dirk Schumacher

#### 14 packages on CRAN

#### 2 packages on GitHub

Provides WHO Child Growth Standards (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: <https://www.who.int/childgrowth/standards/en/>.

This package provides easy access to http://daten.berlin.de It allows you to search through the data catalogue and to download the data directly from within R.

Programmatic access to the DuckDuckGo Instant Answer API <https://api.duckduckgo.com/api>.

Create encrypted html files that are fully self contained and do not require any additional software. Using the package you can encrypt arbitrary html files and also directly create encrypted 'rmarkdown' html reports.

An implementation of list comprehensions as purely syntactic sugar with a minor runtime overhead. It constructs nested for-loops and executes the byte-compiled loops to collect the results.

Model mixed integer linear programs in an algebraic way directly in R. The model is solver-independent and thus offers the possibility to solve a model with different solvers. It currently only supports linear constraints and objective functions. See the 'ompr' website <https://dirkschumacher.github.io/ompr> for more information, documentation and examples.

A solver for 'ompr' based on the R Optimization Infrastructure ('ROI'). The package makes all solvers in 'ROI' available to solve 'ompr' models. Please see the 'ompr' website <https://dirkschumacher.github.io/ompr> and package docs for more information and examples on how to use it.

Bindings for the 'PicoSAT' solver to solve Boolean satisfiability problems (SAT). The boolean satisfiability problem asks the question if a given boolean formula can be TRUE; i.e. does there exist an assignment of TRUE/FALSE for each variable such that the whole formula is TRUE? The package bundles 'PicoSAT' solver release 965 <http://www.fmv.jku.at/picosat/>.

Find out who maintains the packages you use in your current session or in your package library and maybe say 'thank you'.

Provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007, <https://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a>) using 'ROI' <https://cran.r-project.org/package=ROI> or 'lpSolveAPI' <https://cran.r-project.org/package=lpSolveAPI>. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the 'brglm2' <https://cran.r-project.org/package=brglm2> package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration. According to the results in Lesaffre & Albert (1989, <https://www.jstor.org/stable/2345845>), divergence of those ratios indicates data separation.

Contains tools for formatting inline code, renaming redundant columns, aggregating age categories, and calculating proportions with confidence intervals. This is part of the 'R4Epis' project <https://r4epis.netlify.com>.

A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymise data, and fitting discretised Gamma distributions.

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of H�hle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. 'hhh4' estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. 'twinSIR' models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by H�hle (2009) <doi:10.1002/bimj.200900050>. 'twinstim' estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2015) <http://arxiv.org/abs/1411.0416>.

Provides a coherent interface for evaluating models fit with the trending package. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Provides a coherent interface to multiple modelling tools for fitting trends along with a standardised approach for generating confidence and prediction intervals.