Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.

Track Estimation using YAPS (Yet Another Positioning Solver)

Estimate tracks of animals tagged with acoustic transmitters. 'yaps' was introduced in 2017 as a transparent open-source tool to estimate positions of fish (and other aquatic animals) tagged with acoustic transmitters. Based on registrations of acoustic transmitters on hydrophones positioned in a fixed array, 'yaps' enables users to synchronize the collected data (i.e. correcting for drift in the internal clocks of the hydrophones/receivers) and subsequently to estimate tracks of the tagged animals. The paper introducing 'yaps' is available in open access at Baktoft, Gjelland, Økland & Thygesen (2017) . Also check out our cookbook with a completely worked through example at Baktoft, Gjelland, Økland, Rehage, Rodemann, Corujo, Viadero & Thygesen (2019) . Additional tutorials will eventually make their way onto the project website at < https://baktoft.github.io/yaps/>.

Convert Gene IDs Between Each Other and Fetch Annotations from Biomart

Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in 'AnnotationDbi' in convertId2() but that function is only provided as fallback mechanism for the most common use cases in data analysis. The main function in the package is convert.bm() which queries BioMart using the full capacity of the API provided through the 'biomaRt' package. Presets and defaults are provided for convenience but all "marts", "filters" and "attributes" can be set by the user. Function convert.alias() converts Gene Symbols to Aliases and vice versa and function likely_symbol() attempts to determine the most likely current Gene Symbol.

Rasch Model Parameters by Pairwise Algorithm

Performs the explicit calculation -- not estimation! -- of the Rasch item parameters for dichotomous and polytomous item responses, using a pairwise comparison approach. Person parameters (WLE) are calculated according to Warm's weighted likelihood approach.

Automated Transcriptome Classifier Pipeline: Comprehensive Transcriptome Analysis

An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. We rely on the 'pamr' < http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.

R Interface to the Pushbullet Messaging Service

An R interface to the Pushbullet messaging service which provides fast and efficient notifications (and file transfer) between computers, phones and tablets. An account has to be registered at the site < https://www.pushbullet.com> site to obtain a (free) API key.

Configural Frequencies Analysis Using Log-Linear Modeling

Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.

Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

Estimate Global Clustering in Infectious Disease

Implements various novel and standard clustering statistics and other analyses useful for understanding the spread of infectious disease.

Continuous Time Stochastic Modelling using Template Model Builder

Perform state and parameter inference, and forecasting, in stochastic state-space systems using the 'ctsmTMB' class. This class, built with the 'R6' package, provides a user-friendly interface for defining and handling state-space models. Inference is based on maximum likelihood estimation, with derivatives efficiently computed through automatic differentiation enabled by the 'TMB'/'RTMB' packages (Kristensen et al., 2016) . The available inference methods include Kalman filters, in addition to a Laplace approximation-based smoothing method. For further details of these methods refer to the documentation of the 'CTSMR' package < https://ctsm.info/ctsmr-reference.pdf> and Thygesen (2025) . Forecasting capabilities include moment predictions and stochastic path simulations, both implemented in 'C++' using 'Rcpp' (Eddelbuettel et al., 2018) for computational efficiency.