Request Scheduler

Offers classes and functions to contact web servers while enforcing scheduling rules required by the sites. The URL class makes it easy to construct a URL by providing parameters as a vector. The Request class allows to describes SOAP (Simple Object Access Protocol) or standard requests: URL, method (POST or GET), header, body. The Scheduler class controls the request frequency for each server address by mean of rules (Rule class). The RequestResult class permits to get the request status to handle error cases and the content.

File System Cache

Manages a file system cache. Regular files can be moved or copied to the cache folder. Sub-folders can be created in order to organize the files. Files can be located inside the cache using a glob function. Text contents can be easily stored in and retrieved from the cache using dedicated functions. It can be used for an application or a package, as a global cache, or as a per-user cache, in which case the standard OS user cache folder will be used (e.g.: on Linux $HOME/.cache/R/my_app_or_pkg_cache_folder).

'SQL' Query Builder

Allows to build complex 'SQL' (Structured Query Language) queries dynamically. Classes and/or factory functions are used to produce a syntax tree from which the final character string is generated. Strings and identifiers are automatically quoted using the right quotes, using either ANSI (American National Standards Institute) quoting or the quoting style of an existing database connector. Style can be configured to set uppercase/lowercase for keywords, remove unnecessary spaces, or omit optional keywords.

Statistical Support for Hierarchical Clusters

Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach.

Estimate Rogers-Castro Migration Age Schedules with Bayesian Models

A collection of functions to estimate Rogers-Castro migration age schedules using 'Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) .

Mixed Models for Repeated Measures

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.

'rChoiceDialogs' Collection

Collection of portable choice dialog widgets.

Multidimensional Top Scoring for Creativity Research

Implementation of Multidimensional Top Scoring method for creativity assessment proposed in Boris Forthmann, Maciej Karwowski, Roger E. Beaty (2023) .

Mixed ICA/PCA

Implements mixed ICA/PCA model for blind source separation, potentially with inclusion of Gaussian sources

Computes Statistics for Relational Event History Data

Computes a variety of statistics for relational event models (Meijerink et al., 2022, ). Relational event models enable researchers to investigate exogenous and endogenous factors, and interactions, influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, ), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, ), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models.