The goal of dlr is to provide a friendly wrapper around the common 
    pattern of downloading a file if that file does not already exist locally.

Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Magrini, 2018 <doi:10.2478/bile-2018-0012>; Magrini et al., 2019 <doi:10.1007/s11135-019-00855-z>). DLSEMs account for temporal delays in the dependence relationships among the variables through a single parameter per covariate, thus allowing to perform dynamic causal inference in a feasible fashion. Endpoint-constrained quadratic, quadratic decreasing, linearly decreasing and gamma lag shapes are available.

Monthly download stats of 'CRAN' and 'Bioconductor' packages.
	     Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <https://cranlogs.r-pkg.org:443>.
	     'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.

Provides tools for working with multiple related
    tables, stored as data frames or in a relational database.  Multiple
    tables (data and metadata) are stored in a compound object, which can
    then be manipulated with a pipe-friendly syntax.

Dynamic model averaging for binary and continuous
        outcomes.

Functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) (<DOI:10.1016/0304-4076(80)90070-6>).

Functions for fitting a Bayesian model for grouping binary
    dissimilarity matrices in homogeneous clusters. Currently, it includes
    methods only for binary data (<doi:10.18637/jss.v100.i16>).

DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015).
  Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.
  Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>.
  Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs),
  a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008).
  Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common
  features from within standard diffusion models with the addition of superimposed controlled and automatic activation.
  The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural
  conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality
  to fit the model to observed data. Further details are provided in the following paper: 
  Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction 
  time and error rate data. Methods in Psychology, 100074. <doi:10.1016/j.metip.2021.100074>.