Provides routines for Maximum likelihood,
    Kalman filtering and smoothing, and Bayesian
    analysis of Normal linear State Space models, also known as 
    Dynamic Linear Models.

A collection of data-limited management procedures that can be evaluated 
    with management strategy evaluation with the 'MSEtool' package, or applied to 
    fishery data to provide management recommendations.

Collection of functions for distributed lag linear and non-linear models.

A collection of tools that support data diagnosis, exploration, and transformation. 
    Data diagnostics provides information and visualization of missing values, outliers, and unique 
    and negative values to help you understand the distribution and quality of your data. 
    Data exploration provides information and visualization of the descriptive statistics of 
    univariate variables, normality tests and outliers, correlation of two variables, 
    and the relationship between the target variable and predictor. Data transformation supports binning 
    for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. 
    And it creates automated reports that support these three tasks.

Algorithm to handle with optimal subset selection for distributed local principal component analysis. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.

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.