dlmodeler is a set of user-friendly functions to simplify
the state-space modelling, fitting, analysis and forecasting of
Generalized Dynamic Linear Models (DLMs). It includes functions
to name and extract individual components of a DLM, build
classical seasonal time-series models (monthly, quarterly,
yearly, etc. with calendar adjustments) and provides a unified
interface compatible with other state-space packages including:
dlm, FKF and KFAS.

dlmodeler is a set of user-friendly functions to simplify the state-space modelling, fitting, analysis and forecasting of Generalized Dynamic Linear Models (DLMs). It includes functions to name and extract individual components of a DLM, build classical seasonal time-series models (monthly, quarterly, yearly, etc. with calendar adjustments) and provides a unified interface compatible with other state-space packages including: dlm, FKF and KFAS.

stats, graphics, grDevices, splines, tsModel
The package dlnm contains functions to specify basis and
cross-basis matrices in order to run distributed lag models and
their non-linear extension, then to predict and graph the
results for a fitted model.

stats, graphics, grDevices, utils, splines, tsModel

The package dlnm contains functions to specify and run distributed lag linear and non-linear models.

The package dlnm contains functions to specify and run
distributed lag linear and non-linear models.

stats, graphics, grDevices, utils, splines

stats, graphics, grDevices, utils, splines, nlme
mgcv, survival, lme4, gee, geepack, mvmeta

stats, graphics, grDevices, utils, splines, nlme, mgcv,
tsModel
Collection of functions for distributed lag linear and non-linear models.

Antonio Gasparrini [aut, cre],
Ben Armstrong [aut],
Fabian Scheipl [ctb]

Tools for Data Diagnosis, Exploration, Transformation
A collection of tools that support data diagnosis, exploration, and transformation.
Data diagnostics provides information and visualization of missing values and 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
relationship between target variable and predictor. Data transformation supports binning
for categorizing continuous variables, imputates missing values and outliers, resolving skewness.
And it creates automated reports that support these three tasks.
dplyr, magrittr, tidyr, ggplot2, RcmdrMisc, corrplot, rlang,
purrr, tibble, tidyselect, classInt, moments, kableExtra,
prettydoc, smbinning, xtable, knitr, rmarkdown, RColorBrewer,
gridExtra, tinytex, methods, DMwR, mice, rpart, randomForest