Generates all necessary C functions allowing the user to work with
    the compiled-code interface of ode() and bvptwp(). The implementation supports
    "forcings" and "events". Also provides functions to symbolically compute
    Jacobians, sensitivity equations and adjoint sensitivities being the basis for
    sensitivity analysis.

Easily automate the following tasks to describe data frames:
		Summarise the distributions, and labelled missings of variables graphically
		and using descriptive statistics.
		For surveys, compute and summarise reliabilities (internal consistencies, 
		retest, multilevel) for psychological scales.
		Combine this information with metadata (such as item labels and labelled 
		values) that is derived from R attributes.
		To do so, the package relies on 'rmarkdown' partials, so you can generate 
		HTML, PDF, and Word documents. 
		Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so
		that search engines can find your data and index the metadata.
		The metadata are also available at your fingertips via RStudio Addins.

Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame.

Logic game in the style of the early 1980s home computers 
    that can be played in the R console. This game is inspired by 
    Mastermind, a game that became popular in the 1970s.
    Can you break the code?

Includes several classifications such as International Statistical Classification of  Diseases and Related Health Problems 10th Revision (ICD10),  Anatomical Therapeutic Chemical (ATC) Classification,  The International Classification of Diseases for Oncology (ICD-O-3), and  International Classification of Primary Care (ICPC). Includes function that adds descriptive label to code value.  Depending on classification following languages are available: English, Finnish, Swedish, and Latin.

Data analysis often requires coding, especially when data are collected through interviews, observations, or questionnaires. As a result, code counting and data preparation are essential steps in the analysis process. Analysts may need to count the codes in a text (Tokenization, counting of pre-established codes, computing the co-occurrence matrix by line) and prepare the data (e.g., min-max normalization, Z-score, robust scaling, Box-Cox transformation, and non-parametric bootstrap). For the Box-Cox transformation (Box & Cox, 1964, <https://www.jstor.org/stable/2984418>), the optimal Lambda is determined using the log-likelihood method. Non-parametric bootstrap involves randomly sampling data with replacement. Two random number generators are also integrated: a Lehmer congruential generator for uniform distribution and a Box-Muller generator for normal distribution. Package for educational purposes.