Guesses the MIME type from a filename extension using the data
    derived from /etc/mime.types in UNIX-type systems.

Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC) dataset, 
  a repository of freely accessible electronic health records. MIMER(MIMIC-enabled Research) package, offers
  a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes,
  particularly in antimicrobial resistance(AMR) studies. It simplifies complex data manipulation tasks, allowing
  researchers to focus on their primary inquiries without being bogged down by wrangling complexities.

Our approach uses a  mixture of multilayer stochastic block models to group co-membership matrices with similar information into components and to partition observations into different clusters. See De Santiago (2023, ISBN: 978-2-87587-088-9).

The MIMS-unit algorithm is developed to compute Monitor Independent
    Movement Summary Unit, a measurement to summarize raw accelerometer data 
    while ensuring harmonized results across different devices. It also includes
    scripts to reproduce results in the related publication 
    (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).

Calculate dissolved gas concentrations from raw MIMS 
	(Membrane Inlet Mass Spectrometer) signal data. Use mimsy() on 
	a formatted CSV file to return dissolved gas concentrations 
	(mg and microMole) of N2, O2, Ar based on 
	gas solubility at temperature, pressure, and salinity. See references 
	Benson and Krause (1984), Garcia and Gordon (1992), Stull (1947), 
	and Hamme and Emerson (2004) for more information. Easily save the 
	output to a nicely-formatted multi-tab 'Excel' workbook with mimsy.save(). 
	Supports dual-temperature standard calibration for dual-bath MIMS setups.

In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum  number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) <doi:10.1080/03610926.2016.1152490>. This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface.