- Changed from
`stats::nlm()`

to`minpack.lm::nlsLM()`

function for estimating parameters in`estimate_k()`

,`estimate_k_1RM()`

,`estimate_kmod()`

,`estimate_kmod_1RM()`

,`estimate_klin()`

, and`estimate_klin_1RM()`

functions. - Added
`estimate_k_generic()`

and`estimate_k_generic_1RM()`

. These functions return the model object, but use the default`k`

value of 0.0333 - Added
`estimate_k_generic_1RM_mixed()`

which uses generic`k`

value of 0.0333 to predict the`0RM`

- Added
`estimate_k_generic_1RM_quantile()`

which uses generic`k`

value of 0.0333 to predict the`0RM`

- Added day counter (
`day`

) in the`strength_training_log`

dataset. This will be used for an example on how to use the rolling estimation - Added
`estimate_rolling_1RM()`

function. This is used to implement “embedded testing” using training logs to estimate both reps-max profiles and 1RMs - Updated the README.Rmd file with the above additions
- Added TOC to README.Rmd

- Fixed a bug in
`scheme_rep_acc()`

- now the END rep and step is used, and the reps are counted backwards as intended - Added comment in the
`vertical_generic()`

and`vertical_rep_accumulation()`

to avoid generating rep accumulation schme using those two functions, but rather using`scheme_rep_acc()`

- Formatting error in
`scheme_()`

functions - Changed default progression table to
`progression_perc_drop`

in all`scheme_()`

functions - Changed default vertical planning to
`vertical_const`

in all`scheme_()`

functions - Fixed a bug in
`scheme_light_heavy()`

- now it takes the highest rep and use that to estimate %1RMs - Added
`scheme_ladder()`

set and rep scheme - Added
`.vertical_rep_accumulation.post()`

function. This functions is to be applied AFTER scheme is generated. Other options is to use`scheme_rep_acc()`

function, that is flexible enough to generate most schemes, except for the`scheme_ladder()`

and`scheme_light_heavy()`

- Added
`vertical_block_undulating()`

vertical planning function. This is a combination of Block Variant (undulation in the steps) and Undulating (undulation in reps) - Fixed a “corner case” bug in
`scheme_generic()`

, where`vertical_set_accumulation`

didn’t repeat the adjustments, which cause problems if only single set is accumulated. This is because the adjustments were not accumulated, but rather “recycled”. - Changed the parameter name from
`accumulate_rep`

to`accumulate_set`

in`vertical_set_accumulation()`

and`vertical_set_accumulation_reverse()`

functions - Expanded the README.Rmd to include the discussion on Rep Accumulation scheme
- Added extra features to
`vertical_set_accumulation()`

and`vertical_set_accumulation_reverse()`

(see sequence argument) - Fixed the default arguments for
`adjustment`

in the`scheme_`

functions. Now they are flexible, depending on the`reps`

argument, but follow the general logic of a given scheme.

- Improved and simplified scheme plotting in
`plot_scheme()`

function. Removed {ggstance} from package dependencies - Added
`font_size`

arguments to`plot_scheme()`

and`plot_progression_table()`

functions - Removed default progression table from
`generate_progression_table()`

,`create_example()`

,`plot_progression_table()`

functions - Added
`plot_vertical()`

function for plotting vertical plan - Created
`STMr_scheme`

class (subclass of data frame), and now scheme can be plotted by using simple S3`plot`

method.`plot_scheme()`

function is now deprecated. Added three types of plots:`bar`

,`vertical`

, and`fraction`

. The`STMr_scheme`

class has now the following columns:`index`

,`step`

,`set`

,`reps`

,`adjustment`

, and`perc_1RM`

. - Added
`STMr_vertical`

constructor. Now the`vertical_`

functions return`STMr_vertical`

data frame object with following column names:`index`

,`step`

,`set`

,`set_id`

, and`reps`

.`set_id`

is needed to sort out an issue (see above) for the`vertical_set_accumulation()`

and`vertical_set_accumulation_reverse()`

vertical plans when adjustment is applied inside`scheme_generic()`

function - In the output of the
`scheme_light_heavy()`

and`scheme_ladder()`

functions, I have set`adjustment`

to`NA`

since to avoid confusing the user. This is because due to the modifications that these functions does to the “light” sets, the adjustment is not applicable and not related to selected progression table - Added
`{ggfittext}`

package dependency, so the plot labels are now flexible and fit the “container”. This can be useful when set accumulation is used, so the labels do not go outside of the bars - Added
`reps_change`

to`vertical_set_accumulation()`

and`vertical_set_accumulation_reverse()`

, making them really flexible functions - Added
`scheme_manual()`

for manual generation of the scheme, which provides for the ultimate flexibility - Added
`perc_str`

argument to`plot()`

S3 method, which allows the user to remove “%” and thus have more space for label - Created
`release`

function and S3`plot`

method for merging multiple schemes (i.e., blocks or phases) into one release. This is used to inspect how multiple back-to-back phases mold together - Added
`perc_1RM`

argument to`scheme_manual()`

for the user to provide manual 1RM percentages, rather than to be estimated - Added
`scheme_perc_1RM()`

which is simpler`scheme_manual()`

for manually entering 1RM percentages. For example creating simple warm-up scheme - Added
`+`

method for`STMr_scheme`

objects. This allows for easy modular adding of the schemes

- Changed the STMr to ‘STMr’ in the DESCRIPTION as per CRAN member recommendation
- Added documentation about the functions output/return values as per
CRAN member recommendation. Documentation for the following functions
has been updated:
`create_example()`

,`get_perc_1RM()`

,`get_reps()`

- Renamed the package to {STMr} since there is already a CRAN package STM
- Fixed a bug in
`progression_rel_int()`

function - Renamed
`nRM_testing`

dataset to`RTF_testing`

, as well as renamed the columns to be more descriptive - Added mixed-level estimation functions for both simple and 1RM
estimation:
`estimate_k_mixed()`

,`estimate_k_1RM_mixed()`

,`estimate_kmod_mixed()`

,`estimate_kmod_1RM_mixed()`

,`estimate_klin_mixed()`

, and`estimate_klin_1RM_mixed()`

. These are implemented using the {nlme} package and`nlme::nlme()`

function - Improvements on the
`strength_training_log`

dataset. eRIR ratings are now halved, and everything over 5 is now`NA`

- Fixed examples in
`get_reps()`

function documentation - Rewrote README.Rmd file

Added different weighting options for the

`estimate_`

family of functions. These include- “none” (for equal weight, or no weighting of the observations)
- “reps” (for
`1/reps`

weighting) - “load” (for using weight or %1RM)
- “eRIR” (for
`1/(eRIR+1)`

weighting) - “reps x load”
- “reps x eRIR”
- “load x eRIR”
- “reps x load x eRIR”

Added

`strength_training_log`

dataset. Single individual performing two strength training sessions per week, over the course of 12 weeks (4 phases, each 3 weeks long). Individual eRIR (estimated reps-in-reserve) subjective rating is included in the dataset. This dataset is used to demonstrate techniques for*embedded*testing of the 1RM and individual profilesAdded

`estimate_k_quantile()`

,`estimate_kmod_quantile()`

, and`estimate_klin_quantile()`

functions to implement non-linear quantile estimation of the parameters

REWRITTEN the whole package. This version will have compatibility issues with the previous version due to different naming of the functions. The package is now more modular, flexible, and can be parameterized more easily

The functions are organized in the following manner:

- estimation functions (start with
`estimate_`

) - reps-max functions (start with
`max_`

). Epley’s, Modified Epley’s and Linear/Brzycki’s model are implemented - adjustment functions (start with
`adj_`

). Deducted Intensity (DI), Relative Intensity (RelInt), Reps In Reserve (RIR), and % Max Reps (%MR) methods are implemented - wrapper functions
`get_reps()`

and`get_perc_1RM()`

are implemented to combine reps-max models as well as progression (adjustment) functions into easy to use format - progression functions (start with
`progression_`

) are implemented and allow easy parameterization to involve specific model and their estimated parameter values *vertical planning*functions (start with`vertical_`

)*scheme function*(start with`scheme_`

)- plotting and printing functions:
`generate_progression_table()`

,`plot_progression_table()`

,`plot_scheme()`

, and`create_example()`

- estimation functions (start with
Fixed few typos in

`citation()`

Added sample data set

`nRM_testing`

, which contains reps max testing of 12 athletes using 70, 80, and 90% 1RM

- Estimated
`1RM`

in`estimate_xxx_1RM()`

functions is now in the second place in coefficient order - Added
`create_example()`

function for quickly creating example using selected progression table

- Added functionality to forward extra arguments to a custom max-reps
functions (i.e.,
`get_max_perc_1RM()`

). Also see`get_max_perc_1RM_k()`

functions - Added
`get_max_perc_1RM_k()`

,`get_max_reps_k()`

, and`get_predicted_1RM_k()`

functions that uses user defined`k`

value/parameter. Together with the previous functionality, use is not able to easily create custom max-reps table functions with extra arguments. This provides great flexibility - Added
`get_max_perc_1RM_kmod()`

,`get_max_reps_kmod()`

, and`get_predicted_1RM_kmod()`

functions that uses user defined`kmod`

value/parameter for the modified Epley’s equation Added`get_max_perc_1RM_klin()`

,`get_max_reps_klin()`

, and`get_predicted_1RM_klin()`

functions that uses user defined`klin`

value/parameter for the linear equation - Added
`estimate_`

family of functions to estimate Epley’s, modified Epley’s, and linear equation parameters, as well as novel estimation functions that uses absolute weight to estimate both`k`

,`kmod`

,`klin`

and`1RM`

parameters - Added missing
`font_size`

when plotting adjustments using`plot_progression_table()`

- Initial complete version of the package
- Added Relative Intensity and %MR progression tables
- Added plotting functions

- Added a
`NEWS.md`

file to track changes to the package.